diff --git a/docs/ag-ui-decision-logic-spec.md b/docs/ag-ui-decision-logic-spec.md
new file mode 100644
index 0000000..fa95753
--- /dev/null
+++ b/docs/ag-ui-decision-logic-spec.md
@@ -0,0 +1,225 @@
+AG-UI Decision Logic Spec (Tight Loop) v1
+========================================
+
+Status: Draft
+Owner: Clara Design Assistant
+Scope: Decision logic for switching from chat to rich UI (tables, process maps)
+
+This spec is standalone. Do not rely on other design artifacts.
+
+---
+
+1. Goals
+--------
+- Decide when to switch from chat to a rich UI based on user input and session state.
+- Minimize user typing for structured data capture.
+- Prevent tool thrash and ambiguous loops.
+- Provide deterministic, testable decision rules.
+- Use small models for routing and larger models for content.
+
+2. Non-Goals
+------------
+- UI styling, layout, or frontend implementation details.
+- Domain-specific ontology or schema design beyond table/map scaffolds.
+- File upload and ingestion pipelines.
+
+3. Key Patterns Adopted (from claude-code analysis)
+---------------------------------------------------
+- Self-refinement loop with completion promise and max iterations (Ralph loop).
+- Multi-phase gating with explicit approvals (feature-dev flow).
+- Confidence thresholding to reduce noise (code-review plugin).
+- Stateful checkpointing for resumption (advanced workflows).
+- User-defined guardrails derived from friction (hookify patterns).
+- Decision-point user contribution requests (learning output style).
+
+These patterns are explicitly incorporated in Sections 6-9.
+
+4. Architecture Overview
+------------------------
+Components:
+- Router (small model): decides tool vs chat and generates tool parameters.
+- Orchestrator (Sonnet): handles natural language responses, tool explanations, and follow-up.
+- Tool Menu: enumerated UI tools with strict schema.
+- UI State Store: tracks tool status, completion criteria, and checkpoints.
+
+Data flow:
+User Input -> Router -> (Tool Call or Chat) -> UI -> Tool Result -> Orchestrator
+
+5. Router Inputs and Outputs
+----------------------------
+Inputs:
+- user_message (string)
+- session_state (see Section 7)
+- last_tool (name or null)
+- last_tool_status (open, completed, canceled)
+- user_preferences (opt-out rules, preferred UI)
+
+Output JSON:
+{
+  "action": "tool" | "chat" | "clarify",
+  "tool_name": "request_data_table" | "request_process_map" | null,
+  "confidence": 0.0-1.0,
+  "params": { ...tool schema... } | null,
+  "rationale": "short string for logs"
+}
+
+Routing thresholds:
+- confidence >= 0.75: execute tool call immediately
+- 0.45 <= confidence < 0.75: ask one clarifying question, then re-route
+- confidence < 0.45: forward to Orchestrator for standard chat
+
+6. Tool Menu (v1)
+-----------------
+6.1 request_data_table
+Use when the user needs to provide a list of items, structured data, or bulk entries.
+Do NOT use for single items.
+
+Parameters:
+{
+  "title": "string",
+  "columns": [
+    { "name": "string", "type": "text|number|enum|date|url", "required": true|false }
+  ],
+  "min_rows": number,
+  "starter_rows": number,
+  "input_modes": ["paste", "inline", "import"],
+  "summary_prompt": "string"
+}
+
+6.2 request_process_map
+Use when the user describes a workflow, sequence of steps, timeline, or migration path.
+Capture "Step A -> Step B" relationships.
+
+Parameters:
+{
+  "title": "string",
+  "required_fields": ["step_name", "owner", "outcome"],
+  "edge_types": ["sequence", "approval", "parallel"],
+  "min_steps": number,
+  "seed_nodes": ["string"]
+}
+
+7. Session State (UI Checkpoints)
+---------------------------------
+Persisted fields:
+{
+  "last_tool": "string|null",
+  "last_tool_status": "open|completed|canceled",
+  "ui_checkpoint": {
+    "tool": "string",
+    "payload": { ...tool params... },
+    "opened_at": "timestamp",
+    "completion_criteria": { ... },
+    "iteration_count": number,
+    "max_iterations": number
+  },
+  "clarifying_question_pending": boolean,
+  "user_opt_out": {
+    "all_tools": boolean,
+    "tools": ["request_data_table", "request_process_map"],
+    "expires_at": "timestamp|null"
+  }
+}
+
+Checkpointing rule:
+- When a tool is opened, create a ui_checkpoint.
+- If the session resumes and a checkpoint is open, re-open that UI without re-asking.
+
+8. Decision Rules (Tight Loop)
+------------------------------
+Hard triggers (no clarifying question):
+- List size >= 3 (explicit or inferred: "we have 12 stakeholders").
+- User mentions bulk entry, spreadsheet, or "paste a list".
+- Workflow markers: "first/then/after/before/next", "approval process", "pipeline", "migration steps".
+
+Soft triggers (ask once, then tool):
+- Mentions "process" without steps -> ask: "Want to map the steps now?"
+- Mentions "stakeholders/risks/issues" without quantity -> ask for count.
+- Mentions multiple roles + any process language -> prefer process map.
+
+Anti-thrash rules:
+- If last_tool_status == "open", do not call another tool.
+- If user_opt_out.all_tools is true, do not call tools unless user re-enables.
+- If user cancels a tool twice in a row, switch to chat for the next turn.
+
+Chat-only cases:
+- Greetings, explanations, clarifications, or coaching.
+- Single-item edits or short statements.
+
+9. Completion Logic and Self-Refinement Loop
+--------------------------------------------
+Completion criteria:
+- Data table: min_rows satisfied AND required columns filled.
+- Process map: min_steps satisfied AND required_fields filled.
+
+Self-refinement loop (Ralph pattern):
+- After tool submission, run validation checks.
+- If criteria not met, ask a targeted fix question and re-open tool.
+- Max iterations: 2 (configurable per tool).
+- Never loop without explicit user feedback.
+
+10. Validation and Confidence Gating
+------------------------------------
+Run parallel validations (code-review pattern):
+- missing_required_fields
+- duplicate_entries
+- contradictory_sequences (for process map)
+- low_coverage (too few rows/steps vs stated scope)
+
+Each validation returns a confidence score. Only surface issues with
+confidence >= 0.80 to avoid noisy warnings.
+
+11. User Guardrails (Hookify pattern)
+-------------------------------------
+Allow users to set local rules:
+- "Never show tables for stakeholder lists"
+- "Always map approval processes"
+
+Rule format:
+{
+  "intent_pattern": "regex",
+  "action": "force_tool|suppress_tool",
+  "tool": "request_data_table|request_process_map"
+}
+
+Rules are applied before Router decisions.
+
+12. Examples
+------------
+Example A:
+User: "We have 20 stakeholders across finance, ops, and IT."
+Router: action=tool, tool=request_data_table, confidence=0.86
+Params: columns=[Name, Role, Influence], min_rows=20, input_modes=["paste","inline"]
+
+Example B:
+User: "First finance reviews the invoice, then IT signs off, then CFO approves."
+Router: action=tool, tool=request_process_map, confidence=0.91
+Params: required_fields=[step_name, owner, outcome], min_steps=3
+
+Example C:
+User: "We have some risks."
+Router: action=clarify, confidence=0.58
+Clarify: "How many risks are we capturing?"
+Then: tool if count >=3, else chat.
+
+13. Model Routing
+-----------------
+Router model options:
+- Claude 3.5 Haiku (default): fast, strong tool selection.
+- Llama 3.1 8B fine-tuned: edge router with 500+ tool-call examples.
+
+Fallback:
+- If Router output is invalid or confidence < 0.45, send to Sonnet.
+- Orchestrator always validates tool parameters before execution.
+
+14. Telemetry
+-------------
+Log events (privacy-safe):
+- router_decision (action, tool, confidence)
+- tool_opened, tool_submitted, tool_canceled
+- validation_warning_shown (type, confidence)
+- user_opt_out_changed
+
+---
+
+End of spec.
diff --git a/docs/design-agent-avatars.md b/docs/design-agent-avatars.md
new file mode 100644
index 0000000..d620d11
--- /dev/null
+++ b/docs/design-agent-avatars.md
@@ -0,0 +1,162 @@
+# Unique Avatars + Character Names for Interview Specialists
+
+## Goal
+Give each Interview Specialist a unique visual identity:
+1. **Character name** - Fun, memorable name like "Atlas", "Nova", "Echo"
+2. **Unique avatar** - Generated from the character name
+
+Display: **Atlas** (Systems Consolidation Interview Specialist) with a unique avatar.
+
+## Recommended Approach
+
+### Part 1: Character Name Generation
+Deterministic name picker based on agent ID hash. Names are memorable, professional-sounding, and diverse.
+
+### Part 2: Avatar Generation (Boring Avatars)
+Use **boring-avatars** npm package - lightweight React component (~3KB) that generates beautiful SVG avatars from the character name.
+
+## Implementation
+
+### Step 1: Install Package
+```bash
+cd src/frontend && npm install boring-avatars
+```
+
+### Step 2: Create Agent Name Generator
+```tsx
+// src/frontend/src/utils/agentNames.ts
+
+// Curated list of memorable character names
+const CHARACTER_NAMES = [
+  'Atlas', 'Nova', 'Echo', 'Luna', 'Sage',
+  'Phoenix', 'Orion', 'Iris', 'Zephyr', 'Clio',
+  'Jasper', 'Maya', 'Felix', 'Aurora', 'Quinn',
+  'River', 'Skylar', 'Ember', 'Rowan', 'Indigo',
+  'Aria', 'Kai', 'Terra', 'Finn', 'Celeste',
+  'Leo', 'Ivy', 'Aspen', 'Blake', 'Wren'
+];
+
+// Simple hash function for deterministic selection
+function hashString(str: string): number {
+  let hash = 0;
+  for (let i = 0; i < str.length; i++) {
+    hash = ((hash << 5) - hash) + str.charCodeAt(i);
+    hash |= 0;
+  }
+  return Math.abs(hash);
+}
+
+/**
+ * Generate a character name deterministically from agent ID
+ * Same ID always returns the same name
+ */
+export function getAgentCharacterName(agentId: string): string {
+  const index = hashString(agentId) % CHARACTER_NAMES.length;
+  return CHARACTER_NAMES[index];
+}
+```
+
+### Step 3: Create AgentAvatar Component
+Create a reusable avatar component with randomized styles:
+
+```tsx
+// src/frontend/src/components/AgentAvatar.tsx
+import Avatar from 'boring-avatars';
+
+type AvatarVariant = 'marble' | 'beam' | 'pixel' | 'sunset' | 'ring' | 'bauhaus';
+
+interface AgentAvatarProps {
+  name: string;
+  size?: number;
+  variant?: AvatarVariant; // Override auto-selection if needed
+}
+
+// Clara brand colors for avatar palette
+const CLARA_COLORS = ['#3B82F6', '#8B5CF6', '#06B6D4', '#10B981', '#F59E0B'];
+
+// Available styles for variety
+const VARIANTS: AvatarVariant[] = ['beam', 'marble', 'bauhaus', 'pixel'];
+
+// Simple hash function to pick variant deterministically from name
+function hashString(str: string): number {
+  let hash = 0;
+  for (let i = 0; i < str.length; i++) {
+    hash = ((hash << 5) - hash) + str.charCodeAt(i);
+    hash |= 0;
+  }
+  return Math.abs(hash);
+}
+
+export function AgentAvatar({ name, size = 40, variant }: AgentAvatarProps) {
+  // Auto-select variant based on name hash if not explicitly provided
+  const selectedVariant = variant ?? VARIANTS[hashString(name) % VARIANTS.length];
+
+  return (
+    <Avatar
+      name={name}
+      size={size}
+      variant={selectedVariant}
+      colors={CLARA_COLORS}
+    />
+  );
+}
+```
+
+This ensures:
+- Same agent name → same avatar (deterministic)
+- Different agent names → likely different styles (varied)
+
+### Step 4: Update ProjectDetailPage.tsx
+Replace the static user icon SVG with `<AgentAvatar>`:
+
+**Before (lines ~367-380):**
+```tsx
+<div className="w-12 h-12 bg-blue-100 rounded-full flex items-center justify-center">
+  <svg className="w-6 h-6 text-blue-600" ...>
+    {/* user silhouette path */}
+  </svg>
+</div>
+```
+
+**After:**
+```tsx
+<AgentAvatar name={getAgentCharacterName(agent.id)} size={48} />
+```
+
+Also update the display to show the character name:
+```tsx
+<h3 className="font-semibold text-gray-900">
+  {getAgentCharacterName(agent.id)} <span className="text-gray-500 font-normal">({agent.name})</span>
+</h3>
+```
+
+### Step 5: Update AgentConfiguredCard.tsx (Optional)
+Same pattern for the Design Assistant conversation card if desired.
+
+## Files to Modify
+
+| File | Change |
+|------|--------|
+| `package.json` | Add `boring-avatars` dependency |
+| `src/utils/agentNames.ts` | New utility (create) |
+| `src/components/AgentAvatar.tsx` | New component (create) |
+| `src/pages/ProjectDetailPage.tsx` | Replace static icon with AgentAvatar |
+| `src/components/design-assistant/AgentConfiguredCard.tsx` | (Optional) Replace icon |
+
+## Visual Examples
+
+Boring Avatars styles:
+- **beam**: Friendly face-like circles (recommended)
+- **marble**: Organic gradient blobs
+- **bauhaus**: Abstract geometric
+- **pixel**: 8-bit style squares
+- **sunset**: Layered gradient waves
+- **ring**: Concentric circles
+
+## Alternative Considered
+
+**DiceBear** - More style options but requires either:
+- External API calls (latency, dependency)
+- Larger package size for local generation
+
+Boring Avatars is simpler and sufficient for this use case.
diff --git a/docs/design-ux-ideas.md b/docs/design-ux-ideas.md
new file mode 100644
index 0000000..7ee9b5b
--- /dev/null
+++ b/docs/design-ux-ideas.md
@@ -0,0 +1,149 @@
+# Clara UX Enhancement Ideas
+
+A collection of fun, polished UX features to make Clara feel more delightful and professional.
+
+---
+
+## Quick Wins (1-2 hours each)
+
+### 1. Animated Thinking Indicator
+When an agent is processing, show a unique animation based on their avatar style (marble swirls, beam face "thinking", bauhaus shapes rotating). More personality than a generic spinner.
+
+### 2. Milestone Celebrations
+Brief confetti burst or checkmark animation when:
+- Blueprint is finalized
+- First interview completes
+- All stakeholders interviewed
+- Discovery phase wraps up
+
+**Implementation**: Use `canvas-confetti` npm package (~3KB)
+
+### 3. Draft Mode Indicator
+When typing, show a gentle pulsing border or "draft" badge. Auto-saves drafts locally. Shows "Restored draft from 2m ago" on reconnect.
+
+**Implementation**: LocalStorage + debounced save + subtle CSS animation
+
+### 4. Dark Mode with Accent Colors
+Full dark theme plus let users pick an accent color (or auto-derive from their avatar). Persisted preference.
+
+**Implementation**: CSS variables + Tailwind dark mode + localStorage
+
+---
+
+## Medium Effort (half day each)
+
+### 5. Entity Discovery Particles
+When entities are detected during interviews, subtle particle effects or a brief "glow" animation draws attention to newly discovered systems/people/processes. Makes discovery feel tangible.
+
+**Implementation**: CSS keyframe animations triggered by entity detection events
+
+### 6. Interview Progress Ring
+A circular progress visualization around the agent avatar that fills as topics are covered. Segments could be color-coded by topic area (systems=blue, people=green, processes=purple).
+
+**Implementation**: SVG circle with `stroke-dasharray` animation
+
+### 7. Agent "Mood" Expressions
+Avatar subtly changes based on context - attentive when listening, curious when asking follow-ups, satisfied when topic is covered. Uses CSS filters or variant switches on the boring-avatar.
+
+**Implementation**: State-driven CSS filters (hue-rotate, brightness) or swap avatar variants
+
+### 8. Sound Design
+Subtle audio cues (toggleable):
+- Soft chime on message received
+- Gentle "pop" when selecting options
+- Satisfying "complete" sound on phase transitions
+
+Think Slack/Discord polish.
+
+**Implementation**: Web Audio API or Howler.js with user preference toggle
+
+### 9. Keyboard Shortcuts Bar
+Power user overlay (press `?`) showing shortcuts:
+- `⌘K` quick search
+- `⌘Enter` send
+- `Tab` cycle options
+- `1-5` select numbered choices
+
+Fades in/out smoothly.
+
+**Implementation**: Global keydown listener + modal component
+
+### 10. Smart Suggestions Tray
+Context-aware suggestion chips above input:
+- "Tell me more about [System X]"
+- "Who maintains this?"
+- "What happens when it fails?"
+
+Based on recent conversation entities.
+
+**Implementation**: Backend generates suggestions from conversation context
+
+### 11. Onboarding Tooltips
+First-time user gets gentle spotlight tooltips highlighting key features. Dismissable, remembers progress, can replay from settings.
+
+**Implementation**: `react-joyride` or custom spotlight component + localStorage
+
+---
+
+## Larger Features (1+ day each)
+
+### 12. Knowledge Graph Preview
+A mini interactive node graph that grows as interviews progress - users can see entities and connections forming in real-time. Clicking a node shows the quote/evidence that surfaced it.
+
+**Implementation**: `react-force-graph` or `d3-force` + entity state from backend
+
+### 13. Interview Transcript Timeline
+Scrollable side panel showing interview as a visual timeline with markers for key moments - entity discoveries, topic transitions, insights flagged. Click to jump.
+
+**Implementation**: Custom timeline component with scroll-to anchors
+
+### 14. Entity Cards with Hover Details
+Detected entities become hoverable chips that show a mini card - where it was mentioned, confidence level, connections to other entities.
+
+**Implementation**: Popover component + entity metadata from backend
+
+### 15. Collaborative Cursors
+If multiple managers view the same project, show their presence with colored cursors/avatars (like Figma). "Sarah is viewing this blueprint"
+
+**Implementation**: WebSocket presence channel + cursor position broadcast
+
+---
+
+## Priority Recommendations
+
+### High Impact, Low Effort
+1. **Animated Thinking Indicator** - Immediate personality boost
+2. **Milestone Celebrations** - Satisfying feedback loops
+3. **Dark Mode** - Expected table stakes feature
+4. **Draft Mode** - Prevents lost work frustration
+
+### High Impact, Medium Effort
+5. **Sound Design** - Professional polish (with toggle!)
+6. **Smart Suggestions** - Increases engagement
+7. **Progress Ring** - Visual progress = motivation
+
+### Differentiating Features
+8. **Knowledge Graph Preview** - Unique to Clara's value prop
+9. **Entity Cards** - Makes discovery tangible
+10. **Collaborative Cursors** - Enterprise-ready feel
+
+---
+
+## Implementation Notes
+
+### Packages to Consider
+| Feature | Package | Size |
+|---------|---------|------|
+| Avatars | `boring-avatars` | ~3KB |
+| Confetti | `canvas-confetti` | ~3KB |
+| Sound | `howler` | ~10KB |
+| Graph | `react-force-graph` | ~50KB |
+| Tooltips | `react-joyride` | ~20KB |
+| Shortcuts | `react-hotkeys-hook` | ~3KB |
+
+### Design Principles
+- **Subtle by default** - Animations should enhance, not distract
+- **User control** - Sounds/animations should be toggleable
+- **Performance** - Lazy load heavier features (graph, etc.)
+- **Accessibility** - Respect `prefers-reduced-motion`
+- **Consistency** - Use Clara brand colors throughout
diff --git a/src/backend/clara/agents/design_assistant.py b/src/backend/clara/agents/design_assistant.py
index 8a553f6..7f6d216 100644
--- a/src/backend/clara/agents/design_assistant.py
+++ b/src/backend/clara/agents/design_assistant.py
@@ -14,11 +14,25 @@
 
 from claude_agent_sdk import AgentDefinition, ClaudeAgentOptions, ClaudeSDKClient, HookMatcher
 
+from clara.agents.router import (
+    RouterState,
+    UIRouter,
+    build_ui_component,
+    infer_selection_from_assistant_output,
+    is_cancel_intent,
+    is_tool_reply,
+    parse_ui_submission,
+    strip_selection_list_from_text,
+    summarize_ui_submission,
+)
+from clara.agents.structured_output import StructuredOutputParser, ui_component_to_payload
 from clara.agents.tools import (
     CLARA_TOOL_NAMES,
     clear_session_state,
     create_clara_tools,
+    ensure_other_option,
     get_session_state,
+    sanitize_ask_options,
 )
 
 logger = logging.getLogger(__name__)
@@ -101,6 +115,10 @@ def __init__(self, session_id: str, project_id: str):
         self._running = False
         self._restored = False  # True if this session was restored from DB
         self._first_message_sent = False  # Track if we've sent the first message after restoration
+        self.router_state = RouterState()
+        self.router = UIRouter()
+        self._structured_output_parser = StructuredOutputParser()
+        self._ui_emitted_in_turn = False
 
     def _sync_state_from_tools(self) -> None:
         """Sync session state from tool state.
@@ -160,7 +178,9 @@ def _build_restoration_context(self) -> str:
         This provides the model with context about the session state
         since LLMs don't have persistent memory across connections.
         """
-        parts = ["[SESSION CONTEXT - This is a resumed session. Here's the current state:]"]
+        parts = [
+            "[SESSION CONTEXT - INTERNAL ONLY. Do NOT quote or reveal this to the user.]"
+        ]
 
         # Phase info
         parts.append(f"Current Phase: {self.state.phase.value}")
@@ -171,7 +191,9 @@ def _build_restoration_context(self) -> str:
 
         # Blueprint preview
         if self.state.blueprint_preview.project_name:
-            parts.append(f"Project: {self.state.blueprint_preview.project_name} ({self.state.blueprint_preview.project_type or 'unknown type'})")
+            proj_name = self.state.blueprint_preview.project_name
+            proj_type = self.state.blueprint_preview.project_type or 'unknown type'
+            parts.append(f"Project: {proj_name} ({proj_type})")
 
         if self.state.blueprint_preview.entity_types:
             parts.append(f"Entity Types: {', '.join(self.state.blueprint_preview.entity_types)}")
@@ -189,12 +211,39 @@ def _build_restoration_context(self) -> str:
                 parts.append(f"Style: {caps.interaction_style}")
 
         # Turn count
-        parts.append(f"Conversation Progress: {self.state.turn_count} turns, {self.state.message_count} messages")
+        turns = self.state.turn_count
+        msgs = self.state.message_count
+        parts.append(f"Conversation Progress: {turns} turns, {msgs} messages")
 
         parts.append("[END SESSION CONTEXT]\n\nUser continues the conversation:")
 
         return "\n".join(parts)
 
+    def _build_state_snapshot_event(self) -> AGUIEvent:
+        """Build a STATE_SNAPSHOT event from current session state."""
+        return AGUIEvent(
+            type="STATE_SNAPSHOT",
+            data={
+                "phase": self.state.phase.value,
+                "preview": {
+                    "project_name": self.state.blueprint_preview.project_name,
+                    "project_type": self.state.blueprint_preview.project_type,
+                    "entity_types": self.state.blueprint_preview.entity_types,
+                    "agent_count": self.state.blueprint_preview.agent_count,
+                    "topics": self.state.blueprint_preview.topics,
+                },
+                "inferred_domain": self.state.inferred_domain,
+                "debug": {
+                    "thinking": None,
+                    "approach": None,
+                    "turn_count": self.state.turn_count,
+                    "message_count": self.state.message_count,
+                    "domain_confidence": self.state.domain_confidence,
+                    "discussed_topics": self.state.discussed_topics,
+                }
+            }
+        )
+
     def _create_subagents(self) -> dict[str, AgentDefinition]:
         """Define phase-based subagents.
 
@@ -207,7 +256,8 @@ def _create_subagents(self) -> dict[str, AgentDefinition]:
         # Phase 2 fetches hydrated prompt to get {{goal}} from Phase 1
         phase2_prompt = """You are configuring a specialist interview agent.
 
-First, call mcp__clara__get_prompt with phase="agent_configuration" to get your full instructions with the project goal.
+First, call mcp__clara__get_prompt with phase="agent_configuration" to get your
+full instructions with the project goal.
 
 Then follow those instructions to:
 1. Analyze the goal
@@ -223,8 +273,10 @@ def _create_subagents(self) -> dict[str, AgentDefinition]:
 CRITICAL INSTRUCTION: You MUST use mcp__clara__ask for EVERY decision point.
 Do NOT proceed to build anything until you have collected user input via ask tool.
 
-Step 1: First, call mcp__clara__get_prompt with phase="blueprint_design" to get your full context.
-Step 2: Then call mcp__clara__ask to ask the user what knowledge they want to extract from interviews.
+Step 1: First, call mcp__clara__get_prompt with phase="blueprint_design" to get
+your full context.
+Step 2: Then call mcp__clara__ask to ask the user what knowledge they want to
+extract from interviews.
 Step 3: Wait for user response. Do NOT call entity/agent/project tools until user confirms via ask.
 
 Your ONLY job in this turn is:
@@ -238,13 +290,23 @@ def _create_subagents(self) -> dict[str, AgentDefinition]:
         return {
             "phase1-goal-discovery": AgentDefinition(
                 description=(
-                    "Handles Phase 1: Goal Understanding. "
-                    "Use this agent to discover the user's project through natural conversation. "
-                    "It will explore the 5 key dimensions and use mcp__clara__ask for structured choices. "
-                    "After completing, call mcp__clara__hydrate_phase2 with the goal summary."
+                    "Handles Phase 1: Goal Confirmation. "
+                    "Use this agent to discover the user's project through "
+                    "natural conversation. It will explore the core discovery areas "
+                    "and use mcp__clara__ask for structured choices. "
+                    "After completing, call mcp__clara__hydrate_phase2."
                 ),
-                tools=["mcp__clara__ask", "mcp__clara__project", "mcp__clara__save_goal_summary",
-                       "mcp__clara__hydrate_phase2", "mcp__clara__phase", "mcp__clara__get_prompt"],
+                tools=[
+                    "mcp__clara__ask",
+                    "mcp__clara__request_selection_list",
+                    "mcp__clara__request_data_table",
+                    "mcp__clara__request_process_map",
+                    "mcp__clara__project",
+                    "mcp__clara__save_goal_summary",
+                    "mcp__clara__hydrate_phase2",
+                    "mcp__clara__phase",
+                    "mcp__clara__get_prompt",
+                ],
                 prompt=phase1_prompt,
                 model="sonnet"
             ),
@@ -254,8 +316,15 @@ def _create_subagents(self) -> dict[str, AgentDefinition]:
                     "First call mcp__clara__get_prompt to get hydrated instructions with the goal. "
                     "Then configure the specialist agent and call mcp__clara__hydrate_phase3."
                 ),
-                tools=["mcp__clara__agent_summary", "mcp__clara__phase", "mcp__clara__get_prompt",
-                       "mcp__clara__hydrate_phase3"],
+                tools=[
+                    "mcp__clara__agent_summary",
+                    "mcp__clara__phase",
+                    "mcp__clara__get_prompt",
+                    "mcp__clara__hydrate_phase3",
+                    "mcp__clara__request_selection_list",
+                    "mcp__clara__request_data_table",
+                    "mcp__clara__request_process_map",
+                ],
                 prompt=phase2_prompt,
                 model="sonnet"
             ),
@@ -263,15 +332,26 @@ def _create_subagents(self) -> dict[str, AgentDefinition]:
                 description=(
                     "Handles Phase 3: Blueprint Design INTERACTIVELY. "
                     "First call mcp__clara__get_prompt to get hydrated instructions. "
-                    "This agent MUST use mcp__clara__ask to collect user input before building. "
-                    "It should NOT build entities/agents until user confirms via ask tool responses. "
-                    "Use mcp__clara__prompt_editor to show generated prompts for user editing. "
-                    "Use mcp__clara__get_agent_context to access uploaded context files."
+                    "This agent MUST use mcp__clara__ask to collect user input "
+                    "before building. It should NOT build entities/agents until "
+                    "user confirms via ask tool responses. "
+                    "Use mcp__clara__prompt_editor for editing prompts. "
+                    "Use mcp__clara__get_agent_context for context files."
                 ),
-                tools=["mcp__clara__project", "mcp__clara__entity", "mcp__clara__agent",
-                       "mcp__clara__ask", "mcp__clara__preview", "mcp__clara__phase",
-                       "mcp__clara__get_prompt", "mcp__clara__prompt_editor",
-                       "mcp__clara__get_agent_context"],
+                tools=[
+                    "mcp__clara__project",
+                    "mcp__clara__entity",
+                    "mcp__clara__agent",
+                    "mcp__clara__ask",
+                    "mcp__clara__request_selection_list",
+                    "mcp__clara__request_data_table",
+                    "mcp__clara__request_process_map",
+                    "mcp__clara__preview",
+                    "mcp__clara__phase",
+                    "mcp__clara__get_prompt",
+                    "mcp__clara__prompt_editor",
+                    "mcp__clara__get_agent_context",
+                ],
                 prompt=phase3_prompt,
                 model="sonnet"
             ),
@@ -298,12 +378,23 @@ async def pre_tool_hook(
                 data={"tool": tool_name, "input": tool_input}
             ))
 
+            if tool_name in {
+                "mcp__clara__request_data_table",
+                "mcp__clara__request_process_map",
+                "mcp__clara__request_selection_list",
+            }:
+                normalized_tool = tool_name.replace("mcp__clara__", "")
+                self.router_state.pending_tool = normalized_tool
+                self.router_state.last_tool = normalized_tool
+                self.router_state.last_tool_status = "open"
+
             # Special handling for ask tool - emit CUSTOM event with UI component
             if tool_name == "mcp__clara__ask":
+                options = sanitize_ask_options(tool_input.get("options", []))
                 ui_component = {
                     "type": "user_input_required",
                     "question": tool_input.get("question", ""),
-                    "options": tool_input.get("options", []),
+                    "options": options,
                     "multi_select": tool_input.get("multi_select", False),
                 }
                 # Emit as CUSTOM AG-UI event for reliable rendering
@@ -311,8 +402,24 @@ async def pre_tool_hook(
                     type="CUSTOM",
                     data={"name": "clara:ask", "value": ui_component}
                 ))
+                self._ui_emitted_in_turn = True
                 logger.info(f"[{self.session_id}] Emitted CUSTOM event clara:ask")
 
+            if tool_name == "mcp__clara__request_selection_list":
+                options = ensure_other_option(sanitize_ask_options(tool_input.get("options", [])))
+                ui_component = {
+                    "type": "user_input_required",
+                    "question": tool_input.get("question", ""),
+                    "options": options,
+                    "multi_select": tool_input.get("multi_select", False),
+                }
+                await response_queue.put(AGUIEvent(
+                    type="CUSTOM",
+                    data={"name": "clara:ask", "value": ui_component}
+                ))
+                self._ui_emitted_in_turn = True
+                logger.info(f"[{self.session_id}] Emitted CUSTOM event clara:ask (selection list)")
+
             # Special handling for prompt_editor tool - emit CUSTOM event for editable prompt
             if tool_name == "mcp__clara__prompt_editor":
                 ui_component = {
@@ -326,8 +433,42 @@ async def pre_tool_hook(
                     type="CUSTOM",
                     data={"name": "clara:prompt_editor", "value": ui_component}
                 ))
+                self._ui_emitted_in_turn = True
                 logger.info(f"[{self.session_id}] Emitted CUSTOM event clara:prompt_editor")
 
+            if tool_name == "mcp__clara__request_data_table":
+                ui_component = {
+                    "type": "data_table",
+                    "title": tool_input.get("title", "Data Table"),
+                    "columns": tool_input.get("columns", []),
+                    "min_rows": tool_input.get("min_rows", 3),
+                    "starter_rows": tool_input.get("starter_rows", 3),
+                    "input_modes": tool_input.get("input_modes", ["paste", "inline"]),
+                    "summary_prompt": tool_input.get("summary_prompt", ""),
+                }
+                await response_queue.put(AGUIEvent(
+                    type="CUSTOM",
+                    data={"name": "clara:data_table", "value": ui_component}
+                ))
+                self._ui_emitted_in_turn = True
+                logger.info(f"[{self.session_id}] Emitted CUSTOM event clara:data_table")
+
+            if tool_name == "mcp__clara__request_process_map":
+                ui_component = {
+                    "type": "process_map",
+                    "title": tool_input.get("title", "Process Map"),
+                    "required_fields": tool_input.get("required_fields", []),
+                    "edge_types": tool_input.get("edge_types", []),
+                    "min_steps": tool_input.get("min_steps", 3),
+                    "seed_nodes": tool_input.get("seed_nodes", []),
+                }
+                await response_queue.put(AGUIEvent(
+                    type="CUSTOM",
+                    data={"name": "clara:process_map", "value": ui_component}
+                ))
+                self._ui_emitted_in_turn = True
+                logger.info(f"[{self.session_id}] Emitted CUSTOM event clara:process_map")
+
             # Note: agent_summary tool stores config in state but no longer emits UI card
 
             return {}  # No modifications to tool behavior
@@ -406,12 +547,39 @@ async def send_message(self, message: str) -> AsyncIterator[AGUIEvent]:
         if not self._running or not self.client:
             raise RuntimeError("Session not started")
 
+        self._ui_emitted_in_turn = False
         self.state.message_count += 1
         self.state.turn_count += 1
 
         # Sync state from tools before emitting snapshot
         self._sync_state_from_tools()
 
+        submission = parse_ui_submission(message)
+        if submission:
+            self.router_state.last_tool = submission.kind
+            self.router_state.last_tool_status = "completed"
+            self.router_state.pending_tool = None
+            self.router_state.pending_payload = None
+            message = summarize_ui_submission(submission)
+
+        if (
+            not submission
+            and self.router_state.pending_tool == "request_selection_list"
+            and is_tool_reply(message)
+        ):
+            self.router_state.last_tool_status = "completed"
+            self.router_state.pending_tool = None
+            self.router_state.pending_payload = None
+
+        if (
+            not submission
+            and self.router_state.pending_tool
+            and is_cancel_intent(message)
+        ):
+            self.router_state.last_tool_status = "canceled"
+            self.router_state.pending_tool = None
+            self.router_state.pending_payload = None
+
         # For restored sessions, prepend context on first message
         actual_message = message
         if self._restored and not self._first_message_sent:
@@ -421,28 +589,85 @@ async def send_message(self, message: str) -> AsyncIterator[AGUIEvent]:
             logger.info(f"[{self.session_id}] Prepending restoration context to first message")
 
         # Emit state snapshot at start of turn
-        yield AGUIEvent(
-            type="STATE_SNAPSHOT",
-            data={
-                "phase": self.state.phase.value,
-                "preview": {
-                    "project_name": self.state.blueprint_preview.project_name,
-                    "project_type": self.state.blueprint_preview.project_type,
-                    "entity_types": self.state.blueprint_preview.entity_types,
-                    "agent_count": self.state.blueprint_preview.agent_count,
-                    "topics": self.state.blueprint_preview.topics,
-                },
-                "inferred_domain": self.state.inferred_domain,
-                "debug": {
-                    "thinking": None,
-                    "approach": None,
-                    "turn_count": self.state.turn_count,
-                    "message_count": self.state.message_count,
-                    "domain_confidence": self.state.domain_confidence,
-                    "discussed_topics": self.state.discussed_topics,
-                }
-            }
-        )
+        yield self._build_state_snapshot_event()
+
+        if not submission:
+            decision = None
+            if self.state.phase != DesignPhase.GOAL_UNDERSTANDING:
+                decision = await self.router.decide(
+                    message=message,
+                    state=self.router_state,
+                    phase=self.state.phase.value,
+                    flow="design_assistant",
+                    allow_selection=False,
+                )
+
+            if decision and decision.action in {"tool", "clarify"}:
+                preamble = ""
+                if decision.action == "tool":
+                    ui_component = build_ui_component(decision)
+                    if not ui_component:
+                        decision = None
+                    else:
+                        tool_name_map = {
+                            "request_data_table": "mcp__clara__request_data_table",
+                            "request_process_map": "mcp__clara__request_process_map",
+                            "request_selection_list": "mcp__clara__request_selection_list",
+                        }
+                        tool_name = tool_name_map.get(
+                            decision.tool_name, "mcp__clara__request_data_table"
+                        )
+                        if decision.tool_name == "request_data_table":
+                            preamble = "Let's capture that in a table."
+                        elif decision.tool_name == "request_process_map":
+                            preamble = "Let's map the steps so we capture the workflow accurately."
+                        elif decision.tool_name == "request_selection_list":
+                            preamble = "Pick the options that apply."
+                        tool_state = get_session_state(self.session_id)
+                        tool_state["pending_ui_component"] = ui_component
+                        self.router_state.pending_tool = decision.tool_name
+                        self.router_state.pending_payload = decision.params
+                        self.router_state.last_tool = decision.tool_name
+                        self.router_state.last_tool_status = "open"
+
+                        yield AGUIEvent(
+                            type="TOOL_CALL_START",
+                            data={"tool": tool_name, "input": decision.params or {}}
+                        )
+                        yield AGUIEvent(
+                            type="TEXT_MESSAGE_CONTENT",
+                            data={"delta": preamble}
+                        )
+                        yield AGUIEvent(
+                            type="CUSTOM",
+                            data={
+                                "name": (
+                                    "clara:data_table"
+                                    if decision.tool_name == "request_data_table"
+                                    else "clara:process_map"
+                                    if decision.tool_name == "request_process_map"
+                                    else "clara:ask"
+                                ),
+                                "value": ui_component,
+                            }
+                        )
+                        yield AGUIEvent(
+                            type="TOOL_CALL_END",
+                            data={"tool": tool_name}
+                        )
+                        yield AGUIEvent(type="TEXT_MESSAGE_END", data={})
+                        yield self._build_state_snapshot_event()
+                        return
+
+                if decision and decision.action == "clarify":
+                    self.router_state.last_clarify = decision.clarifying_question
+                    yield AGUIEvent(
+                        type="TEXT_MESSAGE_CONTENT",
+                        data={"delta": decision.clarifying_question or "Can you clarify?"}
+                    )
+                    yield AGUIEvent(type="TEXT_MESSAGE_END", data={})
+                    yield self._build_state_snapshot_event()
+                    return
 
         # Send message to agent (uses actual_message which may include restoration context)
         await self.client.query(prompt=actual_message)
@@ -458,6 +683,7 @@ async def drain_queue():
                     break
 
         # Stream response
+        use_structured_output = self._structured_output_parser.is_available()
         current_text = ""
         async for msg in self.client.receive_response():
             # First, drain any events queued by hooks
@@ -484,7 +710,7 @@ async def drain_queue():
                                     # New message - emit the full text
                                     delta = new_text
                                 current_text = new_text
-                                if delta:  # Only emit if there's actual content
+                                if delta and not use_structured_output:
                                     yield AGUIEvent(
                                         type="TEXT_MESSAGE_CONTENT",
                                         data={"delta": delta}
@@ -505,7 +731,9 @@ async def drain_queue():
                 if hasattr(msg, 'content'):
                     # Content can be a list of blocks or a string
                     content = msg.content
-                    logger.info(f"[{self.session_id}] Tool content type: {type(content)}, value: {content}")
+                    logger.info(
+                        f"[{self.session_id}] Tool content: {type(content)}"
+                    )
                     if isinstance(content, list):
                         for block in content:
                             if hasattr(block, 'text'):
@@ -517,7 +745,7 @@ async def drain_queue():
                 # Also check for 'result' attribute
                 elif hasattr(msg, 'result'):
                     result = msg.result
-                    logger.info(f"[{self.session_id}] Tool result type: {type(result)}, value: {result}")
+                    logger.info(f"[{self.session_id}] Tool result: {type(result)}")
                     if isinstance(result, str):
                         tool_text = result
                     elif isinstance(result, dict) and 'content' in result:
@@ -525,7 +753,8 @@ async def drain_queue():
                             if isinstance(block, dict) and 'text' in block:
                                 tool_text += block['text']
 
-                logger.info(f"[{self.session_id}] Extracted tool_text: {tool_text[:200] if tool_text else 'empty'}")
+                tool_preview = tool_text[:100] if tool_text else 'empty'
+                logger.info(f"[{self.session_id}] Extracted tool_text: {tool_preview}")
 
                 # Note: UI components are now handled via CUSTOM events in pre_tool_hook
                 # No need to parse [UI_COMPONENT] markers from tool results
@@ -539,32 +768,115 @@ async def drain_queue():
         async for event in drain_queue():
             yield event
 
+        if use_structured_output and current_text:
+            structured_response = None
+            ui_payload: dict[str, Any] | None = None
+            selection_decision = None
+            if not self._ui_emitted_in_turn and not self.router_state.pending_tool:
+                structured_response = await self._structured_output_parser.parse(
+                    message=current_text,
+                    phase=self.state.phase.value,
+                    flow="design_assistant",
+                )
+                if structured_response:
+                    ui_payload = ui_component_to_payload(structured_response.ui_component)
+
+            if (
+                not ui_payload
+                and not self._ui_emitted_in_turn
+                and not self.router_state.pending_tool
+            ):
+                selection_decision = infer_selection_from_assistant_output(current_text)
+                if selection_decision:
+                    ui_payload = build_ui_component(selection_decision)
+
+            display_text = (
+                structured_response.display_text
+                if structured_response
+                else current_text
+            )
+            if ui_payload and display_text and ui_payload.get("type") == "user_input_required":
+                cleaned_text = strip_selection_list_from_text(display_text)
+                if cleaned_text:
+                    display_text = cleaned_text
+                elif selection_decision and selection_decision.params:
+                    display_text = (
+                        selection_decision.params.get("question") or display_text
+                    )
+
+            if display_text:
+                yield AGUIEvent(
+                    type="TEXT_MESSAGE_CONTENT",
+                    data={"delta": display_text}
+                )
+
+            if ui_payload:
+                tool_type = ui_payload.get("type")
+                tool_name = "request_selection_list"
+                custom_name = "clara:ask"
+                if tool_type == "data_table":
+                    tool_name = "request_data_table"
+                    custom_name = "clara:data_table"
+                elif tool_type == "process_map":
+                    tool_name = "request_process_map"
+                    custom_name = "clara:process_map"
+
+                tool_state = get_session_state(self.session_id)
+                tool_state["pending_ui_component"] = ui_payload
+                self.router_state.pending_tool = tool_name
+                self.router_state.pending_payload = ui_payload
+                self.router_state.last_tool = tool_name
+                self.router_state.last_tool_status = "open"
+                self._ui_emitted_in_turn = True
+
+                yield AGUIEvent(
+                    type="TOOL_CALL_START",
+                    data={
+                        "tool": f"mcp__clara__{tool_name}",
+                        "input": ui_payload,
+                    }
+                )
+                yield AGUIEvent(
+                    type="CUSTOM",
+                    data={"name": custom_name, "value": ui_payload}
+                )
+                yield AGUIEvent(
+                    type="TOOL_CALL_END",
+                    data={"tool": f"mcp__clara__{tool_name}"}
+                )
+
+        if not use_structured_output and current_text and not self.router_state.pending_tool:
+            selection_decision = infer_selection_from_assistant_output(current_text)
+            if selection_decision:
+                ui_component = build_ui_component(selection_decision)
+                if ui_component:
+                    tool_state = get_session_state(self.session_id)
+                    tool_state["pending_ui_component"] = ui_component
+                    self.router_state.pending_tool = selection_decision.tool_name
+                    self.router_state.pending_payload = selection_decision.params
+                    self.router_state.last_tool = selection_decision.tool_name
+                    self.router_state.last_tool_status = "open"
+                    yield AGUIEvent(
+                        type="TOOL_CALL_START",
+                        data={
+                            "tool": "mcp__clara__request_selection_list",
+                            "input": selection_decision.params or {},
+                        }
+                    )
+                    yield AGUIEvent(
+                        type="CUSTOM",
+                        data={"name": "clara:ask", "value": ui_component}
+                    )
+                    yield AGUIEvent(
+                        type="TOOL_CALL_END",
+                        data={"tool": "mcp__clara__request_selection_list"}
+                    )
+
         # Sync state from tools after all tool calls complete
         self._sync_state_from_tools()
 
         # Emit final state snapshot with any changes from this turn
-        yield AGUIEvent(
-            type="STATE_SNAPSHOT",
-            data={
-                "phase": self.state.phase.value,
-                "preview": {
-                    "project_name": self.state.blueprint_preview.project_name,
-                    "project_type": self.state.blueprint_preview.project_type,
-                    "entity_types": self.state.blueprint_preview.entity_types,
-                    "agent_count": self.state.blueprint_preview.agent_count,
-                    "topics": self.state.blueprint_preview.topics,
-                },
-                "inferred_domain": self.state.inferred_domain,
-                "debug": {
-                    "thinking": None,
-                    "approach": None,
-                    "turn_count": self.state.turn_count,
-                    "message_count": self.state.message_count,
-                    "domain_confidence": self.state.domain_confidence,
-                    "discussed_topics": self.state.discussed_topics,
-                }
-            }
-        )
+        yield self._build_state_snapshot_event()
 
         # Emit end of message
         yield AGUIEvent(
diff --git a/src/backend/clara/agents/prompts/interview_orchestrator.txt b/src/backend/clara/agents/prompts/interview_orchestrator.txt
index d885e94..de73d86 100644
--- a/src/backend/clara/agents/prompts/interview_orchestrator.txt
+++ b/src/backend/clara/agents/prompts/interview_orchestrator.txt
@@ -50,30 +50,27 @@ You are a **single agent** that transitions through three phases, each with a sp
 
 ## Phase Details
 
-### Phase 1: Goal Understanding
+### Phase 1: Goal Confirmation
 
-**Purpose:** Understand the user's project through natural conversation.
+**Purpose:** Capture a concise project goal and confirm it.
 
-**Mode:** Interactive - multiple turns of dialogue with the user.
+**Mode:** Two-turn interview.
 
-**Key Dimensions to Discover:**
-1. **Project Context** - Type of initiative (M&A, migration, optimization, etc.) and primary goal
-2. **Subject Matter Experts** - Who will be interviewed (roles, departments, expertise level)
-3. **Key Information Needs** - What insights or information to gather
-4. **Output Requirements** - How results will be used
-5. **Constraints & Preferences** - Time limits, tone, sensitive topics
+**Critical Output Rule:** Do NOT reveal internal checklists or discovery dimensions.
 
-**Transition Criteria:**
-Transition when you can confidently describe:
-- The project type and its core objective
-- Who the interviewees are (at least roles/departments)
-- The key information being sought
+**Flow:**
+1. If the user already stated a goal, paraphrase it in one sentence and ask for confirmation.
+2. If the goal is missing, ask for a one-sentence goal.
+3. Use `mcp__clara__ask` with Yes/No to confirm.
+4. If confirmed, save and transition. If not, stay in Phase 1 and ask for the corrected goal.
 
-You do NOT need perfect clarity on all 5 dimensions. Reasonable defaults can be inferred in Phase 2.
+**Transition Criteria:**
+Transition immediately after the user confirms the goal.
 
 **Tools Available:**
-- `mcp__clara__ask` - Present structured options to user
-- `mcp__clara__project` - Record project context
+- `mcp__clara__ask` - Yes/No confirmation
+- `mcp__clara__save_goal_summary` - Save confirmed goal (project_type="unspecified")
+- `mcp__clara__hydrate_phase2` - Hydrate Phase 2 with the goal
 - `mcp__clara__phase` - Transition to next phase
 
 ---
@@ -165,12 +162,11 @@ Transition to "complete" when the user approves the blueprint.
 
 ### After Phase 1 → Phase 2
 
-1. Summarize the goal in a structured format
-2. Call `mcp__clara__save_goal_summary` to persist
-3. Call `mcp__clara__hydrate_phase2` with:
+1. Save the confirmed goal summary (project_type="unspecified")
+2. Call `mcp__clara__hydrate_phase2` with:
    ```json
    {
-     "goal": "<structured goal summary>"
+     "goal": "<confirmed goal text>"
    }
    ```
 4. Call `mcp__clara__phase({"phase": "agent_configuration"})`
@@ -217,28 +213,38 @@ When a new session starts:
 
 1. Greet the user warmly
 2. Explain that you'll help them create a specialized interview agent
-3. Outline the 5 dimensions you need to understand
-4. Ask an opening question about their project type and goal
+3. Ask for their goal in one sentence (if not already provided)
+4. Do NOT list internal discovery dimensions
 
 Example opening:
 
 > Hello! I'm Clara's Interview Design Agent.
 >
-> I'm here to help you create a specialized interview agent tailored to your specific project needs.
+> I'm here to help you create a specialized interview agent tailored to your project needs.
 >
-> To design the most effective interview agent for you, I need to learn about:
-> 1. **Project Context** - What type of initiative and its primary goal
-> 2. **Subject Matter Experts** - Who will be interviewed
-> 3. **Key Information Needs** - What insights you're trying to gather
-> 4. **Output Requirements** - How the insights will be used
-> 5. **Constraints & Preferences** - Time, tone, and sensitive topics
->
-> Let's start with the basics: **What type of project are you working on, and what's the main goal of conducting these interviews?**
+> What's the primary goal of these interviews? One sentence is perfect.
 
 ---
 
 ## Key Principles
 
+### Rich UI Submissions
+
+Users may submit structured data via rich UI components. These messages arrive as:
+- `[UI_DATA_TABLE]{json}[/UI_DATA_TABLE]`
+- `[UI_PROCESS_MAP]{json}[/UI_PROCESS_MAP]`
+
+When you see these markers:
+1. Extract the JSON payload.
+2. Treat it as authoritative user input.
+3. Use it to continue the conversation and populate tools.
+4. Do NOT repeat the markers or raw JSON in user-facing text.
+
+### Selection Lists
+
+You can request a short list selection with `mcp__clara__request_selection_list`.
+Use it when the user is choosing from a handful of options (2-7 items).
+
 1. **Be conversational, not interrogative** - Have a natural dialogue
 2. **Listen actively** - Build on what users share
 3. **One question at a time** - Don't overwhelm with multiple questions
diff --git a/src/backend/clara/agents/prompts/phase1_goal_understanding.txt b/src/backend/clara/agents/prompts/phase1_goal_understanding.txt
index bb604e0..19250c5 100644
--- a/src/backend/clara/agents/prompts/phase1_goal_understanding.txt
+++ b/src/backend/clara/agents/prompts/phase1_goal_understanding.txt
@@ -1,117 +1,91 @@
 You are Clara's Interview Design Agent, here to help users create specialized interview agents tailored to their project needs.
 
-## Your Role in Phase 1
-
-Your goal is to understand the user's project through natural conversation. You need to learn about 5 key dimensions before you can design an effective interview blueprint.
-
-## The 5 Dimensions to Discover
-
-### 1. Project Context
-- What type of initiative is this? (e.g., M&A due diligence, system migration, process optimization, customer research, organizational change)
-- What's the primary goal of these interviews?
-
-### 2. Subject Matter Experts
-- Who will be interviewed? (roles, departments, expertise level)
-- How many people do you expect to interview?
+## CRITICAL OUTPUT RULES - READ FIRST
 
-### 3. Key Information Needs
-- What specific insights or information are you trying to gather?
-- Are there particular pain points, processes, systems, or decisions you need to understand?
-
-### 4. Output Requirements
-- How will the insights be used?
-- What format would be most valuable? (reports, structured data, recommendations, comparison matrices)
-
-### 5. Constraints & Preferences
-- Any time constraints per interview?
-- Tone preferences? (formal/casual, technical/accessible)
-- Sensitive topics that require special handling?
-
-## Conversation Guidelines
+**NEVER list items in your text that will also appear in the ask tool.**
 
-1. **Start with a warm greeting** - Introduce yourself and explain what you'll help them create
-2. **Be conversational** - Don't interrogate; have a natural dialogue
-3. **Listen actively** - Build on what they share; don't repeat questions they've already answered
-4. **Use `mcp__clara__ask`** - For structured choices (project type, stakeholder types, etc.)
-5. **Probe thoughtfully** - If something is unclear, ask a focused follow-up
-6. **Track coverage** - Mentally note which dimensions you've covered
+When you call `mcp__clara__ask`, your text response should be SHORT (1-2 sentences max). The ask tool renders clickable cards - DO NOT duplicate that content as text.
 
-## Opening Message Template
+BAD (duplicates content):
+```
+I need to learn about: your project goals, who will be interviewed, and what insights you need...
+[then calls mcp__clara__ask with the same items]
+```
 
-When starting a new session, greet the user like this:
+GOOD (short intro, no duplication):
+```
+Hello! I'm Clara's Interview Design Agent. Let's start with the basics.
+[then calls mcp__clara__ask with project type options]
+```
 
----
+## Your Role in Phase 1
 
-Hello! I'm Clara's Interview Design Agent.
+Your only job in Phase 1 is to capture a concise project goal and confirm it.
+Do NOT ask about interviewees, information needs, outputs, or constraints.
 
-I'm here to help you create a specialized interview agent tailored to your specific project needs.
+## Goal Capture Flow (Two Turns)
 
-**Let me understand your project**
+1. If the user already provided a goal, paraphrase it in one sentence and ask for confirmation.
+2. If the goal is missing, ask the user to describe the primary goal in one sentence.
 
-To design the most effective interview agent for you, I need to learn about:
+Use `mcp__clara__ask` for confirmation with a simple Yes/No choice.
+If the user confirms, proceed to Phase 2. If not, stay in Phase 1 and ask for the corrected goal.
 
-1. **Project Context** - What type of initiative and its primary goal
-2. **Subject Matter Experts** - Who will be interviewed
-3. **Key Information Needs** - What insights you're trying to gather
-4. **Output Requirements** - How the insights will be used
-5. **Constraints & Preferences** - Time, tone, and sensitive topics
+Example confirmation call:
+```
+mcp__clara__ask({
+  "question": "I understand the goal is: <one-sentence goal>. Is that correct?",
+  "options": [
+    {"id": "yes", "label": "Yes"},
+    {"id": "no", "label": "No"}
+  ],
+  "multi_select": false
+})
+```
 
-Let's start with the basics: **What type of project are you working on, and what's the main goal of conducting these interviews?**
+## Conversation Guidelines
 
----
+1. **Keep text SHORT** - 1-3 sentences max when using ask tool
+2. **Use `mcp__clara__ask`** - For the Yes/No confirmation only
+3. **Be conversational** - Don't interrogate; keep it simple
+4. **NO NUMBERED LISTS in text**
+5. **Do NOT reveal internal checklists** - Never list "Project Context", "Subject Matter Experts", "Key Information Needs", "Output Requirements", or "Constraints & Preferences" to the user.
 
 ## When to Transition to Phase 2
 
-You have enough information when you can confidently describe:
-- The project type and its core objective
-- Who the interviewees are (at least roles/departments)
-- The key information being sought
-- How results will be used
-
-You do NOT need perfect clarity on all 5 dimensions. If something is missing but not critical, you can infer reasonable defaults in Phase 2.
-
-When ready, use `mcp__clara__phase` to transition to "agent_configuration" and summarize what you've learned.
+As soon as the user confirms the goal, transition to Phase 2.
+Use the confirmed goal text for Phase 2 hydration.
 
 ## Tools Available
 
-- **mcp__clara__ask**: Present interactive options to the user
-- **mcp__clara__project**: Record project context (use when you have name, type, domain)
-- **mcp__clara__phase**: Transition to next phase
+- **mcp__clara__ask**: Confirm the goal with Yes/No
+- **mcp__clara__save_goal_summary**: Save `project_type="unspecified"` and the confirmed goal
+- **mcp__clara__hydrate_phase2**: Hydrate Phase 2 with the confirmed goal
+- **mcp__clara__phase**: Transition to Phase 2
 
 ## Example Interactions
 
-### User provides vague input:
-User: "I want to interview people about our systems"
+### User provides clear input:
+User: "We're acquiring a company and need to understand their IT landscape."
+
+Your text response: "Got it. I understand the goal is to map their IT landscape as part of the acquisition."
 
-Response: Acknowledge, then use `mcp__clara__ask`:
+Then call:
 ```
 mcp__clara__ask({
-  "question": "What's the primary goal of these interviews?",
+  "question": "I understand the goal is: map the target company's IT landscape for the acquisition. Is that correct?",
   "options": [
-    {"id": "ma", "label": "M&A Due Diligence", "description": "Understanding systems for a merger or acquisition"},
-    {"id": "migration", "label": "System Migration", "description": "Planning to move or consolidate systems"},
-    {"id": "optimization", "label": "Process Optimization", "description": "Finding inefficiencies and improvements"},
-    {"id": "documentation", "label": "Knowledge Capture", "description": "Recording institutional knowledge"},
-    {"id": "other", "label": "Something Else", "description": "A different goal"}
+    {"id": "yes", "label": "Yes"},
+    {"id": "no", "label": "No"}
   ],
   "multi_select": false
 })
 ```
 
-### User provides clear input:
-User: "We're acquiring a company and need to understand their IT landscape for integration planning. We'll interview their tech leads and architects."
+If Yes:
+1) `mcp__clara__save_goal_summary` with project_type="unspecified" and primary_goal
+2) `mcp__clara__hydrate_phase2` with the goal
+3) `mcp__clara__phase` to "agent_configuration"
 
-Response: Great context! Record with `mcp__clara__project`, then ask about information needs:
-```
-mcp__clara__ask({
-  "question": "What aspects of their IT landscape are most important to understand?",
-  "options": [
-    {"id": "systems", "label": "Systems & Applications", "description": "What software they run and how it's structured"},
-    {"id": "integrations", "label": "Integration Points", "description": "How systems connect and share data"},
-    {"id": "debt", "label": "Technical Debt", "description": "Known issues, outdated tech, maintenance burden"},
-    {"id": "team", "label": "Team & Ownership", "description": "Who owns what and team structure"},
-    {"id": "all", "label": "All of the Above", "description": "Comprehensive IT discovery"}
-  ],
-  "multi_select": true
-})
-```
+If No:
+Ask: "Please share the correct goal in one sentence."
diff --git a/src/backend/clara/agents/router.py b/src/backend/clara/agents/router.py
new file mode 100644
index 0000000..b08f13d
--- /dev/null
+++ b/src/backend/clara/agents/router.py
@@ -0,0 +1,1084 @@
+"""AG-UI router for switching between chat and rich UI tools."""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import re
+from dataclasses import dataclass
+from typing import Any, Literal
+
+import anthropic
+from pydantic import ValidationError
+
+from clara.agents.structured_output import RouterDecisionModel
+from clara.config import settings
+from clara.security import InputSanitizer
+
+logger = logging.getLogger(__name__)
+
+DATA_TABLE_MARKER_START = "[DATA_TABLE_SUBMIT]"
+DATA_TABLE_MARKER_END = "[/DATA_TABLE_SUBMIT]"
+PROCESS_MAP_MARKER_START = "[PROCESS_MAP_SUBMIT]"
+PROCESS_MAP_MARKER_END = "[/PROCESS_MAP_SUBMIT]"
+
+TOOL_CONFIDENCE_THRESHOLD = 0.75
+CLARIFY_CONFIDENCE_THRESHOLD = 0.45
+
+ROUTER_MODEL_MAP = {
+    "haiku": "claude-3-5-haiku-20241022",
+    "sonnet": "claude-sonnet-4-20250514",
+    "opus": "claude-opus-4-20250514",
+}
+
+ROUTER_TOOL_NAME = "router_decision"
+
+DATA_TABLE_COLUMN_TYPES = {"text", "number", "enum", "date", "url"}
+
+LIST_KEYWORDS = {
+    "stakeholder",
+    "stakeholders",
+    "list",
+    "table",
+    "spreadsheet",
+    "excel",
+    "bulk",
+    "batch",
+    "inventory",
+    "items",
+    "risks",
+    "issues",
+    "dependencies",
+    "systems",
+    "users",
+    "customers",
+    "vendors",
+}
+
+PROCESS_KEYWORDS = {
+    "process",
+    "workflow",
+    "steps",
+    "step",
+    "approval",
+    "approvals",
+    "pipeline",
+    "migration",
+    "sequence",
+    "timeline",
+    "handoff",
+    "hand-off",
+    "handover",
+    "stage",
+    "stages",
+    "phase",
+    "phases",
+}
+
+SEQUENCE_MARKERS = {"first", "then", "next", "after", "before", "finally", "last"}
+
+SELECTION_KEYWORDS = {
+    "choose",
+    "pick",
+    "select",
+    "which",
+    "options",
+    "choices",
+    "prefer",
+}
+
+INTERNAL_SELECTION_PHRASES = {
+    "project context",
+    "subject matter experts",
+    "key information needs",
+    "output requirements",
+    "constraints",
+    "constraints & preferences",
+}
+
+MAX_SELECTION_QUESTION_LEN = 160
+MAX_SELECTION_OPTION_LEN = 80
+SELECTION_MULTI_PATTERNS = [
+    r"select all",
+    r"choose all",
+    r"pick all",
+    r"all that apply",
+    r"which ones",
+    r"which of these apply",
+    r"what (?:key )?information",
+    r"what (?:areas|topics|aspects|issues|needs)",
+    r"which (?:areas|topics|aspects|issues|needs)",
+    r"are you focused on",
+    r"what are you focused on",
+]
+
+SELECTION_SINGLE_PATTERNS = [
+    r"choose one",
+    r"pick one",
+    r"which one",
+    r"either",
+    r"choose between",
+    r"pick between",
+]
+
+
+@dataclass
+class RouterDecision:
+    """Router decision output."""
+    action: Literal["tool", "chat", "clarify"]
+    tool_name: str | None = None
+    confidence: float = 0.0
+    params: dict[str, Any] | None = None
+    rationale: str | None = None
+    clarifying_question: str | None = None
+
+
+@dataclass
+class RouterState:
+    """Per-session router state."""
+    last_tool: str | None = None
+    last_tool_status: Literal["open", "completed", "canceled"] | None = None
+    pending_tool: str | None = None
+    pending_payload: dict[str, Any] | None = None
+    last_clarify: str | None = None
+
+
+@dataclass
+class UISubmission:
+    """Structured payload submitted via a rich UI component."""
+    kind: Literal["data_table", "process_map"]
+    payload: dict[str, Any]
+
+
+def parse_ui_submission(message: str) -> UISubmission | None:
+    """Extract structured UI submissions from a message."""
+    if not message:
+        return None
+
+    table_match = re.search(
+        re.escape(DATA_TABLE_MARKER_START) + r"(.*?)" + re.escape(DATA_TABLE_MARKER_END),
+        message,
+        flags=re.DOTALL,
+    )
+    if table_match:
+        payload = _load_json_payload(table_match.group(1))
+        if payload is not None:
+            return UISubmission(kind="data_table", payload=payload)
+
+    process_match = re.search(
+        re.escape(PROCESS_MAP_MARKER_START) + r"(.*?)" + re.escape(PROCESS_MAP_MARKER_END),
+        message,
+        flags=re.DOTALL,
+    )
+    if process_match:
+        payload = _load_json_payload(process_match.group(1))
+        if payload is not None:
+            return UISubmission(kind="process_map", payload=payload)
+
+    return None
+
+
+def summarize_ui_submission(submission: UISubmission) -> str:
+    """Create a compact structured summary for the main model."""
+    if submission.kind == "data_table":
+        payload = _normalize_table_payload(submission.payload)
+        summary = {
+            "type": "data_table",
+            "title": payload.get("title"),
+            "columns": payload.get("columns", []),
+            "row_count": len(payload.get("rows", [])),
+            "rows": payload.get("rows", []),
+        }
+        return f"[UI_DATA_TABLE]{json.dumps(summary, ensure_ascii=True)}[/UI_DATA_TABLE]"
+
+    payload = _normalize_process_payload(submission.payload)
+    summary = {
+        "type": "process_map",
+        "title": payload.get("title"),
+        "step_count": len(payload.get("steps", [])),
+        "steps": payload.get("steps", []),
+    }
+    return f"[UI_PROCESS_MAP]{json.dumps(summary, ensure_ascii=True)}[/UI_PROCESS_MAP]"
+
+
+def is_cancel_intent(message: str) -> bool:
+    """Detect user intent to cancel a pending UI flow."""
+    normalized = message.lower().strip()
+    cancel_terms = {"cancel", "skip", "not now", "back to chat", "no thanks"}
+    return any(term in normalized for term in cancel_terms)
+
+
+def is_tool_reply(message: str) -> bool:
+    """Detect a user reply that came from a UI selection."""
+    return _is_tool_reply(message)
+
+
+class UIRouter:
+    """Router for deciding when to render rich UI components."""
+
+    def __init__(self, model: str | None = None) -> None:
+        self.model = model or settings.router_model
+        self._client: anthropic.AsyncAnthropic | None = None
+
+    async def decide(
+        self,
+        message: str,
+        state: RouterState,
+        phase: str | None = None,
+        flow: str | None = None,
+        allow_selection: bool = True,
+    ) -> RouterDecision:
+        """Return a routing decision for a user message."""
+        message = InputSanitizer.sanitize_message(message)
+        if not message:
+            return RouterDecision(action="chat", confidence=0.0, rationale="empty_message")
+
+        if state.pending_tool and state.last_tool_status == "open":
+            return RouterDecision(action="chat", confidence=0.2, rationale="pending_tool_open")
+
+        if _is_tool_reply(message):
+            return RouterDecision(action="chat", confidence=0.1, rationale="tool_reply")
+
+        if parse_ui_submission(message):
+            return RouterDecision(action="chat", confidence=0.1, rationale="ui_submission")
+
+        if self._use_llm_router():
+            decision = await self._llm_decide(message=message, phase=phase, flow=flow)
+            if decision:
+                decision = _normalize_selection_decision(message, decision)
+                if (
+                    not allow_selection
+                    and decision.action == "tool"
+                    and decision.tool_name == "request_selection_list"
+                ):
+                    return RouterDecision(
+                        action="chat",
+                        confidence=decision.confidence,
+                        rationale="selection_disabled_on_input",
+                    )
+                return _apply_thresholds(decision)
+
+        decision = _heuristic_decide(message=message, allow_selection=allow_selection)
+        if allow_selection:
+            decision = _normalize_selection_decision(message, decision)
+        elif decision.action == "tool" and decision.tool_name == "request_selection_list":
+            decision = RouterDecision(
+                action="chat",
+                confidence=decision.confidence,
+                rationale="selection_disabled_on_input",
+            )
+        return _apply_thresholds(decision)
+
+    def _use_llm_router(self) -> bool:
+        if self.model == "heuristic":
+            return False
+        if settings.anthropic_api_key:
+            return True
+        return bool(os.getenv("ANTHROPIC_API_KEY"))
+
+    async def _llm_decide(
+        self,
+        message: str,
+        phase: str | None,
+        flow: str | None,
+    ) -> RouterDecision | None:
+        """Call a small model to decide routing using tool-forced JSON."""
+        model_id = ROUTER_MODEL_MAP.get(self.model, self.model)
+
+        try:
+            if not self._client:
+                if settings.anthropic_api_key:
+                    self._client = anthropic.AsyncAnthropic(
+                        api_key=settings.anthropic_api_key
+                    )
+                else:
+                    self._client = anthropic.AsyncAnthropic()
+
+            system_prompt = _router_system_prompt()
+            user_prompt = _router_user_prompt(message=message, phase=phase, flow=flow)
+
+            response = await self._client.messages.create(
+                model=model_id,
+                max_tokens=512,
+                max_retries=2,
+                system=system_prompt,
+                messages=[{"role": "user", "content": user_prompt}],
+                tools=[_router_tool_definition()],
+                tool_choice={"type": "tool", "name": ROUTER_TOOL_NAME},
+            )
+
+            tool_input = _extract_tool_input(response, ROUTER_TOOL_NAME)
+            if not isinstance(tool_input, dict):
+                return None
+
+            try:
+                decision_model = RouterDecisionModel.model_validate(tool_input)
+            except ValidationError as exc:
+                logger.warning("Router decision invalid: %s", exc)
+                return None
+
+            # Convert Pydantic model to RouterDecision dataclass
+            return RouterDecision(
+                action=decision_model.action,
+                tool_name=decision_model.tool_name,
+                confidence=decision_model.confidence,
+                rationale=decision_model.rationale,
+                clarifying_question=decision_model.clarifying_question,
+            )
+        except Exception as exc:  # pragma: no cover - network failure fallback
+            logger.warning("Router model failed, using heuristics: %s", exc)
+
+        return None
+
+
+def _load_json_payload(raw: str) -> dict[str, Any] | None:
+    """Parse JSON payload safely."""
+    try:
+        return json.loads(raw.strip())
+    except json.JSONDecodeError:
+        return None
+
+
+def _extract_tool_input(response: Any, tool_name: str) -> dict[str, Any] | None:
+    if not response or not hasattr(response, "content"):
+        return None
+    for block in response.content:
+        if isinstance(block, dict):
+            if block.get("type") == "tool_use" and block.get("name") == tool_name:
+                return block.get("input")
+            continue
+        block_type = getattr(block, "type", None)
+        if block_type == "tool_use" and getattr(block, "name", None) == tool_name:
+            return getattr(block, "input", None)
+    return None
+
+
+def _normalize_table_payload(payload: dict[str, Any]) -> dict[str, Any]:
+    """Normalize data table submissions for downstream use."""
+    title = InputSanitizer.sanitize_name(payload.get("title", "")) or "Data Table"
+    columns = payload.get("columns") or []
+    rows = payload.get("rows") or []
+
+    sanitized_columns = []
+    for col in columns:
+        name = InputSanitizer.sanitize_name(col.get("name")) or "Item"
+        col_type = col.get("type", "text")
+        if col_type not in DATA_TABLE_COLUMN_TYPES:
+            col_type = "text"
+        sanitized_columns.append(
+            {
+                "name": name,
+                "type": col_type,
+                "required": bool(col.get("required")),
+                "options": InputSanitizer.sanitize_array(col.get("options")) or None,
+            }
+        )
+
+    normalized_rows: list[dict[str, str]] = []
+    for row in rows:
+        if not isinstance(row, dict):
+            continue
+        cleaned = {}
+        for key, value in row.items():
+            cleaned[str(key)] = str(value).strip()
+        if any(cleaned.values()):
+            normalized_rows.append(cleaned)
+
+    return {
+        "title": title,
+        "columns": sanitized_columns,
+        "rows": normalized_rows,
+    }
+
+
+def _normalize_process_payload(payload: dict[str, Any]) -> dict[str, Any]:
+    """Normalize process map submissions for downstream use."""
+    title = InputSanitizer.sanitize_name(payload.get("title", "")) or "Process Map"
+    steps = payload.get("steps") or []
+
+    normalized_steps = []
+    for step in steps:
+        if not isinstance(step, dict):
+            continue
+        normalized_steps.append(
+            {
+                "step_name": InputSanitizer.sanitize_name(step.get("step_name")),
+                "owner": InputSanitizer.sanitize_name(step.get("owner")),
+                "outcome": InputSanitizer.sanitize_description(step.get("outcome")),
+                "edge_type": InputSanitizer.sanitize_name(step.get("edge_type")),
+            }
+        )
+
+    return {"title": title, "steps": normalized_steps}
+
+
+def _router_system_prompt() -> str:
+    return (
+        "You are a router that decides when to switch from chat to a rich UI.\n"
+        "You must call the router_decision tool with keys:\n"
+        "- action: tool|chat|clarify\n"
+        "- tool_name: request_data_table|request_process_map|request_selection_list|null\n"
+        "- confidence: number 0-1\n"
+        "- params: object|null\n"
+        "- rationale: short string\n"
+        "- clarifying_question: string|null\n"
+        "Follow these rules:\n"
+        "1) Use request_data_table for lists or bulk structured data.\n"
+        "2) Use request_process_map for workflows or step sequences.\n"
+        "3) Use request_selection_list when the user is choosing from 2-7 options.\n"
+        "3) If unclear, action=clarify with a short question.\n"
+        "4) Otherwise action=chat.\n"
+    )
+
+
+def _router_user_prompt(message: str, phase: str | None, flow: str | None) -> str:
+    context_parts = []
+    if phase:
+        context_parts.append(f"phase={phase}")
+    if flow:
+        context_parts.append(f"flow={flow}")
+    context = f"Context: {', '.join(context_parts)}" if context_parts else "Context: none"
+    return f"{context}\nUser message: {message}"
+
+
+def _router_tool_definition() -> dict[str, Any]:
+    return {
+        "name": ROUTER_TOOL_NAME,
+        "description": "Return a routing decision for Clara's UI selection.",
+        "input_schema": {
+            "type": "object",
+            "properties": {
+                "action": {
+                    "type": "string",
+                    "enum": ["tool", "chat", "clarify"],
+                    "description": "Routing action to take.",
+                },
+                "tool_name": {
+                    "type": ["string", "null"],
+                    "enum": [
+                        "request_data_table",
+                        "request_process_map",
+                        "request_selection_list",
+                        None,
+                    ],
+                    "description": "Tool to use when action=tool.",
+                },
+                "confidence": {
+                    "type": "number",
+                    "minimum": 0,
+                    "maximum": 1,
+                    "description": "Confidence score between 0 and 1.",
+                },
+                "params": {
+                    "type": ["object", "null"],
+                    "description": "Tool parameters if action=tool.",
+                    "additionalProperties": True,
+                },
+                "rationale": {
+                    "type": "string",
+                    "description": "Short explanation for the decision.",
+                },
+                "clarifying_question": {
+                    "type": ["string", "null"],
+                    "description": "Question to ask when action=clarify.",
+                },
+            },
+            "required": ["action", "confidence", "rationale"],
+        },
+    }
+
+
+def _parse_router_json(text: str) -> RouterDecision | None:
+    """Parse router JSON output with fallback to substring extraction."""
+    if not text:
+        return None
+
+    match = re.search(r"\{.*\}", text, flags=re.DOTALL)
+    if not match:
+        return None
+
+    try:
+        data = json.loads(match.group(0))
+    except json.JSONDecodeError:
+        return None
+
+    action = data.get("action")
+    if action not in {"tool", "chat", "clarify"}:
+        return None
+
+    return RouterDecision(
+        action=action,
+        tool_name=data.get("tool_name"),
+        confidence=float(data.get("confidence", 0.0) or 0.0),
+        params=data.get("params"),
+        rationale=data.get("rationale"),
+        clarifying_question=data.get("clarifying_question"),
+    )
+
+
+def _heuristic_decide(message: str, allow_selection: bool = True) -> RouterDecision:
+    """Heuristic router used when LLM router is unavailable."""
+    normalized = message.lower()
+
+    if allow_selection:
+        selection_items = _extract_selection_items(message)
+        if selection_items:
+            multi_select = _selection_is_multi(normalized)
+            params = _build_selection_params(message, selection_items, multi_select)
+            confidence = 0.82
+            return RouterDecision(
+                action="tool",
+                tool_name="request_selection_list",
+                confidence=confidence,
+                params=params,
+                rationale="selection_list_detected",
+            )
+
+    count = _extract_explicit_count(normalized)
+    list_score = sum(1 for key in LIST_KEYWORDS if key in normalized)
+    process_score = sum(1 for key in PROCESS_KEYWORDS if key in normalized)
+    sequence_hits = sum(1 for key in SEQUENCE_MARKERS if key in normalized)
+
+    list_force = count is not None and count >= 3
+    list_force = list_force or any(
+        term in normalized for term in ["spreadsheet", "excel", "bulk", "paste", "table"]
+    )
+
+    process_force = (
+        "->" in normalized
+        or sequence_hits >= 2
+        or ("process" in normalized and sequence_hits >= 1)
+    )
+
+    if process_force or (process_score >= 2 and process_score >= list_score):
+        params = _build_process_map_params(message=message, min_steps=count)
+        confidence = _confidence_from_score(process_score + sequence_hits, base=0.6)
+        if process_force:
+            confidence = max(confidence, 0.82)
+        return RouterDecision(
+            action="tool",
+            tool_name="request_process_map",
+            confidence=confidence,
+            params=params,
+            rationale="process_markers_detected",
+        )
+
+    if list_force or list_score >= 2:
+        params = _build_data_table_params(message=message, min_rows=count)
+        confidence = _confidence_from_score(list_score, base=0.6)
+        if list_force:
+            confidence = max(confidence, 0.82)
+        return RouterDecision(
+            action="tool",
+            tool_name="request_data_table",
+            confidence=confidence,
+            params=params,
+            rationale="list_markers_detected",
+        )
+
+    if process_score == 1 or sequence_hits == 1:
+        return RouterDecision(
+            action="clarify",
+            confidence=0.55,
+            clarifying_question="Want to map the steps in a process map?",
+            rationale="weak_process_signal",
+        )
+
+    if list_score == 1:
+        return RouterDecision(
+            action="clarify",
+            confidence=0.5,
+            clarifying_question="How many items are you capturing?",
+            rationale="weak_list_signal",
+        )
+
+    return RouterDecision(action="chat", confidence=0.2, rationale="no_signal")
+
+
+def _extract_explicit_count(text: str) -> int | None:
+    match = re.search(r"\b(\d{1,3})\b", text)
+    if not match:
+        return None
+    try:
+        value = int(match.group(1))
+    except ValueError:
+        return None
+    return value if value > 0 else None
+
+
+def _confidence_from_score(score: int, base: float = 0.5) -> float:
+    return min(0.95, base + (score * 0.08))
+
+
+def _build_data_table_params(message: str, min_rows: int | None = None) -> dict[str, Any]:
+    normalized = message.lower()
+    title = "Data Table"
+    columns = _default_columns()
+
+    if "stakeholder" in normalized:
+        title = "Stakeholder List"
+        columns = [
+            {"name": "Name", "type": "text", "required": True},
+            {"name": "Role", "type": "text", "required": True},
+            {"name": "Influence Level", "type": "enum", "required": False,
+             "options": ["Low", "Medium", "High"]},
+        ]
+    elif "risk" in normalized:
+        title = "Risk Register"
+        columns = [
+            {"name": "Risk", "type": "text", "required": True},
+            {"name": "Severity", "type": "enum", "required": False,
+             "options": ["Low", "Medium", "High"]},
+            {"name": "Owner", "type": "text", "required": False},
+        ]
+    elif "system" in normalized:
+        title = "System Inventory"
+        columns = [
+            {"name": "System", "type": "text", "required": True},
+            {"name": "Owner", "type": "text", "required": False},
+            {"name": "Criticality", "type": "enum", "required": False,
+             "options": ["Low", "Medium", "High"]},
+        ]
+
+    max_rows = 50
+    min_rows = min_rows or 3
+    min_rows = min(max_rows, min_rows)
+    starter_rows = min(5, min_rows)
+
+    return {
+        "title": title,
+        "columns": columns,
+        "min_rows": min_rows,
+        "starter_rows": starter_rows,
+        "input_modes": ["paste", "inline"],
+        "summary_prompt": "Capture the list as structured rows.",
+    }
+
+
+def _default_columns() -> list[dict[str, Any]]:
+    return [
+        {"name": "Item", "type": "text", "required": True},
+        {"name": "Description", "type": "text", "required": False},
+        {"name": "Owner", "type": "text", "required": False},
+    ]
+
+
+def _sanitize_columns(columns: Any) -> list[dict[str, Any]]:
+    if not isinstance(columns, list):
+        return _default_columns()
+
+    sanitized = []
+    for column in columns:
+        if not isinstance(column, dict):
+            continue
+        name = InputSanitizer.sanitize_name(column.get("name"))
+        if not name:
+            continue
+        col_type = column.get("type", "text")
+        if col_type not in DATA_TABLE_COLUMN_TYPES:
+            col_type = "text"
+        options = InputSanitizer.sanitize_array(column.get("options")) or None
+        sanitized.append(
+            {
+                "name": name,
+                "type": col_type,
+                "required": bool(column.get("required")),
+                "options": options,
+            }
+        )
+
+    return sanitized or _default_columns()
+
+
+def _build_process_map_params(message: str, min_steps: int | None = None) -> dict[str, Any]:
+    normalized = message.lower()
+    title = "Process Map"
+    if "approval" in normalized:
+        title = "Approval Process"
+    elif "migration" in normalized:
+        title = "Migration Workflow"
+
+    min_steps = min_steps or 3
+    min_steps = min(min_steps, 20)
+    return {
+        "title": title,
+        "required_fields": ["step_name", "owner", "outcome"],
+        "edge_types": ["sequence", "approval", "parallel"],
+        "min_steps": min_steps,
+        "seed_nodes": [],
+    }
+
+
+def _build_selection_params(
+    message: str,
+    items: list[str],
+    multi_select: bool,
+) -> dict[str, Any]:
+    question = _extract_question(message)
+    if not question:
+        question = "Select the options that apply." if multi_select else "Choose one option."
+
+    options = []
+    for item in items:
+        label = InputSanitizer.sanitize_name(item)
+        if not label:
+            continue
+        option_id = _slugify(label)
+        options.append({"id": option_id, "label": label})
+
+    options = _ensure_other_option(options)
+
+    return {
+        "question": question,
+        "options": options,
+        "multi_select": multi_select,
+    }
+
+
+def _is_tool_reply(message: str) -> bool:
+    normalized = message.lower().strip()
+    if normalized.startswith("i chose:"):
+        return True
+    if normalized.startswith("[prompt_saved]"):
+        return True
+    return False
+
+
+def _extract_selection_items(message: str) -> list[str]:
+    normalized = message.lower()
+    if not any(keyword in normalized for keyword in SELECTION_KEYWORDS):
+        return []
+
+    patterns = [
+        r"(?:options|choices)\s*[:\-]\s*(.+)",
+        r"(?:choose between|pick between|select from|choose from|pick from)\s+(.+)",
+    ]
+
+    segment = None
+    for pattern in patterns:
+        match = re.search(pattern, message, flags=re.IGNORECASE)
+        if match:
+            segment = match.group(1)
+            break
+
+    if segment is None:
+        if any(sep in message for sep in [",", "/", "|", " or "]):
+            segment = message
+        else:
+            return []
+
+    segment = re.split(r"[?.!]", segment)[0]
+    items = _split_list_items(segment)
+
+    cleaned = []
+    for item in items:
+        value = item.strip().strip('"').strip("'")
+        if len(value) < 2:
+            continue
+        cleaned.append(value)
+
+    if 2 <= len(cleaned) <= 7:
+        return cleaned
+    return []
+
+
+def _split_list_items(segment: str) -> list[str]:
+    if "," in segment or ";" in segment:
+        return re.split(r"\s*[,;]\s*", segment)
+    if "/" in segment or "|" in segment:
+        return re.split(r"\s*[\/|]\s*", segment)
+    if re.search(r"\bor\b", segment, flags=re.IGNORECASE):
+        return re.split(r"\s+\bor\b\s+", segment, flags=re.IGNORECASE)
+    return re.split(r"\s+\band\b\s+", segment, flags=re.IGNORECASE)
+
+
+def _clean_list_item(item: str) -> str:
+    cleaned = re.sub(r"[*_`]+", "", item)
+    cleaned = re.sub(r"\s+", " ", cleaned)
+    return cleaned.strip(" \t-–—:;.,")
+
+
+def _extract_numbered_items(message: str) -> list[str]:
+    pattern = re.compile(
+        r"(?:^|\s)(?:\d+)[\.\)]\s*([^\n]+?)(?=\s*\d+[\.\)]|$)",
+        flags=re.DOTALL,
+    )
+    items: list[str] = []
+    for match in pattern.finditer(message):
+        value = _clean_list_item(match.group(1))
+        if value:
+            items.append(value)
+    return items
+
+
+def _extract_bulleted_items(message: str) -> list[str]:
+    items: list[str] = []
+    for line in message.splitlines():
+        match = re.match(r"\s*(?:[-*•])\s+(.+)", line)
+        if not match:
+            continue
+        value = _clean_list_item(match.group(1))
+        if value:
+            items.append(value)
+    return items
+
+
+def strip_selection_list_from_text(message: str) -> str:
+    lines: list[str] = []
+    for line in message.splitlines():
+        stripped = line.strip()
+        if not stripped:
+            lines.append(line)
+            continue
+        if re.match(r"(?:[-*•]|\d+[.)])\s+", stripped):
+            continue
+        lowered = stripped.lower()
+        if lowered.startswith(("for example", "for instance", "e.g.")):
+            continue
+        lines.append(line)
+    return "\n".join(lines).strip()
+
+
+def infer_selection_from_assistant_output(message: str) -> RouterDecision | None:
+    """Infer a selection list from assistant output."""
+    sanitized = InputSanitizer.sanitize_message(message)
+    if not sanitized:
+        return None
+    if not _assistant_output_has_selection_prompt(sanitized):
+        return None
+
+    question = _extract_question(sanitized)
+    if question and len(question) > MAX_SELECTION_QUESTION_LEN:
+        return None
+
+    selection_items = _extract_selection_items(sanitized)
+    if not selection_items:
+        selection_items = _extract_numbered_items(sanitized)
+    if not selection_items:
+        selection_items = _extract_bulleted_items(sanitized)
+
+    selection_items = _sanitize_selection_items(selection_items)
+    if not (2 <= len(selection_items) <= 7):
+        return None
+
+    multi_select = _selection_is_multi(sanitized.lower())
+    params = _build_selection_params(sanitized, selection_items, multi_select)
+    return RouterDecision(
+        action="tool",
+        tool_name="request_selection_list",
+        confidence=0.7,
+        params=params,
+        rationale="assistant_output_list",
+    )
+
+
+def _assistant_output_has_selection_prompt(message: str) -> bool:
+    normalized = message.lower()
+    if any(keyword in normalized for keyword in SELECTION_KEYWORDS):
+        return True
+    if "for example" in normalized or "for instance" in normalized or "e.g." in normalized:
+        return True
+    if re.search(r"which (?:one|of these|of the following)", normalized):
+        return True
+    return False
+
+
+def _sanitize_selection_items(items: list[str]) -> list[str]:
+    cleaned: list[str] = []
+    for item in items:
+        value = _clean_list_item(item)
+        if not value or len(value) > MAX_SELECTION_OPTION_LEN:
+            continue
+        if _is_internal_option(value):
+            continue
+        cleaned.append(value)
+    return cleaned
+
+
+def _is_internal_option(label: str) -> bool:
+    normalized = label.lower()
+    return any(phrase in normalized for phrase in INTERNAL_SELECTION_PHRASES)
+
+
+def _selection_is_multi(normalized: str) -> bool:
+    for pattern in SELECTION_MULTI_PATTERNS:
+        if re.search(pattern, normalized, flags=re.IGNORECASE):
+            return True
+    for pattern in SELECTION_SINGLE_PATTERNS:
+        if re.search(pattern, normalized, flags=re.IGNORECASE):
+            return False
+    return False
+
+
+def _selection_is_explicit(normalized: str) -> bool:
+    keywords = {"options", "choices", "choose", "select", "pick"}
+    return any(keyword in normalized for keyword in keywords)
+
+
+def _extract_question(message: str) -> str | None:
+    if "?" in message:
+        question = message.split("?", 1)[0].strip()
+        if question:
+            return f"{question}?"
+    return None
+
+
+def _slugify(value: str) -> str:
+    slug = re.sub(r"[^a-z0-9]+", "_", value.lower()).strip("_")
+    return slug or "option"
+
+
+def _ensure_other_option(options: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    for option in options:
+        label = str(option.get("label", "")).lower().strip()
+        if label.startswith("other") or label in {"something else"}:
+            option["requires_input"] = True
+            return options
+
+    other_id = "other"
+    existing_ids = {str(option.get("id")) for option in options}
+    if other_id in existing_ids:
+        suffix = 2
+        while f"other_{suffix}" in existing_ids:
+            suffix += 1
+        other_id = f"other_{suffix}"
+
+    return [
+        *options,
+        {
+            "id": other_id,
+            "label": "Other",
+            "description": "Something else",
+            "requires_input": True,
+        },
+    ]
+
+
+def _normalize_selection_decision(message: str, decision: RouterDecision) -> RouterDecision:
+    if decision.tool_name != "request_selection_list":
+        return decision
+
+    params = dict(decision.params or {})
+    params["multi_select"] = _selection_is_multi(message.lower())
+    if "options" in params and isinstance(params["options"], list):
+        params["options"] = _ensure_other_option(params["options"])
+    decision.params = params
+    return decision
+
+
+def build_ui_component(decision: RouterDecision) -> dict[str, Any] | None:
+    """Convert router params into UI component payloads."""
+    if decision.action != "tool" or not decision.tool_name:
+        return None
+
+    params = decision.params or {}
+
+    if decision.tool_name == "request_data_table":
+        min_rows = _safe_int(params.get("min_rows"), 3)
+        min_rows = min(min_rows, 50)
+        starter_rows = _safe_int(params.get("starter_rows"), min_rows)
+        starter_rows = min(starter_rows, min_rows)
+        input_modes = InputSanitizer.sanitize_array(params.get("input_modes"))
+        title = InputSanitizer.sanitize_name(params.get("title")) or "Data Table"
+        return {
+            "type": "data_table",
+            "title": title,
+            "columns": _sanitize_columns(params.get("columns")),
+            "min_rows": min_rows,
+            "starter_rows": starter_rows,
+            "input_modes": input_modes or ["paste", "inline"],
+            "summary_prompt": params.get("summary_prompt", ""),
+        }
+
+    if decision.tool_name == "request_process_map":
+        min_steps = _safe_int(params.get("min_steps"), 3)
+        min_steps = min(min_steps, 20)
+        required_fields = InputSanitizer.sanitize_array(params.get("required_fields"))
+        edge_types = InputSanitizer.sanitize_array(params.get("edge_types"))
+        seed_nodes = InputSanitizer.sanitize_array(params.get("seed_nodes"))
+        title = InputSanitizer.sanitize_name(params.get("title")) or "Process Map"
+        return {
+            "type": "process_map",
+            "title": title,
+            "required_fields": required_fields or ["step_name", "owner", "outcome"],
+            "edge_types": edge_types or ["sequence", "approval", "parallel"],
+            "min_steps": min_steps,
+            "seed_nodes": seed_nodes,
+        }
+
+    if decision.tool_name == "request_selection_list":
+        question = InputSanitizer.sanitize_description(params.get("question"))
+        options = params.get("options") or []
+        sanitized_options = []
+        for option in options:
+            if not isinstance(option, dict):
+                continue
+            label = InputSanitizer.sanitize_name(option.get("label"))
+            if not label:
+                continue
+            option_id = option.get("id") or _slugify(label)
+            requires_input = bool(option.get("requires_input"))
+            if label.strip().lower().startswith("other"):
+                requires_input = True
+            sanitized_options.append(
+                {
+                    "id": InputSanitizer.sanitize_name(option_id),
+                    "label": label,
+                    "description": InputSanitizer.sanitize_description(option.get("description")),
+                    "requires_input": requires_input,
+                }
+            )
+
+        return {
+            "type": "user_input_required",
+            "question": question or "Choose an option.",
+            "options": sanitized_options,
+            "multi_select": bool(params.get("multi_select")),
+        }
+
+    return None
+
+
+def _safe_int(value: Any, default: int) -> int:
+    try:
+        return int(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def _apply_thresholds(decision: RouterDecision) -> RouterDecision:
+    """Apply confidence thresholds to router decisions."""
+    if decision.action == "tool" and decision.confidence < TOOL_CONFIDENCE_THRESHOLD:
+        if decision.confidence >= CLARIFY_CONFIDENCE_THRESHOLD:
+            return RouterDecision(
+                action="clarify",
+                confidence=decision.confidence,
+                clarifying_question=decision.clarifying_question
+                or _clarify_question_for_tool(decision.tool_name),
+                rationale="threshold_downgrade_to_clarify",
+            )
+        return RouterDecision(
+            action="chat",
+            confidence=decision.confidence,
+            rationale="threshold_downgrade_to_chat",
+        )
+
+    if decision.action == "clarify" and decision.confidence < CLARIFY_CONFIDENCE_THRESHOLD:
+        return RouterDecision(
+            action="chat",
+            confidence=decision.confidence,
+            rationale="clarify_below_threshold",
+        )
+
+    return decision
+
+
+def _clarify_question_for_tool(tool_name: str | None) -> str:
+    if tool_name == "request_data_table":
+        return "How many items are you capturing?"
+    if tool_name == "request_process_map":
+        return "Want to map the steps in a process map?"
+    if tool_name == "request_selection_list":
+        return "Do you want to pick from a short list?"
+    return "Can you clarify what you need captured?"
diff --git a/src/backend/clara/agents/simulation_agent.py b/src/backend/clara/agents/simulation_agent.py
index b670025..914f63c 100644
--- a/src/backend/clara/agents/simulation_agent.py
+++ b/src/backend/clara/agents/simulation_agent.py
@@ -18,6 +18,18 @@
 import httpx
 from claude_agent_sdk import ClaudeAgentOptions, ClaudeSDKClient
 
+from clara.agents.router import (
+    RouterState,
+    UIRouter,
+    build_ui_component,
+    infer_selection_from_assistant_output,
+    is_cancel_intent,
+    is_tool_reply,
+    parse_ui_submission,
+    strip_selection_list_from_text,
+    summarize_ui_submission,
+)
+from clara.agents.structured_output import StructuredOutputParser, ui_component_to_payload
 from clara.config import settings
 
 logger = logging.getLogger(__name__)
@@ -71,6 +83,11 @@ class SimulationSession:
     model: str = field(default_factory=lambda: settings.simulation_interviewer_model)
     persona: PersonaConfig | None = None  # For auto-simulation mode
     messages: list[dict] = field(default_factory=list)
+    router_state: RouterState = field(default_factory=RouterState)
+    router: UIRouter = field(default_factory=UIRouter)
+    _structured_output_parser: StructuredOutputParser = field(
+        default_factory=StructuredOutputParser, repr=False
+    )
 
     # Timestamps for TTL cleanup
     created_at: datetime = field(default_factory=datetime.now)
@@ -221,7 +238,11 @@ async def get_introduction(self) -> AsyncGenerator[AGUIEvent, None]:
 
         yield AGUIEvent(type="TEXT_MESSAGE_END", data={"role": "assistant"})
 
-    async def send_user_message(self, user_message: str) -> AsyncGenerator[AGUIEvent, None]:
+    async def send_user_message(
+        self,
+        user_message: str,
+        apply_router: bool = True
+    ) -> AsyncGenerator[AGUIEvent, None]:
         """Send a message from the user (human or simulated) and get the interviewer's response."""
         if not self._running or not self._interviewer_client:
             raise RuntimeError("Session not started")
@@ -229,6 +250,32 @@ async def send_user_message(self, user_message: str) -> AsyncGenerator[AGUIEvent
         # Update last activity
         self.last_activity = datetime.now()
 
+        submission = parse_ui_submission(user_message)
+        if submission:
+            self.router_state.last_tool = submission.kind
+            self.router_state.last_tool_status = "completed"
+            self.router_state.pending_tool = None
+            self.router_state.pending_payload = None
+            user_message = summarize_ui_submission(submission)
+
+        if (
+            not submission
+            and self.router_state.pending_tool == "request_selection_list"
+            and is_tool_reply(user_message)
+        ):
+            self.router_state.last_tool_status = "completed"
+            self.router_state.pending_tool = None
+            self.router_state.pending_payload = None
+
+        if (
+            not submission
+            and self.router_state.pending_tool
+            and is_cancel_intent(user_message)
+        ):
+            self.router_state.last_tool_status = "canceled"
+            self.router_state.pending_tool = None
+            self.router_state.pending_payload = None
+
         # Store user message
         self.messages.append({"role": "user", "content": user_message})
 
@@ -236,9 +283,78 @@ async def send_user_message(self, user_message: str) -> AsyncGenerator[AGUIEvent
         if len(self.messages) > MAX_MESSAGE_HISTORY:
             self.messages = self.messages[-MAX_MESSAGE_HISTORY:]
 
+        if apply_router and not submission:
+            decision = await self.router.decide(
+                message=user_message,
+                state=self.router_state,
+                phase="interview",
+                flow="simulation",
+                allow_selection=False,
+            )
+
+            if decision.action in {"tool", "clarify"}:
+                preamble = ""
+                if decision.action == "tool":
+                    ui_component = build_ui_component(decision)
+                    if ui_component:
+                        if decision.tool_name == "request_data_table":
+                            preamble = (
+                                "Let's capture that in a table so you can paste rows quickly."
+                            )
+                        elif decision.tool_name == "request_process_map":
+                            preamble = (
+                                "Let's map the steps so we capture the workflow accurately."
+                            )
+                        elif decision.tool_name == "request_selection_list":
+                            preamble = "Pick the options that apply."
+
+                        self.router_state.pending_tool = decision.tool_name
+                        self.router_state.pending_payload = decision.params
+                        self.router_state.last_tool = decision.tool_name
+                        self.router_state.last_tool_status = "open"
+
+                        self.messages.append({"role": "assistant", "content": preamble})
+                        if len(self.messages) > MAX_MESSAGE_HISTORY:
+                            self.messages = self.messages[-MAX_MESSAGE_HISTORY:]
+
+                        yield AGUIEvent(
+                            type="TEXT_MESSAGE_CONTENT",
+                            data={"delta": preamble, "role": "assistant"}
+                        )
+                        yield AGUIEvent(
+                            type="CUSTOM",
+                            data={
+                                "name": (
+                                    "clara:data_table"
+                                    if decision.tool_name == "request_data_table"
+                                    else "clara:process_map"
+                                    if decision.tool_name == "request_process_map"
+                                    else "clara:ask"
+                                ),
+                                "value": ui_component,
+                            },
+                        )
+                        yield AGUIEvent(type="TEXT_MESSAGE_END", data={"role": "assistant"})
+                        return
+
+                if decision.action == "clarify":
+                    question = decision.clarifying_question or "Can you clarify?"
+                    self.router_state.last_clarify = question
+                    self.messages.append({"role": "assistant", "content": question})
+                    if len(self.messages) > MAX_MESSAGE_HISTORY:
+                        self.messages = self.messages[-MAX_MESSAGE_HISTORY:]
+
+                    yield AGUIEvent(
+                        type="TEXT_MESSAGE_CONTENT",
+                        data={"delta": question, "role": "assistant"}
+                    )
+                    yield AGUIEvent(type="TEXT_MESSAGE_END", data={"role": "assistant"})
+                    return
+
         try:
             await self._interviewer_client.query(prompt=user_message)
 
+            use_structured_output = self._structured_output_parser.is_available()
             current_text = ""
             async for msg in self._interviewer_client.receive_response():
                 msg_type = type(msg).__name__
@@ -254,15 +370,89 @@ async def send_user_message(self, user_message: str) -> AsyncGenerator[AGUIEvent
                                     else:
                                         delta = new_text
                                     current_text = new_text
-                                    if delta:
+                                    if delta and not use_structured_output:
                                         yield AGUIEvent(
                                             type="TEXT_MESSAGE_CONTENT",
                                             data={"delta": delta, "role": "assistant"}
                                         )
 
             # Store assistant message
-            if current_text:
-                self.messages.append({"role": "assistant", "content": current_text})
+            if use_structured_output and current_text:
+                structured_response = None
+                ui_payload: dict | None = None
+                selection_decision = None
+                if not self.router_state.pending_tool:
+                    structured_response = await self._structured_output_parser.parse(
+                        message=current_text,
+                        phase="interview",
+                        flow="simulation",
+                    )
+                    if structured_response:
+                        ui_payload = ui_component_to_payload(structured_response.ui_component)
+
+                if not ui_payload and not self.router_state.pending_tool:
+                    selection_decision = infer_selection_from_assistant_output(current_text)
+                    if selection_decision:
+                        ui_payload = build_ui_component(selection_decision)
+
+                display_text = (
+                    structured_response.display_text
+                    if structured_response
+                    else current_text
+                )
+                if ui_payload and display_text and ui_payload.get("type") == "user_input_required":
+                    cleaned_text = strip_selection_list_from_text(display_text)
+                    if cleaned_text:
+                        display_text = cleaned_text
+                    elif selection_decision and selection_decision.params:
+                        display_text = (
+                            selection_decision.params.get("question") or display_text
+                        )
+
+                if display_text:
+                    self.messages.append({"role": "assistant", "content": display_text})
+                    yield AGUIEvent(
+                        type="TEXT_MESSAGE_CONTENT",
+                        data={"delta": display_text, "role": "assistant"}
+                    )
+
+                if ui_payload:
+                    tool_type = ui_payload.get("type")
+                    tool_name = "request_selection_list"
+                    custom_name = "clara:ask"
+                    if tool_type == "data_table":
+                        tool_name = "request_data_table"
+                        custom_name = "clara:data_table"
+                    elif tool_type == "process_map":
+                        tool_name = "request_process_map"
+                        custom_name = "clara:process_map"
+
+                    self.router_state.pending_tool = tool_name
+                    self.router_state.pending_payload = ui_payload
+                    self.router_state.last_tool = tool_name
+                    self.router_state.last_tool_status = "open"
+                    yield AGUIEvent(
+                        type="CUSTOM",
+                        data={"name": custom_name, "value": ui_payload}
+                    )
+
+            if not use_structured_output:
+                if current_text:
+                    self.messages.append({"role": "assistant", "content": current_text})
+
+                if current_text and not self.router_state.pending_tool:
+                    selection_decision = infer_selection_from_assistant_output(current_text)
+                    if selection_decision:
+                        ui_component = build_ui_component(selection_decision)
+                        if ui_component:
+                            self.router_state.pending_tool = selection_decision.tool_name
+                            self.router_state.pending_payload = selection_decision.params
+                            self.router_state.last_tool = selection_decision.tool_name
+                            self.router_state.last_tool_status = "open"
+                            yield AGUIEvent(
+                                type="CUSTOM",
+                                data={"name": "clara:ask", "value": ui_component}
+                            )
 
             yield AGUIEvent(type="TEXT_MESSAGE_END", data={"role": "assistant"})
 
@@ -371,6 +561,7 @@ def reset(self):
         """Reset conversation history."""
         self.messages = []
         self._introduction_sent = False
+        self.router_state = RouterState()
 
 
 def is_safe_url(url: str) -> bool:
diff --git a/src/backend/clara/agents/structured_output.py b/src/backend/clara/agents/structured_output.py
new file mode 100644
index 0000000..f33aec2
--- /dev/null
+++ b/src/backend/clara/agents/structured_output.py
@@ -0,0 +1,586 @@
+"""Structured output models for Clara's Design Assistant.
+
+This module defines Pydantic models and a tool-forced parser that ensure
+the LLM produces structured, validated responses that clearly separate
+display text from interactive UI components.
+"""
+
+import logging
+import os
+import re
+from typing import Any, Literal, Self
+
+import anthropic
+from pydantic import BaseModel, Field, ValidationError, field_validator, model_validator
+
+from clara.config import settings
+from clara.security import InputSanitizer
+
+logger = logging.getLogger(__name__)
+
+STRUCTURED_OUTPUT_MODEL_MAP = {
+    "haiku": "claude-3-5-haiku-20241022",
+    "sonnet": "claude-sonnet-4-20250514",
+    "opus": "claude-opus-4-20250514",
+}
+
+STRUCTURED_OUTPUT_TOOL_NAME = "structured_response"
+
+INTERNAL_SELECTION_PHRASES = {
+    "project context",
+    "subject matter experts",
+    "key information needs",
+    "output requirements",
+    "constraints",
+    "constraints & preferences",
+}
+
+class SelectionOption(BaseModel):
+    """A single option in a selection list."""
+
+    id: str = Field(description="Unique identifier for the option")
+    label: str = Field(description="Display label for the option")
+    description: str | None = Field(
+        default=None, description="Optional description of the option"
+    )
+    requires_input: bool = Field(
+        default=False,
+        description="True if selecting this option requires additional text input",
+    )
+
+
+def _normalize_selection_options(options: list[SelectionOption]) -> list[SelectionOption]:
+    if len(options) < 2:
+        raise ValueError("Selection must have at least 2 options")
+
+    normalized: list[SelectionOption] = []
+    other_option: SelectionOption | None = None
+
+    for option in options:
+        is_other = option.label.lower().startswith("other") or option.id == "other"
+        if is_other:
+            if not option.requires_input:
+                option = option.model_copy(update={"requires_input": True})
+            if other_option is None:
+                other_option = option
+            continue
+        normalized.append(option)
+
+    if other_option is None:
+        other_option = SelectionOption(
+            id="other",
+            label="Other",
+            description="Something else",
+            requires_input=True,
+        )
+
+    max_non_other = 6
+    if len(normalized) > max_non_other:
+        normalized = normalized[:max_non_other]
+
+    normalized.append(other_option)
+
+    if len(normalized) < 2:
+        raise ValueError("Selection must have at least 2 options")
+    return normalized
+
+
+def _slugify(value: str) -> str:
+    slug = re.sub(r"[^a-z0-9]+", "_", value.lower()).strip("_")
+    return slug or "option"
+
+
+def _compact_text(value: str | None, max_len: int) -> str:
+    if not value:
+        return ""
+    cleaned = re.sub(r"[`*_>#]+", "", value)
+    cleaned = re.sub(r"\s+", " ", cleaned).strip(" \t-:;.,")
+    if not cleaned or len(cleaned) > max_len:
+        return ""
+    return cleaned
+
+
+def _is_internal_option(label: str) -> bool:
+    normalized = label.lower()
+    return any(phrase in normalized for phrase in INTERNAL_SELECTION_PHRASES)
+
+
+class DataTableColumn(BaseModel):
+    """Column definition for a data table."""
+
+    name: str = Field(description="Column header name")
+    type: Literal["text", "number", "enum", "date", "url"] = Field(
+        default="text", description="Data type for the column"
+    )
+    required: bool = Field(default=False, description="Whether the column is required")
+    options: list[str] | None = Field(
+        default=None, description="Options for enum columns"
+    )
+
+
+class SelectionUIComponent(BaseModel):
+    """Selection list UI component."""
+
+    type: Literal["selection"] = "selection"
+    question: str = Field(description="Question to ask the user")
+    options: list[SelectionOption] = Field(description="Available options to choose from")
+    multi_select: bool = Field(
+        default=False, description="Whether multiple options can be selected"
+    )
+
+    @field_validator("options")
+    @classmethod
+    def validate_options(cls, v: list[SelectionOption]) -> list[SelectionOption]:
+        return _normalize_selection_options(list(v))
+
+
+class DataTableUIComponent(BaseModel):
+    """Data table UI component for bulk data entry."""
+
+    type: Literal["data_table"] = "data_table"
+    title: str = Field(description="Title of the data table")
+    columns: list[DataTableColumn] = Field(description="Column definitions")
+    min_rows: int = Field(default=3, ge=1, le=50, description="Minimum rows required")
+    starter_rows: int = Field(default=3, ge=1, le=50, description="Initial empty rows")
+    input_modes: list[Literal["paste", "inline", "import"]] = Field(
+        default_factory=lambda: ["paste", "inline"],
+        description="Allowed input modes for the table",
+    )
+    summary_prompt: str | None = Field(
+        default=None, description="Prompt for summarizing collected data"
+    )
+
+
+class ProcessMapUIComponent(BaseModel):
+    """Process map UI component for workflow capture."""
+
+    type: Literal["process_map"] = "process_map"
+    title: str = Field(description="Title of the process map")
+    required_fields: list[str] = Field(
+        default_factory=lambda: ["step_name", "owner", "outcome"],
+        description="Required fields per step",
+    )
+    edge_types: list[str] = Field(
+        default_factory=lambda: ["sequence", "approval", "parallel"],
+        description="Types of connections between steps",
+    )
+    min_steps: int = Field(default=3, ge=2, le=20, description="Minimum steps required")
+    seed_nodes: list[str] = Field(
+        default_factory=list, description="Pre-filled step names"
+    )
+
+
+class NoUIComponent(BaseModel):
+    """Marker for responses that don't include UI components."""
+
+    type: Literal["none"] = "none"
+
+
+# Union type for all UI components
+UIComponent = SelectionUIComponent | DataTableUIComponent | ProcessMapUIComponent | NoUIComponent
+
+
+class DesignAssistantResponse(BaseModel):
+    """Structured response from Clara's Design Assistant.
+
+    This model ensures the LLM clearly separates display text from
+    interactive UI components, preventing the duplicate content issue.
+    """
+
+    display_text: str = Field(
+        description=(
+            "Conversational text to show the user. "
+            "Do NOT include numbered lists if presenting options as a selection UI. "
+            "Keep this concise when a UI component is present."
+        )
+    )
+    ui_component: UIComponent = Field(
+        default_factory=lambda: NoUIComponent(),
+        description=(
+            "Interactive UI component for user input. "
+            "Use 'selection' for choices, 'data_table' for bulk data, "
+            "'process_map' for workflows, 'none' for chat-only responses."
+        ),
+    )
+    phase_transition: str | None = Field(
+        default=None,
+        description=(
+            "If phase should change, specify: goal_understanding, "
+            "agent_configuration, blueprint_design, or complete"
+        ),
+    )
+
+    @field_validator("display_text")
+    @classmethod
+    def validate_display_text(cls, v: str) -> str:
+        if not v or not v.strip():
+            raise ValueError("display_text cannot be empty")
+        return v.strip()
+
+
+class StructuredOutputPayload(BaseModel):
+    """Payload shape returned by the structured_response tool."""
+
+    display_text: str = Field(description="Conversational text to show the user.")
+    ui_type: Literal["none", "selection", "data_table", "process_map"] = Field(
+        default="none",
+        description="Type of UI component to render.",
+    )
+    ui: dict[str, Any] | None = Field(
+        default=None,
+        description="UI payload for the selected ui_type.",
+    )
+    phase_transition: str | None = Field(
+        default=None,
+        description=(
+            "If phase should change, specify: goal_understanding, "
+            "agent_configuration, blueprint_design, or complete"
+        ),
+    )
+
+    @field_validator("display_text")
+    @classmethod
+    def validate_display_text(cls, v: str) -> str:
+        if not v or not v.strip():
+            raise ValueError("display_text cannot be empty")
+        return v.strip()
+
+
+class RouterDecisionModel(BaseModel):
+    """Structured model for routing decisions.
+
+    Used by the small router model (Haiku) to decide whether
+    to render a rich UI component for user input.
+    """
+
+    action: Literal["tool", "chat", "clarify"] = Field(
+        description=(
+            "'tool' = render a rich UI component, "
+            "'chat' = continue with normal conversation, "
+            "'clarify' = ask a clarifying question before deciding"
+        )
+    )
+    tool_name: Literal[
+        "request_data_table", "request_process_map", "request_selection_list"
+    ] | None = Field(
+        default=None,
+        description="Which tool to use. Required if action='tool'.",
+    )
+    confidence: float = Field(
+        ge=0.0,
+        le=1.0,
+        description="Confidence in the decision (0-1)",
+    )
+    rationale: str = Field(
+        description="Brief explanation of why this decision was made"
+    )
+    clarifying_question: str | None = Field(
+        default=None,
+        description="Question to ask if action='clarify'",
+    )
+
+    @model_validator(mode="after")
+    def validate_action_requirements(self) -> Self:
+        """Validate that action-specific fields are provided."""
+        if self.action == "tool" and self.tool_name is None:
+            raise ValueError("tool_name is required when action='tool'")
+        if self.action == "clarify" and not self.clarifying_question:
+            raise ValueError("clarifying_question is required when action='clarify'")
+        return self
+
+
+class DataTableParams(BaseModel):
+    """Parameters for request_data_table tool."""
+
+    title: str = Field(description="Title for the data table")
+    columns: list[DataTableColumn] = Field(description="Column definitions")
+    min_rows: int = Field(default=3, ge=1, le=50)
+    starter_rows: int = Field(default=3, ge=1, le=50)
+    input_modes: list[Literal["paste", "inline", "import"]] = Field(
+        default_factory=lambda: ["paste", "inline"]
+    )
+    summary_prompt: str | None = None
+
+
+class ProcessMapParams(BaseModel):
+    """Parameters for request_process_map tool."""
+
+    title: str = Field(description="Title for the process map")
+    required_fields: list[str] = Field(
+        default_factory=lambda: ["step_name", "owner", "outcome"]
+    )
+    edge_types: list[str] = Field(
+        default_factory=lambda: ["sequence", "approval", "parallel"]
+    )
+    min_steps: int = Field(default=3, ge=2, le=20)
+    seed_nodes: list[str] = Field(default_factory=list)
+
+
+class SelectionListParams(BaseModel):
+    """Parameters for request_selection_list tool."""
+
+    question: str = Field(description="Question to ask the user")
+    options: list[SelectionOption] = Field(description="Options to choose from")
+    multi_select: bool = Field(default=False)
+
+    @field_validator("options")
+    @classmethod
+    def validate_options(cls, v: list[SelectionOption]) -> list[SelectionOption]:
+        return _normalize_selection_options(list(v))
+
+
+def ui_component_to_payload(component: UIComponent) -> dict[str, Any] | None:
+    """Convert a structured UI component into the UI payload expected by the frontend."""
+    if isinstance(component, NoUIComponent):
+        return None
+
+    payload = component.model_dump()
+    if isinstance(component, SelectionUIComponent):
+        payload["type"] = "user_input_required"
+    return payload
+
+
+def _structured_output_tool_definition() -> dict[str, Any]:
+    return {
+        "name": STRUCTURED_OUTPUT_TOOL_NAME,
+        "description": "Return structured output for Clara UI rendering.",
+        "input_schema": {
+            "type": "object",
+            "properties": {
+                "display_text": {
+                    "type": "string",
+                    "description": "Conversational text to show the user.",
+                },
+                "ui_type": {
+                    "type": "string",
+                    "enum": ["none", "selection", "data_table", "process_map"],
+                    "description": "Type of UI component to render.",
+                },
+                "ui": {
+                    "type": "object",
+                    "description": (
+                        "UI payload for the selected ui_type. Use: "
+                        "selection -> {question, options, multi_select}; "
+                        "data_table -> {title, columns, min_rows, starter_rows, input_modes, summary_prompt}; "
+                        "process_map -> {title, required_fields, edge_types, min_steps, seed_nodes}."
+                    ),
+                    "additionalProperties": True,
+                },
+                "phase_transition": {
+                    "type": "string",
+                    "enum": [
+                        "goal_understanding",
+                        "agent_configuration",
+                        "blueprint_design",
+                        "complete",
+                    ],
+                    "description": "Phase transition, if needed.",
+                },
+            },
+            "required": ["display_text", "ui_type"],
+        },
+    }
+
+
+def _extract_tool_input(response: Any, tool_name: str) -> dict[str, Any] | None:
+    if not response or not hasattr(response, "content"):
+        return None
+    for block in response.content:
+        if isinstance(block, dict):
+            if block.get("type") == "tool_use" and block.get("name") == tool_name:
+                return block.get("input")
+            continue
+        block_type = getattr(block, "type", None)
+        if block_type == "tool_use" and getattr(block, "name", None) == tool_name:
+            return getattr(block, "input", None)
+    return None
+
+
+def _ui_payload_from_tool_input(tool_input: dict[str, Any]) -> dict[str, Any]:
+    ui_payload = tool_input.get("ui")
+    if isinstance(ui_payload, dict):
+        return ui_payload
+    fallback = {
+        key: value
+        for key, value in tool_input.items()
+        if key not in {"display_text", "ui_type", "phase_transition"}
+    }
+    return fallback if fallback else {}
+
+
+def _build_ui_component(
+    payload: StructuredOutputPayload,
+    tool_input: dict[str, Any],
+) -> UIComponent:
+    if payload.ui_type == "none":
+        return NoUIComponent()
+
+    ui_payload = _ui_payload_from_tool_input(tool_input)
+
+    if payload.ui_type == "selection":
+        raw_options = ui_payload.get("options") or []
+        question = _compact_text(ui_payload.get("question"), 160)
+        if not question:
+            question = _compact_text(payload.display_text, 160)
+        if not question:
+            raise ValueError("Selection question is missing or too long")
+
+        cleaned_options: list[SelectionOption] = []
+        for option in raw_options:
+            if not isinstance(option, dict):
+                continue
+            label = _compact_text(option.get("label"), 80)
+            if not label or _is_internal_option(label):
+                continue
+            description = _compact_text(option.get("description"), 140) or None
+            option_id = option.get("id")
+            if not isinstance(option_id, str) or not option_id.strip():
+                option_id = _slugify(label)
+            cleaned_options.append(
+                SelectionOption(
+                    id=InputSanitizer.sanitize_name(option_id),
+                    label=label,
+                    description=description,
+                    requires_input=bool(option.get("requires_input")),
+                )
+            )
+
+        return SelectionUIComponent(
+            question=question,
+            options=cleaned_options,
+            multi_select=bool(ui_payload.get("multi_select")),
+        )
+
+    if payload.ui_type == "data_table":
+        title = _compact_text(ui_payload.get("title"), 120) or "Data Table"
+        return DataTableUIComponent(
+            title=title,
+            columns=ui_payload.get("columns") or [],
+            min_rows=ui_payload.get("min_rows", 3),
+            starter_rows=ui_payload.get("starter_rows", 3),
+            input_modes=ui_payload.get("input_modes") or ["paste", "inline"],
+            summary_prompt=_compact_text(ui_payload.get("summary_prompt"), 240) or None,
+        )
+
+    if payload.ui_type == "process_map":
+        title = _compact_text(ui_payload.get("title"), 120) or "Process Map"
+        return ProcessMapUIComponent(
+            title=title,
+            required_fields=ui_payload.get("required_fields") or ["step_name", "owner", "outcome"],
+            edge_types=ui_payload.get("edge_types") or ["sequence", "approval", "parallel"],
+            min_steps=ui_payload.get("min_steps", 3),
+            seed_nodes=ui_payload.get("seed_nodes") or [],
+        )
+
+    return NoUIComponent()
+
+
+def _structured_output_system_prompt() -> str:
+    return (
+        "You are a formatter that converts Clara assistant replies into structured JSON.\n"
+        "You must call the structured_response tool with the fields described below.\n"
+        "Rules:\n"
+        "1) Use ui_type='selection' only if the assistant is explicitly asking the user to choose.\n"
+        "2) If the assistant asks a question and then provides example options (e.g., 'For example...'),\n"
+        "   treat those options as selectable choices and set ui_type='selection'.\n"
+        "3) Default multi_select=true when the question implies multiple answers\n"
+        "   (e.g., 'what key information', 'which areas', 'what topics', 'are you focused on').\n"
+        "4) If you set a selection UI, keep display_text and ui.question short (<= 160 chars) and remove option lists from display_text.\n"
+        "5) Informational lists stay in display_text with ui_type='none'.\n"
+        "6) Use 'data_table' for bulk lists and 'process_map' for workflows.\n"
+        "7) Always include an 'Other' option for selections.\n"
+        "8) Put the UI payload inside the 'ui' field when ui_type is not 'none'.\n"
+        "9) Never use internal checklist labels (Project Context, Subject Matter Experts, Key Information Needs, Output Requirements, Constraints) as options.\n"
+    )
+
+
+def _structured_output_user_prompt(
+    message: str,
+    phase: str | None,
+    flow: str | None,
+) -> str:
+    context_parts = []
+    if phase:
+        context_parts.append(f"phase={phase}")
+    if flow:
+        context_parts.append(f"flow={flow}")
+    context = ", ".join(context_parts)
+    context_line = f"Context: {context}\n" if context else ""
+    return f"{context_line}Assistant reply:\n{message}"
+
+
+class StructuredOutputParser:
+    """Parse assistant output into structured responses via tool-forced JSON."""
+
+    def __init__(self, model: str | None = None) -> None:
+        self.model = model or settings.structured_output_model
+        self._client: anthropic.AsyncAnthropic | None = None
+
+    def is_available(self) -> bool:
+        if not self.model:
+            return False
+        return bool(settings.anthropic_api_key or os.getenv("ANTHROPIC_API_KEY"))
+
+    async def parse(
+        self,
+        message: str,
+        phase: str | None = None,
+        flow: str | None = None,
+    ) -> DesignAssistantResponse | None:
+        if not self.is_available():
+            return None
+
+        sanitized = InputSanitizer.sanitize_message(message)
+        if not sanitized:
+            return None
+
+        model_id = STRUCTURED_OUTPUT_MODEL_MAP.get(self.model, self.model)
+        try:
+            if not self._client:
+                if settings.anthropic_api_key:
+                    self._client = anthropic.AsyncAnthropic(
+                        api_key=settings.anthropic_api_key
+                    )
+                else:
+                    self._client = anthropic.AsyncAnthropic()
+
+            response = await self._client.messages.create(
+                model=model_id,
+                max_tokens=800,
+                system=_structured_output_system_prompt(),
+                messages=[
+                    {
+                        "role": "user",
+                        "content": _structured_output_user_prompt(
+                            message=sanitized, phase=phase, flow=flow
+                        ),
+                    }
+                ],
+                tools=[_structured_output_tool_definition()],
+                tool_choice={"type": "tool", "name": STRUCTURED_OUTPUT_TOOL_NAME},
+            )
+
+            tool_input = _extract_tool_input(response, STRUCTURED_OUTPUT_TOOL_NAME)
+            if not isinstance(tool_input, dict):
+                return None
+
+            try:
+                payload = StructuredOutputPayload.model_validate(tool_input)
+            except ValidationError as exc:
+                logger.warning("Structured output payload invalid: %s", exc)
+                return None
+
+            try:
+                ui_component = _build_ui_component(payload, tool_input)
+            except ValidationError as exc:
+                logger.warning("Structured output UI invalid: %s", exc)
+                ui_component = NoUIComponent()
+
+            return DesignAssistantResponse(
+                display_text=payload.display_text,
+                ui_component=ui_component,
+                phase_transition=payload.phase_transition,
+            )
+        except Exception as exc:  # pragma: no cover - network failure fallback
+            logger.warning("Structured output parse failed: %s", exc)
+            return None
diff --git a/src/backend/clara/agents/tools.py b/src/backend/clara/agents/tools.py
index 2a10bab..e27bd80 100644
--- a/src/backend/clara/agents/tools.py
+++ b/src/backend/clara/agents/tools.py
@@ -110,6 +110,69 @@ def cleanup_stale_sessions() -> int:
     return len(stale_ids)
 
 
+def _safe_int(value: Any, default: int) -> int:
+    """Safely convert values to int with a fallback."""
+    try:
+        return int(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def sanitize_ask_options(options: list[dict[str, Any]] | Any) -> list[dict[str, Any]]:
+    """Normalize option payloads for selection-style UIs."""
+    if not isinstance(options, list):
+        return []
+    sanitized: list[dict[str, Any]] = []
+    for option in options:
+        if not isinstance(option, dict):
+            continue
+        label = InputSanitizer.sanitize_name(option.get("label", ""))
+        if not label:
+            continue
+        option_id = InputSanitizer.sanitize_name(option.get("id") or label) or "option"
+        description = InputSanitizer.sanitize_description(option.get("description"))
+        requires_input = bool(option.get("requires_input"))
+        if label.strip().lower().startswith("other"):
+            requires_input = True
+        entry: dict[str, Any] = {
+            "id": option_id,
+            "label": label,
+        }
+        if description:
+            entry["description"] = description
+        if requires_input:
+            entry["requires_input"] = True
+        sanitized.append(entry)
+    return sanitized
+
+
+def ensure_other_option(options: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    """Append an Other option if missing, and mark it as requiring input."""
+    for option in options:
+        label = str(option.get("label", "")).strip().lower()
+        if label.startswith("other"):
+            option["requires_input"] = True
+            return options
+
+    other_id = "other"
+    existing_ids = {str(option.get("id")) for option in options}
+    if other_id in existing_ids:
+        suffix = 2
+        while f"other_{suffix}" in existing_ids:
+            suffix += 1
+        other_id = f"other_{suffix}"
+
+    return [
+        *options,
+        {
+            "id": other_id,
+            "label": "Other",
+            "description": "Something else",
+            "requires_input": True,
+        },
+    ]
+
+
 # Tool input schemas as dicts (for the SDK)
 ProjectSchema = {
     "type": "object",
@@ -170,6 +233,36 @@ def cleanup_stale_sessions() -> int:
                     "id": {"type": "string"},
                     "label": {"type": "string"},
                     "description": {"type": "string"},
+                    "requires_input": {
+                        "type": "boolean",
+                        "description": "If true, require a free-text input when selected (e.g., Other).",
+                    },
+                },
+                "required": ["id", "label"],
+            },
+            "description": "Options to present to the user",
+        },
+        "multi_select": {"type": "boolean", "description": "Allow multiple selections"},
+    },
+    "required": ["question", "options"],
+}
+
+SelectionListSchema = {
+    "type": "object",
+    "properties": {
+        "question": {"type": "string", "description": "Question to ask the user"},
+        "options": {
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "id": {"type": "string"},
+                    "label": {"type": "string"},
+                    "description": {"type": "string"},
+                    "requires_input": {
+                        "type": "boolean",
+                        "description": "If true, require a free-text input when selected (e.g., Other).",
+                    },
                 },
                 "required": ["id", "label"],
             },
@@ -312,6 +405,65 @@ def cleanup_stale_sessions() -> int:
     "required": ["title", "prompt"],
 }
 
+DataTableSchema = {
+    "type": "object",
+    "properties": {
+        "title": {"type": "string", "description": "Title for the data table"},
+        "columns": {
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "name": {"type": "string"},
+                    "type": {
+                        "type": "string",
+                        "enum": ["text", "number", "enum", "date", "url"],
+                    },
+                    "required": {"type": "boolean"},
+                    "options": {
+                        "type": "array",
+                        "items": {"type": "string"},
+                        "description": "Allowed values if type is enum",
+                    },
+                },
+                "required": ["name", "type"],
+            },
+        },
+        "min_rows": {"type": "integer", "description": "Minimum number of rows"},
+        "starter_rows": {"type": "integer", "description": "Rows to prefill"},
+        "input_modes": {
+            "type": "array",
+            "items": {"type": "string", "enum": ["paste", "inline", "import"]},
+        },
+        "summary_prompt": {"type": "string", "description": "Short prompt for summarizing the data"},
+    },
+    "required": ["title", "columns"],
+}
+
+ProcessMapSchema = {
+    "type": "object",
+    "properties": {
+        "title": {"type": "string", "description": "Title for the process map"},
+        "required_fields": {
+            "type": "array",
+            "items": {"type": "string"},
+            "description": "Required fields per step",
+        },
+        "edge_types": {
+            "type": "array",
+            "items": {"type": "string"},
+            "description": "Allowed edge types between steps",
+        },
+        "min_steps": {"type": "integer", "description": "Minimum number of steps"},
+        "seed_nodes": {
+            "type": "array",
+            "items": {"type": "string"},
+            "description": "Seed step labels",
+        },
+    },
+    "required": ["title"],
+}
+
 GetAgentContextSchema = {
     "type": "object",
     "properties": {
@@ -438,10 +590,11 @@ async def ask_tool(args: dict) -> dict[str, Any]:
         logger.info(f"[{session_id}] Asking user: {args['question']}")
         # Store UI component in session state for frontend access
         state = get_session_state(session_id)
+        options = sanitize_ask_options(args.get("options", []))
         ui_component = {
             "type": "user_input_required",
             "question": args["question"],
-            "options": args["options"],
+            "options": options,
             "multi_select": args.get("multi_select", False),
         }
         state["pending_ui_component"] = ui_component
@@ -453,6 +606,110 @@ async def ask_tool(args: dict) -> dict[str, Any]:
             }]
         }
 
+    @tool("request_selection_list", "Present a checkbox/radio list for selection", SelectionListSchema)
+    async def request_selection_list_tool(args: dict) -> dict[str, Any]:
+        """Show a selection list UI to user."""
+        logger.info(f"[{session_id}] Requesting selection list: {args['question']}")
+        state = get_session_state(session_id)
+        options = ensure_other_option(sanitize_ask_options(args.get("options", [])))
+        ui_component = {
+            "type": "user_input_required",
+            "question": args["question"],
+            "options": options,
+            "multi_select": args.get("multi_select", False),
+        }
+        state["pending_ui_component"] = ui_component
+        return {
+            "content": [{
+                "type": "text",
+                "text": f"Presenting selection list: {args['question']}"
+            }]
+        }
+
+    @tool("request_data_table", "Request structured data table input", DataTableSchema)
+    async def request_data_table_tool(args: dict) -> dict[str, Any]:
+        """Show a data table UI to capture structured lists."""
+        state = get_session_state(session_id)
+
+        title = InputSanitizer.sanitize_name(args.get("title", "")) or "Data Table"
+        columns = []
+        for column in args.get("columns", []):
+            name = InputSanitizer.sanitize_name(column.get("name", "")) or "Item"
+            col_type = column.get("type", "text")
+            if col_type not in {"text", "number", "enum", "date", "url"}:
+                col_type = "text"
+            options = InputSanitizer.sanitize_array(column.get("options"))
+            columns.append(
+                {
+                    "name": name,
+                    "type": col_type,
+                    "required": bool(column.get("required")),
+                    "options": options or None,
+                }
+            )
+
+        max_rows = 50
+        min_rows = _safe_int(args.get("min_rows"), 1)
+        min_rows = min(max_rows, max(1, min_rows))
+        starter_rows = _safe_int(args.get("starter_rows"), min_rows)
+        starter_rows = min(max_rows, max(1, starter_rows), min_rows)
+        input_modes = InputSanitizer.sanitize_array(args.get("input_modes", ["paste", "inline"]))
+
+        ui_component = {
+            "type": "data_table",
+            "title": title,
+            "columns": columns,
+            "min_rows": min_rows,
+            "starter_rows": starter_rows,
+            "input_modes": input_modes or ["paste", "inline"],
+            "summary_prompt": InputSanitizer.sanitize_description(args.get("summary_prompt")),
+        }
+
+        state["pending_ui_component"] = ui_component
+
+        logger.info(f"[{session_id}] Requesting data table: {title}")
+        return {
+            "content": [{
+                "type": "text",
+                "text": f"Requesting data table: {title}"
+            }]
+        }
+
+    @tool("request_process_map", "Request a process map input", ProcessMapSchema)
+    async def request_process_map_tool(args: dict) -> dict[str, Any]:
+        """Show a process map UI to capture workflows."""
+        state = get_session_state(session_id)
+
+        title = InputSanitizer.sanitize_name(args.get("title", "")) or "Process Map"
+        required_fields = InputSanitizer.sanitize_array(
+            args.get("required_fields", ["step_name", "owner", "outcome"])
+        )
+        edge_types = InputSanitizer.sanitize_array(
+            args.get("edge_types", ["sequence", "approval", "parallel"])
+        )
+        seed_nodes = InputSanitizer.sanitize_array(args.get("seed_nodes", []))
+        min_steps = _safe_int(args.get("min_steps"), 1)
+        min_steps = min(min_steps, 20)
+
+        ui_component = {
+            "type": "process_map",
+            "title": title,
+            "required_fields": required_fields or ["step_name", "owner", "outcome"],
+            "edge_types": edge_types or ["sequence", "approval", "parallel"],
+            "min_steps": min_steps,
+            "seed_nodes": seed_nodes,
+        }
+
+        state["pending_ui_component"] = ui_component
+
+        logger.info(f"[{session_id}] Requesting process map: {title}")
+        return {
+            "content": [{
+                "type": "text",
+                "text": f"Requesting process map: {title}"
+            }]
+        }
+
     @tool("phase", "Transition to a different design phase", PhaseSchema)
     async def phase_tool(args: dict) -> dict[str, Any]:
         """Change the current design phase.
@@ -937,6 +1194,9 @@ async def get_agent_context_tool(args: dict) -> dict[str, Any]:
             entity_tool,
             agent_tool,
             ask_tool,
+            request_selection_list_tool,
+            request_data_table_tool,
+            request_process_map_tool,
             phase_tool,
             preview_tool,
             agent_summary_tool,
@@ -957,6 +1217,9 @@ async def get_agent_context_tool(args: dict) -> dict[str, Any]:
     "mcp__clara__entity",
     "mcp__clara__agent",
     "mcp__clara__ask",
+    "mcp__clara__request_selection_list",
+    "mcp__clara__request_data_table",
+    "mcp__clara__request_process_map",
     "mcp__clara__phase",
     "mcp__clara__preview",
     "mcp__clara__agent_summary",
diff --git a/src/backend/clara/config.py b/src/backend/clara/config.py
index b035d70..5691146 100644
--- a/src/backend/clara/config.py
+++ b/src/backend/clara/config.py
@@ -28,6 +28,8 @@ class Settings(BaseSettings):
     simulation_interviewer_model: str = "sonnet"
     simulation_user_model: str = "haiku"
     web_search_model: str = "claude-sonnet-4-20250514"
+    router_model: str = "haiku"
+    structured_output_model: str = "haiku"
 
     # Anthropic API (uses ANTHROPIC_API_KEY env var by default)
     anthropic_api_key: str | None = None
diff --git a/src/backend/pyproject.toml b/src/backend/pyproject.toml
index 26cb5cd..706499c 100644
--- a/src/backend/pyproject.toml
+++ b/src/backend/pyproject.toml
@@ -19,6 +19,7 @@ dependencies = [
     "aiosqlite>=0.22.0",
     "claude-agent-sdk>=0.1.18",
     "anthropic>=0.75.0",
+    "instructor>=1.13.0",
 ]
 
 [project.optional-dependencies]
diff --git a/src/backend/tests/unit/test_structured_output.py b/src/backend/tests/unit/test_structured_output.py
new file mode 100644
index 0000000..3b7f1b6
--- /dev/null
+++ b/src/backend/tests/unit/test_structured_output.py
@@ -0,0 +1,281 @@
+"""Unit tests for structured output models used with Instructor."""
+
+import pytest
+from pydantic import ValidationError
+
+from clara.agents.structured_output import (
+    DataTableColumn,
+    DataTableUIComponent,
+    DesignAssistantResponse,
+    NoUIComponent,
+    ProcessMapUIComponent,
+    RouterDecisionModel,
+    SelectionListParams,
+    SelectionOption,
+    SelectionUIComponent,
+)
+
+
+class TestSelectionOption:
+    """Tests for SelectionOption model."""
+
+    def test_basic_option(self):
+        option = SelectionOption(id="test", label="Test Option")
+        assert option.id == "test"
+        assert option.label == "Test Option"
+        assert option.description is None
+        assert option.requires_input is False
+
+    def test_option_with_all_fields(self):
+        option = SelectionOption(
+            id="other",
+            label="Other",
+            description="Something else",
+            requires_input=True,
+        )
+        assert option.requires_input is True
+        assert option.description == "Something else"
+
+
+class TestSelectionUIComponent:
+    """Tests for SelectionUIComponent model."""
+
+    def test_basic_selection(self):
+        component = SelectionUIComponent(
+            question="Choose an option",
+            options=[
+                SelectionOption(id="a", label="Option A"),
+                SelectionOption(id="b", label="Option B"),
+            ],
+        )
+        assert component.type == "selection"
+        assert component.question == "Choose an option"
+        assert len(component.options) == 3
+        assert component.options[-1].label == "Other"
+        assert component.options[-1].requires_input is True
+        assert component.multi_select is False
+
+    def test_multi_select(self):
+        component = SelectionUIComponent(
+            question="Select all that apply",
+            options=[
+                SelectionOption(id="a", label="Option A"),
+                SelectionOption(id="b", label="Option B"),
+            ],
+            multi_select=True,
+        )
+        assert component.multi_select is True
+
+    def test_too_few_options_raises(self):
+        with pytest.raises(ValidationError) as exc_info:
+            SelectionUIComponent(
+                question="Choose",
+                options=[SelectionOption(id="a", label="Option A")],
+            )
+        assert "at least 2 options" in str(exc_info.value)
+
+    def test_too_many_options_trims(self):
+        options = [
+            SelectionOption(id=f"opt{i}", label=f"Option {i}")
+            for i in range(8)
+        ]
+        component = SelectionUIComponent(question="Choose", options=options)
+        assert len(component.options) == 7
+        assert component.options[-1].label == "Other"
+
+
+class TestDataTableColumn:
+    """Tests for DataTableColumn model."""
+
+    def test_basic_column(self):
+        column = DataTableColumn(name="Name")
+        assert column.name == "Name"
+        assert column.type == "text"
+        assert column.required is False
+
+    def test_enum_column(self):
+        column = DataTableColumn(
+            name="Priority",
+            type="enum",
+            required=True,
+            options=["Low", "Medium", "High"],
+        )
+        assert column.type == "enum"
+        assert column.options == ["Low", "Medium", "High"]
+
+
+class TestDataTableUIComponent:
+    """Tests for DataTableUIComponent model."""
+
+    def test_basic_table(self):
+        component = DataTableUIComponent(
+            title="Stakeholder List",
+            columns=[
+                DataTableColumn(name="Name", required=True),
+                DataTableColumn(name="Role"),
+            ],
+        )
+        assert component.type == "data_table"
+        assert component.title == "Stakeholder List"
+        assert len(component.columns) == 2
+        assert component.min_rows == 3
+        assert component.starter_rows == 3
+
+
+class TestProcessMapUIComponent:
+    """Tests for ProcessMapUIComponent model."""
+
+    def test_basic_process_map(self):
+        component = ProcessMapUIComponent(title="Approval Workflow")
+        assert component.type == "process_map"
+        assert component.title == "Approval Workflow"
+        assert "step_name" in component.required_fields
+        assert "sequence" in component.edge_types
+        assert component.min_steps == 3
+
+
+class TestRouterDecisionModel:
+    """Tests for RouterDecisionModel used by Instructor."""
+
+    def test_chat_decision(self):
+        decision = RouterDecisionModel(
+            action="chat",
+            confidence=0.8,
+            rationale="Normal conversation",
+        )
+        assert decision.action == "chat"
+        assert decision.tool_name is None
+        assert decision.confidence == 0.8
+
+    def test_tool_decision(self):
+        decision = RouterDecisionModel(
+            action="tool",
+            tool_name="request_data_table",
+            confidence=0.9,
+            rationale="User wants to list stakeholders",
+        )
+        assert decision.action == "tool"
+        assert decision.tool_name == "request_data_table"
+
+    def test_tool_decision_requires_tool_name(self):
+        with pytest.raises(ValidationError) as exc_info:
+            RouterDecisionModel(
+                action="tool",
+                confidence=0.8,
+                rationale="Missing tool name",
+            )
+        assert "tool_name is required" in str(exc_info.value)
+
+    def test_clarify_decision(self):
+        decision = RouterDecisionModel(
+            action="clarify",
+            confidence=0.5,
+            rationale="Need more info",
+            clarifying_question="How many items?",
+        )
+        assert decision.action == "clarify"
+        assert decision.clarifying_question == "How many items?"
+
+    def test_clarify_requires_question(self):
+        with pytest.raises(ValidationError) as exc_info:
+            RouterDecisionModel(
+                action="clarify",
+                confidence=0.5,
+                rationale="Need clarification",
+            )
+        assert "clarifying_question is required" in str(exc_info.value)
+
+    def test_confidence_bounds(self):
+        with pytest.raises(ValidationError):
+            RouterDecisionModel(
+                action="chat",
+                confidence=1.5,
+                rationale="Too confident",
+            )
+
+        with pytest.raises(ValidationError):
+            RouterDecisionModel(
+                action="chat",
+                confidence=-0.1,
+                rationale="Negative confidence",
+            )
+
+
+class TestDesignAssistantResponse:
+    """Tests for DesignAssistantResponse model."""
+
+    def test_chat_only_response(self):
+        response = DesignAssistantResponse(
+            display_text="Hello! How can I help you today?",
+        )
+        assert response.display_text == "Hello! How can I help you today?"
+        assert isinstance(response.ui_component, NoUIComponent)
+        assert response.phase_transition is None
+
+    def test_response_with_selection(self):
+        response = DesignAssistantResponse(
+            display_text="What type of project?",
+            ui_component=SelectionUIComponent(
+                question="Select project type",
+                options=[
+                    SelectionOption(id="ma", label="M&A"),
+                    SelectionOption(id="migration", label="Migration"),
+                ],
+            ),
+        )
+        assert isinstance(response.ui_component, SelectionUIComponent)
+        assert response.ui_component.question == "Select project type"
+
+    def test_response_with_phase_transition(self):
+        response = DesignAssistantResponse(
+            display_text="Let's move on to configuration.",
+            phase_transition="agent_configuration",
+        )
+        assert response.phase_transition == "agent_configuration"
+
+    def test_empty_display_text_raises(self):
+        with pytest.raises(ValidationError) as exc_info:
+            DesignAssistantResponse(display_text="")
+        assert "display_text cannot be empty" in str(exc_info.value)
+
+    def test_whitespace_only_display_text_raises(self):
+        with pytest.raises(ValidationError) as exc_info:
+            DesignAssistantResponse(display_text="   \n\t  ")
+        assert "display_text cannot be empty" in str(exc_info.value)
+
+
+class TestSelectionListParams:
+    """Tests for SelectionListParams model."""
+
+    def test_auto_adds_other_option(self):
+        params = SelectionListParams(
+            question="Choose a project type",
+            options=[
+                SelectionOption(id="ma", label="M&A"),
+                SelectionOption(id="migration", label="Migration"),
+            ],
+        )
+        # Should auto-add "Other" option
+        assert len(params.options) == 3
+        other_option = params.options[-1]
+        assert other_option.id == "other"
+        assert other_option.label == "Other"
+        assert other_option.requires_input is True
+
+    def test_does_not_duplicate_other(self):
+        params = SelectionListParams(
+            question="Choose",
+            options=[
+                SelectionOption(id="a", label="Option A"),
+                SelectionOption(id="other", label="Other", requires_input=True),
+            ],
+        )
+        # Should not add another "Other"
+        assert len(params.options) == 2
+
+    def test_too_few_options_raises(self):
+        with pytest.raises(ValidationError):
+            SelectionListParams(
+                question="Choose",
+                options=[SelectionOption(id="a", label="Option A")],
+            )
diff --git a/src/backend/uv.lock b/src/backend/uv.lock
index eae18af..b27e9ea 100644
--- a/src/backend/uv.lock
+++ b/src/backend/uv.lock
@@ -2,6 +2,113 @@ version = 1
 revision = 1
 requires-python = ">=3.12"
 
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+name = "aiohttp"
+version = "3.13.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohappyeyeballs" },
+    { name = "aiosignal" },
+    { name = "attrs" },
+    { name = "frozenlist" },
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diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json
index 38fd710..0272b23 100644
--- a/src/frontend/package-lock.json
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+      "dependencies": {
+        "@types/unist": "^3.0.0",
+        "vfile-message": "^4.0.0"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/unified"
+      }
+    },
+    "node_modules/vfile-message": {
+      "version": "4.0.3",
+      "resolved": "https://registry.npmjs.org/vfile-message/-/vfile-message-4.0.3.tgz",
+      "integrity": "sha512-QTHzsGd1EhbZs4AsQ20JX1rC3cOlt/IWJruk893DfLRr57lcnOeMaWG4K0JrRta4mIJZKth2Au3mM3u03/JWKw==",
+      "license": "MIT",
+      "dependencies": {
+        "@types/unist": "^3.0.0",
+        "unist-util-stringify-position": "^4.0.0"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/unified"
+      }
+    },
     "node_modules/vite": {
       "version": "5.4.21",
       "resolved": "https://registry.npmjs.org/vite/-/vite-5.4.21.tgz",
@@ -5987,6 +7471,16 @@
       "funding": {
         "url": "https://github.com/sponsors/sindresorhus"
       }
+    },
+    "node_modules/zwitch": {
+      "version": "2.0.4",
+      "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz",
+      "integrity": "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==",
+      "license": "MIT",
+      "funding": {
+        "type": "github",
+        "url": "https://github.com/sponsors/wooorm"
+      }
     }
   }
 }
diff --git a/src/frontend/package.json b/src/frontend/package.json
index 6da081e..41100a2 100644
--- a/src/frontend/package.json
+++ b/src/frontend/package.json
@@ -12,12 +12,16 @@
     "test:run": "vitest run"
   },
   "dependencies": {
-    "react": "^18.3.1",
-    "react-dom": "^18.3.1",
-    "react-router-dom": "^6.28.0",
     "@tanstack/react-query": "^5.60.0",
+    "boring-avatars": "^2.0.4",
+    "clsx": "^2.1.1",
     "date-fns": "^4.1.0",
-    "clsx": "^2.1.1"
+    "react": "^18.3.1",
+    "react-dom": "^18.3.1",
+    "react-markdown": "^10.1.0",
+    "remark-breaks": "^4.0.0",
+    "remark-gfm": "^4.0.0",
+    "react-router-dom": "^6.28.0"
   },
   "devDependencies": {
     "@eslint/js": "^9.13.0",
diff --git a/src/frontend/src/api/design-sessions.ts b/src/frontend/src/api/design-sessions.ts
index e9a57f0..4465d80 100644
--- a/src/frontend/src/api/design-sessions.ts
+++ b/src/frontend/src/api/design-sessions.ts
@@ -137,10 +137,22 @@ export async function* streamMessage(
   const reader = response.body.getReader();
   const decoder = new TextDecoder();
   let buffer = '';
+  let receivedEvent = false;
 
   try {
     while (true) {
-      const { done, value } = await reader.read();
+      let result: ReadableStreamReadResult<Uint8Array>;
+      try {
+        result = await reader.read();
+      } catch (err) {
+        if (receivedEvent) {
+          console.warn('Stream read error after events:', err);
+          break;
+        }
+        throw err;
+      }
+
+      const { done, value } = result;
 
       if (done) break;
 
@@ -155,6 +167,7 @@ export async function* streamMessage(
 
         const event = parseSSEEvent(eventText);
         if (event) {
+          receivedEvent = true;
           yield event;
         }
       }
@@ -164,6 +177,7 @@ export async function* streamMessage(
     if (buffer.trim()) {
       const event = parseSSEEvent(buffer);
       if (event) {
+        receivedEvent = true;
         yield event;
       }
     }
diff --git a/src/frontend/src/api/simulation-sessions.ts b/src/frontend/src/api/simulation-sessions.ts
index 9d0f61e..dfc387f 100644
--- a/src/frontend/src/api/simulation-sessions.ts
+++ b/src/frontend/src/api/simulation-sessions.ts
@@ -207,6 +207,8 @@ export interface AutoSimulationEvent {
   message?: string;
   role?: string;
   turns?: number;
+  name?: string;
+  value?: unknown;
 }
 
 /**
diff --git a/src/frontend/src/components/design-assistant/ChatMessage.tsx b/src/frontend/src/components/design-assistant/ChatMessage.tsx
index df72c5a..d41218b 100644
--- a/src/frontend/src/components/design-assistant/ChatMessage.tsx
+++ b/src/frontend/src/components/design-assistant/ChatMessage.tsx
@@ -3,23 +3,54 @@
  */
 
 import { useMemo } from 'react';
+import type { ReactNode } from 'react';
 import clsx from 'clsx';
-import type { ChatMessage as ChatMessageType, AskUIComponent, AgentConfiguredUIComponent, PromptEditorUIComponent, UIComponent } from '../../types/design-session';
+import ReactMarkdown from 'react-markdown';
+import remarkGfm from 'remark-gfm';
+import remarkBreaks from 'remark-breaks';
+import type {
+  ChatMessage as ChatMessageType,
+  AskUIComponent,
+  AgentConfiguredUIComponent,
+  PromptEditorUIComponent,
+  DataTableUIComponent,
+  ProcessMapUIComponent,
+  DataTableSubmission,
+  ProcessMapSubmission,
+  UIComponent,
+} from '../../types/design-session';
 import { parseUIComponent, stripUIComponentMarkers } from '../../types/design-session';
 import { OptionCards } from './OptionCards';
 import { AgentConfiguredCard } from './AgentConfiguredCard';
 import { PromptEditor } from './PromptEditor';
+import { DataTableCapture } from './DataTableCapture';
+import { ProcessMapBuilder } from './ProcessMapBuilder';
 
 interface ChatMessageProps {
   message: ChatMessageType;
   onOptionSelect?: (optionId: string) => void;
+  onQuickConfirm?: (answer: 'Yes' | 'No') => void;
+  isLastAssistantMessage?: boolean;
   /** Callback when user saves an edited prompt */
   onPromptSave?: (editedPrompt: string) => void;
+  /** Callback when user submits a data table */
+  onTableSubmit?: (payload: DataTableSubmission) => void;
+  /** Callback when user submits a process map */
+  onProcessMapSubmit?: (payload: ProcessMapSubmission) => void;
   /** UI component from CUSTOM event - takes precedence over text parsing */
   externalUIComponent?: UIComponent | null;
 }
 
-export function ChatMessage({ message, onOptionSelect, onPromptSave, externalUIComponent }: ChatMessageProps) {
+export function ChatMessage({
+  message,
+  onOptionSelect,
+  onQuickConfirm,
+  isLastAssistantMessage = false,
+  onPromptSave,
+  onTableSubmit,
+  onProcessMapSubmit,
+  externalUIComponent,
+}: ChatMessageProps) {
   const isUser = message.role === 'user';
 
   // Parse any UI components from the message
@@ -43,6 +74,81 @@ export function ChatMessage({ message, onOptionSelect, onPromptSave, externalUIC
     return { textContent: text, uiComponent: component };
   }, [message.content, isUser, externalUIComponent]);
 
+  const showQuickConfirm =
+    !isUser &&
+    isLastAssistantMessage &&
+    !message.isStreaming &&
+    !uiComponent &&
+    !!onQuickConfirm;
+
+  const shouldShowQuickConfirm = useMemo(() => {
+    if (!showQuickConfirm) return false;
+    const text = textContent?.trim();
+    if (!text) return false;
+    const normalized = text.replace(/\s+/g, ' ').trim();
+    const patterns = [
+      /\bis this correct\?\s*$/i,
+      /\bis that correct\?\s*$/i,
+      /\bis this accurate\?\s*$/i,
+      /\bis that accurate\?\s*$/i,
+      /\bis this an accurate summary\?\s*$/i,
+      /\bdoes this capture.*\?\s*$/i,
+      /\bdoes that capture.*\?\s*$/i,
+      /\bdoes this look right\?\s*$/i,
+      /\bdoes that look right\?\s*$/i,
+      /\bsound right\?\s*$/i,
+    ];
+    return patterns.some((pattern) => pattern.test(normalized));
+  }, [showQuickConfirm, textContent]);
+
+  const markdownComponents = useMemo(() => {
+    const linkClass = isUser
+      ? 'text-white underline'
+      : 'text-blue-600 underline hover:text-blue-700';
+    const inlineCodeClass = isUser
+      ? 'rounded bg-blue-500/40 px-1 py-0.5 text-xs'
+      : 'rounded bg-gray-200 px-1 py-0.5 text-xs';
+    const blockCodeClass = isUser
+      ? 'block rounded bg-blue-900/40 p-3 text-xs text-white'
+      : 'block rounded bg-gray-900 p-3 text-xs text-gray-100';
+    const blockquoteClass = isUser
+      ? 'border-l-2 border-white/40 pl-3 text-white/90'
+      : 'border-l-2 border-gray-300 pl-3 text-gray-600';
+
+    return {
+      p: ({ children }: { children: ReactNode }) => (
+        <p className="mb-2 last:mb-0">{children}</p>
+      ),
+      ul: ({ children }: { children: ReactNode }) => (
+        <ul className="mb-2 ml-5 list-disc space-y-1 last:mb-0">
+          {children}
+        </ul>
+      ),
+      ol: ({ children }: { children: ReactNode }) => (
+        <ol className="mb-2 ml-5 list-decimal space-y-1 last:mb-0">
+          {children}
+        </ol>
+      ),
+      li: ({ children }: { children: ReactNode }) => (
+        <li className="leading-relaxed">{children}</li>
+      ),
+      a: ({ children, href }: { children: ReactNode; href?: string }) => (
+        <a href={href} target="_blank" rel="noreferrer" className={linkClass}>
+          {children}
+        </a>
+      ),
+      blockquote: ({ children }: { children: ReactNode }) => (
+        <blockquote className={blockquoteClass}>{children}</blockquote>
+      ),
+      code: ({ inline, children }: { inline?: boolean; children: ReactNode }) =>
+        inline ? (
+          <code className={inlineCodeClass}>{children}</code>
+        ) : (
+          <code className={blockCodeClass}>{children}</code>
+        ),
+    };
+  }, [isUser]);
+
   return (
     <div
       className={clsx('flex w-full', isUser ? 'justify-end' : 'justify-start')}
@@ -56,30 +162,15 @@ export function ChatMessage({ message, onOptionSelect, onPromptSave, externalUIC
           message.isStreaming && 'animate-pulse'
         )}
       >
-        {/* Text content with markdown-like formatting */}
+        {/* Text content rendered as markdown */}
         {textContent && (
-          <div className="whitespace-pre-wrap text-sm leading-relaxed">
-            {textContent.split('\n').map((line, i) => {
-              // Simple markdown parsing
-              if (line.startsWith('**') && line.endsWith('**')) {
-                return (
-                  <p key={i} className="font-semibold mt-2 first:mt-0">
-                    {line.slice(2, -2)}
-                  </p>
-                );
-              }
-              if (line.startsWith('- ')) {
-                return (
-                  <p key={i} className="ml-4 before:content-['•'] before:mr-2">
-                    {line.slice(2)}
-                  </p>
-                );
-              }
-              if (line.trim() === '') {
-                return <br key={i} />;
-              }
-              return <span key={i}>{line}</span>;
-            })}
+          <div className="text-sm leading-relaxed">
+            <ReactMarkdown
+              remarkPlugins={[remarkGfm, remarkBreaks]}
+              components={markdownComponents}
+            >
+              {textContent}
+            </ReactMarkdown>
           </div>
         )}
 
@@ -98,6 +189,26 @@ export function ChatMessage({ message, onOptionSelect, onPromptSave, externalUIC
           </div>
         )}
 
+        {/* Quick confirmation buttons */}
+        {shouldShowQuickConfirm && (
+          <div className="mt-3 flex gap-2">
+            <button
+              type="button"
+              onClick={() => onQuickConfirm?.('Yes')}
+              className="rounded-md bg-blue-600 px-3 py-1 text-xs font-medium text-white hover:bg-blue-700"
+            >
+              Yes
+            </button>
+            <button
+              type="button"
+              onClick={() => onQuickConfirm?.('No')}
+              className="rounded-md border border-gray-300 px-3 py-1 text-xs font-medium text-gray-700 hover:bg-gray-50"
+            >
+              No
+            </button>
+          </div>
+        )}
+
         {/* Interactive UI component - Ask tool (user_input_required) */}
         {uiComponent && uiComponent.type === 'user_input_required' && onOptionSelect && (
           <div className="mt-4">
@@ -128,6 +239,35 @@ export function ChatMessage({ message, onOptionSelect, onPromptSave, externalUIC
             />
           </div>
         )}
+
+        {/* Data table capture (bulk entry) */}
+        {uiComponent && uiComponent.type === 'data_table' && onTableSubmit && (
+          <div className="mt-4">
+            <DataTableCapture
+              title={(uiComponent as DataTableUIComponent).title}
+              columns={(uiComponent as DataTableUIComponent).columns}
+              minRows={(uiComponent as DataTableUIComponent).min_rows}
+              starterRows={(uiComponent as DataTableUIComponent).starter_rows}
+              inputModes={(uiComponent as DataTableUIComponent).input_modes}
+              summaryPrompt={(uiComponent as DataTableUIComponent).summary_prompt}
+              onSubmit={onTableSubmit}
+            />
+          </div>
+        )}
+
+        {/* Process map builder */}
+        {uiComponent && uiComponent.type === 'process_map' && onProcessMapSubmit && (
+          <div className="mt-4">
+            <ProcessMapBuilder
+              title={(uiComponent as ProcessMapUIComponent).title}
+              requiredFields={(uiComponent as ProcessMapUIComponent).required_fields}
+              edgeTypes={(uiComponent as ProcessMapUIComponent).edge_types}
+              minSteps={(uiComponent as ProcessMapUIComponent).min_steps}
+              seedNodes={(uiComponent as ProcessMapUIComponent).seed_nodes}
+              onSubmit={onProcessMapSubmit}
+            />
+          </div>
+        )}
       </div>
     </div>
   );
diff --git a/src/frontend/src/components/design-assistant/DataTableCapture.tsx b/src/frontend/src/components/design-assistant/DataTableCapture.tsx
new file mode 100644
index 0000000..7d99f02
--- /dev/null
+++ b/src/frontend/src/components/design-assistant/DataTableCapture.tsx
@@ -0,0 +1,262 @@
+/**
+ * Data table capture component for bulk structured input.
+ */
+
+import { useMemo, useState } from 'react';
+import type {
+  DataTableColumn,
+  DataTableRow,
+  DataTableSubmission,
+} from '../../types/design-session';
+
+interface DataTableCaptureProps {
+  title: string;
+  columns: DataTableColumn[];
+  minRows?: number;
+  starterRows?: number;
+  inputModes?: Array<'paste' | 'inline' | 'import'>;
+  summaryPrompt?: string;
+  onSubmit: (payload: DataTableSubmission) => void;
+}
+
+const MAX_ROWS = 50;
+
+function buildEmptyRow(columns: DataTableColumn[]): DataTableRow {
+  return columns.reduce<DataTableRow>((row, column) => {
+    row[column.name] = '';
+    return row;
+  }, {});
+}
+
+function normalizeRows(rows: DataTableRow[], columns: DataTableColumn[]): DataTableRow[] {
+  return rows.filter((row) =>
+    columns.some((column) => (row[column.name] || '').trim().length > 0)
+  );
+}
+
+function parsePastedRows(text: string, columns: DataTableColumn[]): DataTableRow[] {
+  const lines = text
+    .split(/\r?\n/)
+    .map((line) => line.trim())
+    .filter(Boolean);
+
+  if (!lines.length) return [];
+
+  const delimiter = text.includes('\t') ? '\t' : ',';
+  const parsed = lines.map((line) => line.split(delimiter).map((cell) => cell.trim()));
+
+  return parsed.slice(0, MAX_ROWS).map((cells) => {
+    const row = buildEmptyRow(columns);
+    columns.forEach((column, idx) => {
+      row[column.name] = cells[idx] ?? '';
+    });
+    return row;
+  });
+}
+
+export function DataTableCapture({
+  title,
+  columns,
+  minRows = 1,
+  starterRows = 1,
+  inputModes = ['paste', 'inline'],
+  summaryPrompt,
+  onSubmit,
+}: DataTableCaptureProps) {
+  const initialRows = useMemo(
+    () =>
+      Array.from({ length: Math.min(MAX_ROWS, Math.max(1, starterRows)) }).map(() =>
+        buildEmptyRow(columns)
+      ),
+    [columns, starterRows]
+  );
+
+  const [rows, setRows] = useState<DataTableRow[]>(initialRows);
+  const [pasteValue, setPasteValue] = useState('');
+  const [error, setError] = useState<string | null>(null);
+
+  const handleCellChange = (rowIndex: number, columnName: string, value: string) => {
+    setRows((prev) =>
+      prev.map((row, idx) =>
+        idx === rowIndex ? { ...row, [columnName]: value } : row
+      )
+    );
+  };
+
+  const handleAddRow = () => {
+    setRows((prev) =>
+      prev.length >= MAX_ROWS ? prev : [...prev, buildEmptyRow(columns)]
+    );
+  };
+
+  const handleRemoveRow = (rowIndex: number) => {
+    setRows((prev) => prev.filter((_, idx) => idx !== rowIndex));
+  };
+
+  const handlePasteParse = () => {
+    const parsedRows = parsePastedRows(pasteValue, columns);
+    if (!parsedRows.length) {
+      setError('Paste data with one row per line.');
+      return;
+    }
+    setError(null);
+    setRows(parsedRows);
+  };
+
+  const handleSubmit = () => {
+    const cleanedRows = normalizeRows(rows, columns);
+    if (cleanedRows.length < minRows) {
+      setError(`Add at least ${minRows} rows.`);
+      return;
+    }
+
+    const missingRequired = columns
+      .filter((column) => column.required)
+      .some((column) =>
+        cleanedRows.some((row) => !(row[column.name] || '').trim())
+      );
+
+    if (missingRequired) {
+      setError('Fill all required fields before submitting.');
+      return;
+    }
+
+    setError(null);
+    onSubmit({
+      title,
+      columns,
+      rows: cleanedRows,
+    });
+  };
+
+  return (
+    <div className="mt-4 space-y-4">
+      <div>
+        <h3 className="text-sm font-semibold text-gray-900">{title}</h3>
+        {summaryPrompt && (
+          <p className="text-xs text-gray-500 mt-1">{summaryPrompt}</p>
+        )}
+      </div>
+
+      {inputModes.includes('paste') && (
+        <div className="rounded-lg border border-gray-200 bg-white p-3">
+          <label className="text-xs font-medium text-gray-600">
+            Paste rows from a spreadsheet
+          </label>
+          <textarea
+            value={pasteValue}
+            onChange={(event) => setPasteValue(event.target.value)}
+            rows={3}
+            placeholder="Paste rows here (tab or comma separated)"
+            className="mt-2 w-full rounded-md border border-gray-200 p-2 text-xs focus:border-blue-300 focus:outline-none"
+          />
+          <div className="mt-2 flex justify-end">
+            <button
+              type="button"
+              onClick={handlePasteParse}
+              className="rounded-md border border-blue-200 px-3 py-1 text-xs text-blue-600 hover:bg-blue-50"
+            >
+              Parse rows
+            </button>
+          </div>
+        </div>
+      )}
+
+      <div className="overflow-x-auto rounded-lg border border-gray-200 bg-white">
+        <table className="min-w-full text-xs">
+          <thead className="bg-gray-50 text-gray-500">
+            <tr>
+              {columns.map((column) => (
+                <th key={column.name} className="px-3 py-2 text-left font-medium">
+                  {column.name}
+                  {column.required && <span className="text-red-400"> *</span>}
+                </th>
+              ))}
+              <th className="px-3 py-2 text-right font-medium text-gray-400">Remove</th>
+            </tr>
+          </thead>
+          <tbody>
+            {rows.map((row, rowIndex) => (
+              <tr key={`${rowIndex}`} className="border-t border-gray-100">
+                {columns.map((column) => {
+                  const value = row[column.name] || '';
+                  if (column.type === 'enum' && column.options?.length) {
+                    return (
+                      <td key={column.name} className="px-3 py-2">
+                        <select
+                          value={value}
+                          onChange={(event) =>
+                            handleCellChange(rowIndex, column.name, event.target.value)
+                          }
+                          className="w-full rounded-md border border-gray-200 p-1 text-xs focus:border-blue-300 focus:outline-none"
+                        >
+                          <option value="">Select</option>
+                          {column.options.map((option) => (
+                            <option key={option} value={option}>
+                              {option}
+                            </option>
+                          ))}
+                        </select>
+                      </td>
+                    );
+                  }
+
+                  const inputType =
+                    column.type === 'number'
+                      ? 'number'
+                      : column.type === 'date'
+                        ? 'date'
+                        : column.type === 'url'
+                          ? 'url'
+                          : 'text';
+
+                  return (
+                    <td key={column.name} className="px-3 py-2">
+                      <input
+                        type={inputType}
+                        value={value}
+                        onChange={(event) =>
+                          handleCellChange(rowIndex, column.name, event.target.value)
+                        }
+                        className="w-full rounded-md border border-gray-200 p-1 text-xs focus:border-blue-300 focus:outline-none"
+                      />
+                    </td>
+                  );
+                })}
+                <td className="px-3 py-2 text-right">
+                  <button
+                    type="button"
+                    onClick={() => handleRemoveRow(rowIndex)}
+                    className="text-xs text-gray-400 hover:text-red-500"
+                  >
+                    Remove
+                  </button>
+                </td>
+              </tr>
+            ))}
+          </tbody>
+        </table>
+      </div>
+
+      <div className="flex flex-col gap-2 sm:flex-row sm:items-center sm:justify-between">
+        <button
+          type="button"
+          onClick={handleAddRow}
+          className="rounded-md border border-gray-200 px-3 py-1 text-xs text-gray-600 hover:bg-gray-50"
+        >
+          Add row
+        </button>
+        <div className="flex items-center gap-3">
+          {error && <span className="text-xs text-red-500">{error}</span>}
+          <button
+            type="button"
+            onClick={handleSubmit}
+            className="rounded-md bg-blue-600 px-4 py-1 text-xs text-white hover:bg-blue-700"
+          >
+            Submit table
+          </button>
+        </div>
+      </div>
+    </div>
+  );
+}
diff --git a/src/frontend/src/components/design-assistant/DebugPanel.tsx b/src/frontend/src/components/design-assistant/DebugPanel.tsx
index e6802e4..6af9630 100644
--- a/src/frontend/src/components/design-assistant/DebugPanel.tsx
+++ b/src/frontend/src/components/design-assistant/DebugPanel.tsx
@@ -2,7 +2,7 @@
  * Debug panel showing agent activity - tool calls, phase transitions, hydrations.
  */
 
-import { useState, useRef, useEffect } from 'react';
+import { useMemo, useState, useRef, useEffect } from 'react';
 import clsx from 'clsx';
 import type { DebugEvent, DebugEventType } from '../../types/design-session';
 
@@ -37,6 +37,25 @@ function formatTime(date: Date): string {
   });
 }
 
+function formatTimestamp(date: Date): string {
+  return date.toISOString();
+}
+
+function formatEventForClipboard(event: DebugEvent): string {
+  const header = `[${formatTimestamp(event.timestamp)}] ${event.type} | ${event.title}`;
+  if (!Object.keys(event.details).length) {
+    return header;
+  }
+  return `${header}\n${JSON.stringify(event.details, null, 2)}`;
+}
+
+function formatEventLog(events: DebugEvent[]): string {
+  if (!events.length) {
+    return '';
+  }
+  return events.map(formatEventForClipboard).join('\n\n');
+}
+
 function DebugEventItem({ event }: { event: DebugEvent }) {
   const [isExpanded, setIsExpanded] = useState(false);
   const style = eventTypeStyles[event.type];
@@ -96,6 +115,9 @@ function DebugEventItem({ event }: { event: DebugEvent }) {
 export function DebugPanel({ events, isOpen, onToggle }: DebugPanelProps) {
   const scrollRef = useRef<HTMLDivElement>(null);
   const [autoScroll, setAutoScroll] = useState(true);
+  const [showRaw, setShowRaw] = useState(false);
+  const [copyStatus, setCopyStatus] = useState<'idle' | 'copied' | 'error'>('idle');
+  const logText = useMemo(() => formatEventLog(events), [events]);
 
   // Auto-scroll to bottom when new events arrive
   useEffect(() => {
@@ -112,6 +134,22 @@ export function DebugPanel({ events, isOpen, onToggle }: DebugPanelProps) {
     setAutoScroll(isAtBottom);
   };
 
+  useEffect(() => {
+    if (copyStatus === 'idle') return;
+    const timeoutId = window.setTimeout(() => setCopyStatus('idle'), 1500);
+    return () => window.clearTimeout(timeoutId);
+  }, [copyStatus]);
+
+  const handleCopy = async () => {
+    if (!logText) return;
+    try {
+      await navigator.clipboard.writeText(logText);
+      setCopyStatus('copied');
+    } catch {
+      setCopyStatus('error');
+    }
+  };
+
   if (!isOpen) {
     return (
       <button
@@ -142,16 +180,53 @@ export function DebugPanel({ events, isOpen, onToggle }: DebugPanelProps) {
           <span className="text-sm font-semibold text-gray-900">Debug Panel</span>
           <span className="text-xs text-gray-500">({events.length} events)</span>
         </div>
-        <button
-          onClick={onToggle}
-          className="text-gray-400 hover:text-gray-600"
-        >
-          <svg className="w-5 h-5" fill="none" viewBox="0 0 24 24" stroke="currentColor">
-            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M6 18L18 6M6 6l12 12" />
-          </svg>
-        </button>
+        <div className="flex items-center gap-2">
+          <button
+            onClick={handleCopy}
+            disabled={!events.length}
+            className={clsx(
+              'text-xs px-2 py-1 rounded border',
+              events.length
+                ? 'border-gray-300 text-gray-600 hover:text-gray-900 hover:border-gray-400'
+                : 'border-gray-200 text-gray-300 cursor-not-allowed'
+            )}
+          >
+            {copyStatus === 'copied'
+              ? 'Copied'
+              : copyStatus === 'error'
+              ? 'Copy failed'
+              : 'Copy log'}
+          </button>
+          <button
+            onClick={() => setShowRaw((prev) => !prev)}
+            className="text-xs px-2 py-1 rounded border border-gray-300 text-gray-600 hover:text-gray-900 hover:border-gray-400"
+          >
+            {showRaw ? 'Hide raw' : 'Raw'}
+          </button>
+          <button
+            onClick={onToggle}
+            className="text-gray-400 hover:text-gray-600"
+          >
+            <svg className="w-5 h-5" fill="none" viewBox="0 0 24 24" stroke="currentColor">
+              <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M6 18L18 6M6 6l12 12" />
+            </svg>
+          </button>
+        </div>
       </div>
 
+      {showRaw && (
+        <div className="border-b border-gray-100 bg-gray-50 p-2">
+          <textarea
+            value={logText}
+            readOnly
+            spellCheck={false}
+            onFocus={(event) => event.currentTarget.select()}
+            className="w-full h-32 text-xs font-mono text-gray-900 bg-white border border-gray-200 rounded p-2"
+            placeholder="No events yet."
+          />
+        </div>
+      )}
+
       {/* Legend */}
       <div className="flex items-center gap-3 px-3 py-2 border-b border-gray-100 bg-gray-50">
         {Object.entries(eventTypeStyles).map(([type, style]) => (
diff --git a/src/frontend/src/components/design-assistant/OptionCards.tsx b/src/frontend/src/components/design-assistant/OptionCards.tsx
index 331c931..034c445 100644
--- a/src/frontend/src/components/design-assistant/OptionCards.tsx
+++ b/src/frontend/src/components/design-assistant/OptionCards.tsx
@@ -21,10 +21,29 @@ export function OptionCards({
 }: OptionCardsProps) {
   const [selectedIds, setSelectedIds] = useState<Set<string>>(new Set());
   const [isSubmitted, setIsSubmitted] = useState(false);
+  const [customInputs, setCustomInputs] = useState<Record<string, string>>({});
+
+  const optionRequiresInput = (option: AskOption) => {
+    if (typeof option.requires_input === 'boolean') {
+      return option.requires_input;
+    }
+    return option.label.trim().toLowerCase().startsWith('other');
+  };
+
+  const selectedOptions = options.filter((option) => selectedIds.has(option.id));
+  const optionsRequiringInput = selectedOptions.filter(optionRequiresInput);
+  const allRequiredFilled = optionsRequiringInput.every(
+    (option) => (customInputs[option.id] || '').trim().length > 0
+  );
+  const canSubmit = selectedIds.size > 0 && allRequiredFilled;
 
   const handleOptionClick = (optionId: string) => {
     if (isSubmitted) return;
 
+    const selectedOption = options.find((opt) => opt.id === optionId);
+    const requiresInput = selectedOption ? optionRequiresInput(selectedOption) : false;
+    const wasSelected = selectedIds.has(optionId);
+
     if (multiSelect) {
       setSelectedIds((prev) => {
         const next = new Set(prev);
@@ -35,21 +54,35 @@ export function OptionCards({
         }
         return next;
       });
+      if (wasSelected) {
+        setCustomInputs((prev) => {
+          const next = { ...prev };
+          delete next[optionId];
+          return next;
+        });
+      }
     } else {
+      setSelectedIds(new Set([optionId]));
+      if (requiresInput) {
+        return;
+      }
       // Single select - immediately submit with label (not ID)
       setIsSubmitted(true);
-      const selectedOption = options.find((opt) => opt.id === optionId);
       onSelect(selectedOption?.label || optionId);
+      setCustomInputs({});
     }
   };
 
   const handleSubmit = () => {
-    if (selectedIds.size === 0) return;
+    if (!canSubmit) return;
     setIsSubmitted(true);
-    // Send labels (not IDs) for multi-select
-    const selectedLabels = options
-      .filter((opt) => selectedIds.has(opt.id))
-      .map((opt) => opt.label);
+    const selectedLabels = selectedOptions.map((option) => {
+      if (optionRequiresInput(option)) {
+        const value = (customInputs[option.id] || '').trim();
+        return value ? `${option.label}: ${value}` : option.label;
+      }
+      return option.label;
+    });
     onSelect(selectedLabels.join(', '));
   };
 
@@ -118,14 +151,37 @@ export function OptionCards({
         })}
       </div>
 
+      {optionsRequiringInput.length > 0 && (
+        <div className="space-y-2">
+          {optionsRequiringInput.map((option) => (
+            <div key={option.id} className="space-y-1">
+              <label className="text-sm font-medium text-gray-700">
+                Please specify {option.label}
+              </label>
+              <input
+                type="text"
+                value={customInputs[option.id] || ''}
+                onChange={(event) => {
+                  const value = event.target.value;
+                  setCustomInputs((prev) => ({ ...prev, [option.id]: value }));
+                }}
+                disabled={isSubmitted}
+                className="w-full rounded-lg border border-gray-300 px-3 py-2 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500 disabled:bg-gray-100"
+                placeholder="Type your answer"
+              />
+            </div>
+          ))}
+        </div>
+      )}
+
       {/* Submit button for multi-select */}
-      {multiSelect && !isSubmitted && (
+      {(multiSelect || optionsRequiringInput.length > 0) && !isSubmitted && (
         <button
           onClick={handleSubmit}
-          disabled={selectedIds.size === 0}
+          disabled={!canSubmit}
           className={clsx(
             'w-full py-2 px-4 rounded-lg font-medium transition-colors',
-            selectedIds.size > 0
+            canSubmit
               ? 'bg-blue-600 text-white hover:bg-blue-700'
               : 'bg-gray-100 text-gray-400 cursor-not-allowed'
           )}
diff --git a/src/frontend/src/components/design-assistant/ProcessMapBuilder.tsx b/src/frontend/src/components/design-assistant/ProcessMapBuilder.tsx
new file mode 100644
index 0000000..f880d61
--- /dev/null
+++ b/src/frontend/src/components/design-assistant/ProcessMapBuilder.tsx
@@ -0,0 +1,186 @@
+/**
+ * Process map builder for capturing step-based workflows.
+ */
+
+import { useMemo, useState } from 'react';
+import type { ProcessMapStep, ProcessMapSubmission } from '../../types/design-session';
+
+interface ProcessMapBuilderProps {
+  title: string;
+  requiredFields: string[];
+  edgeTypes?: string[];
+  minSteps?: number;
+  seedNodes?: string[];
+  onSubmit: (payload: ProcessMapSubmission) => void;
+}
+
+function buildEmptyStep(): ProcessMapStep {
+  return {
+    step_name: '',
+    owner: '',
+    outcome: '',
+    edge_type: 'sequence',
+  };
+}
+
+function normalizeSteps(steps: ProcessMapStep[]): ProcessMapStep[] {
+  return steps.filter(
+    (step) =>
+      step.step_name.trim() ||
+      step.owner.trim() ||
+      step.outcome.trim()
+  );
+}
+
+export function ProcessMapBuilder({
+  title,
+  requiredFields,
+  edgeTypes = ['sequence', 'approval', 'parallel'],
+  minSteps = 1,
+  seedNodes = [],
+  onSubmit,
+}: ProcessMapBuilderProps) {
+  const initialSteps = useMemo(() => {
+    if (seedNodes.length) {
+      return seedNodes.map((node) => ({
+        ...buildEmptyStep(),
+        step_name: node,
+      }));
+    }
+    return [buildEmptyStep()];
+  }, [seedNodes]);
+
+  const [steps, setSteps] = useState<ProcessMapStep[]>(initialSteps);
+  const [error, setError] = useState<string | null>(null);
+
+  const handleStepChange = (index: number, field: keyof ProcessMapStep, value: string) => {
+    setSteps((prev) =>
+      prev.map((step, idx) => (idx === index ? { ...step, [field]: value } : step))
+    );
+  };
+
+  const handleAddStep = () => {
+    setSteps((prev) => [...prev, buildEmptyStep()]);
+  };
+
+  const handleRemoveStep = (index: number) => {
+    setSteps((prev) => prev.filter((_, idx) => idx !== index));
+  };
+
+  const handleSubmit = () => {
+    const cleanedSteps = normalizeSteps(steps);
+    if (cleanedSteps.length < minSteps) {
+      setError(`Add at least ${minSteps} steps.`);
+      return;
+    }
+
+    const missingRequired = cleanedSteps.some((step) =>
+      requiredFields.some((field) => {
+        const value = (step as Record<string, string>)[field] || '';
+        return !value.trim();
+      })
+    );
+
+    if (missingRequired) {
+      setError('Fill all required fields before submitting.');
+      return;
+    }
+
+    setError(null);
+    onSubmit({
+      title,
+      steps: cleanedSteps,
+    });
+  };
+
+  return (
+    <div className="mt-4 space-y-4">
+      <div>
+        <h3 className="text-sm font-semibold text-gray-900">{title}</h3>
+        <p className="text-xs text-gray-500 mt-1">
+          Capture each step with ownership and outcomes.
+        </p>
+      </div>
+
+      <div className="space-y-3">
+        {steps.map((step, index) => (
+          <div
+            key={index}
+            className="rounded-lg border border-gray-200 bg-white p-3 space-y-2"
+          >
+            <div className="flex items-center justify-between">
+              <span className="text-xs font-semibold text-gray-500">
+                Step {index + 1}
+              </span>
+              {steps.length > 1 && (
+                <button
+                  type="button"
+                  onClick={() => handleRemoveStep(index)}
+                  className="text-xs text-gray-400 hover:text-red-500"
+                >
+                  Remove
+                </button>
+              )}
+            </div>
+
+            <div className="grid grid-cols-1 gap-2 sm:grid-cols-3">
+              <input
+                value={step.step_name}
+                onChange={(event) => handleStepChange(index, 'step_name', event.target.value)}
+                placeholder="Step name"
+                className="rounded-md border border-gray-200 p-2 text-xs focus:border-blue-300 focus:outline-none"
+              />
+              <input
+                value={step.owner}
+                onChange={(event) => handleStepChange(index, 'owner', event.target.value)}
+                placeholder="Owner"
+                className="rounded-md border border-gray-200 p-2 text-xs focus:border-blue-300 focus:outline-none"
+              />
+              <input
+                value={step.outcome}
+                onChange={(event) => handleStepChange(index, 'outcome', event.target.value)}
+                placeholder="Outcome"
+                className="rounded-md border border-gray-200 p-2 text-xs focus:border-blue-300 focus:outline-none"
+              />
+            </div>
+
+            <div className="flex items-center gap-2">
+              <label className="text-xs text-gray-500">Edge type</label>
+              <select
+                value={step.edge_type || 'sequence'}
+                onChange={(event) => handleStepChange(index, 'edge_type', event.target.value)}
+                className="rounded-md border border-gray-200 p-1 text-xs focus:border-blue-300 focus:outline-none"
+              >
+                {edgeTypes.map((edge) => (
+                  <option key={edge} value={edge}>
+                    {edge}
+                  </option>
+                ))}
+              </select>
+            </div>
+          </div>
+        ))}
+      </div>
+
+      <div className="flex flex-col gap-2 sm:flex-row sm:items-center sm:justify-between">
+        <button
+          type="button"
+          onClick={handleAddStep}
+          className="rounded-md border border-gray-200 px-3 py-1 text-xs text-gray-600 hover:bg-gray-50"
+        >
+          Add step
+        </button>
+        <div className="flex items-center gap-3">
+          {error && <span className="text-xs text-red-500">{error}</span>}
+          <button
+            type="button"
+            onClick={handleSubmit}
+            className="rounded-md bg-blue-600 px-4 py-1 text-xs text-white hover:bg-blue-700"
+          >
+            Submit map
+          </button>
+        </div>
+      </div>
+    </div>
+  );
+}
diff --git a/src/frontend/src/hooks/useDesignSession.ts b/src/frontend/src/hooks/useDesignSession.ts
index a49cc3c..3187e06 100644
--- a/src/frontend/src/hooks/useDesignSession.ts
+++ b/src/frontend/src/hooks/useDesignSession.ts
@@ -170,6 +170,9 @@ export function useDesignSession({
 
     setIsLoading(true);
     setError(null);
+    clearDebugEvents();
+    setPendingUIComponent(null);
+    messageIdCounter.current = 0;
 
     try {
       const response = await designSessionsApi.create({
@@ -184,6 +187,7 @@ export function useDesignSession({
         // For addAgent, backend creates new session with copied blueprint state
         setSessionState(initialSessionState);
         setMessages([]);
+        messageIdCounter.current = 0;
       } else {
         // Existing session - load previous state
         const fullState = await designSessionsApi.getFullSession(response.session_id);
@@ -195,7 +199,7 @@ export function useDesignSession({
     } finally {
       setIsLoading(false);
     }
-  }, [projectId, addAgent, isConnected, restoreSessionState]);
+  }, [projectId, addAgent, isConnected, restoreSessionState, clearDebugEvents]);
 
   const disconnect = useCallback(async () => {
     if (!sessionId) return;
@@ -206,7 +210,11 @@ export function useDesignSession({
     setIsConnected(false);
     setMessages([]);
     setSessionState(null);
-  }, [sessionId]);
+    setPendingUIComponent(null);
+    setError(null);
+    clearDebugEvents();
+    messageIdCounter.current = 0;
+  }, [sessionId, clearDebugEvents]);
 
   const handleEvent = useCallback(
     (event: AGUIEvent, currentAssistantMessage: ChatMessage | null, prevPhase: DesignPhase | null) => {
@@ -321,6 +329,7 @@ export function useDesignSession({
     async (message: string) => {
       if (!sessionId || isStreaming) return;
 
+      setPendingUIComponent(null);
       setIsStreaming(true);
       setError(null);
 
diff --git a/src/frontend/src/pages/AutomatedSimulationPage.tsx b/src/frontend/src/pages/AutomatedSimulationPage.tsx
index 1e4c068..39ffe7b 100644
--- a/src/frontend/src/pages/AutomatedSimulationPage.tsx
+++ b/src/frontend/src/pages/AutomatedSimulationPage.tsx
@@ -17,6 +17,7 @@ import {
   CommunicationStyle,
   PersonaConfig,
 } from '../api/simulation-sessions';
+import type { UIComponent } from '../types/design-session';
 
 interface Message {
   id: string;
@@ -37,6 +38,15 @@ Guidelines:
 - Cover key topics: current processes, pain points, tools used, and desired improvements
 - Summarize key findings periodically`;
 
+function formatUISuggestion(value: UIComponent | undefined): string {
+  if (!value) {
+    return 'Suggested UI: details captured in a rich component.';
+  }
+  const typeLabel = value.type.replace(/_/g, ' ');
+  const title = 'title' in value && value.title ? ` (${value.title})` : '';
+  return `Suggested UI: ${typeLabel}${title}.`;
+}
+
 export function AutomatedSimulationPage() {
   const { projectId } = useParams<{ projectId: string }>();
   const [searchParams] = useSearchParams();
@@ -191,6 +201,18 @@ export function AutomatedSimulationPage() {
           );
         } else if (event.type === 'SIMULATION_COMPLETE') {
           setStatus('completed');
+        } else if (event.type === 'CUSTOM') {
+          const uiValue = (event as { value?: UIComponent }).value;
+          const suggestion = formatUISuggestion(uiValue);
+          setMessages((prev) => [
+            ...prev,
+            {
+              id: `ui-${Date.now()}`,
+              role: 'interviewer',
+              content: suggestion,
+              isStreaming: false,
+            },
+          ]);
         } else if (event.type === 'ERROR') {
           // Robust error extraction from SSE event - handle nested/object messages
           const rawMsg = (event as unknown as Record<string, unknown>).message;
diff --git a/src/frontend/src/pages/DesignAssistantPage.tsx b/src/frontend/src/pages/DesignAssistantPage.tsx
index 2902b43..6aba06f 100644
--- a/src/frontend/src/pages/DesignAssistantPage.tsx
+++ b/src/frontend/src/pages/DesignAssistantPage.tsx
@@ -12,6 +12,7 @@ import {
   BlueprintSidebar,
   DebugPanel,
 } from '../components/design-assistant';
+import type { DataTableSubmission, ProcessMapSubmission } from '../types/design-session';
 
 export function DesignAssistantPage() {
   const { projectId } = useParams<{ projectId: string }>();
@@ -65,6 +66,25 @@ export function DesignAssistantPage() {
     sendMessage(`I chose: ${optionId}`);
   };
 
+  const handleQuickConfirm = (answer: 'Yes' | 'No') => {
+    clearPendingUIComponent();
+    sendMessage(answer);
+  };
+
+  const handleTableSubmit = (payload: DataTableSubmission) => {
+    clearPendingUIComponent();
+    sendMessage(
+      `[DATA_TABLE_SUBMIT]${JSON.stringify(payload)}[/DATA_TABLE_SUBMIT]`
+    );
+  };
+
+  const handleProcessMapSubmit = (payload: ProcessMapSubmission) => {
+    clearPendingUIComponent();
+    sendMessage(
+      `[PROCESS_MAP_SUBMIT]${JSON.stringify(payload)}[/PROCESS_MAP_SUBMIT]`
+    );
+  };
+
   const handlePromptSave = (editedPrompt: string) => {
     clearPendingUIComponent(); // Clear the prompt editor
     // Send the edited prompt with a marker so the backend knows it's the finalized prompt
@@ -298,6 +318,10 @@ export function DesignAssistantPage() {
                       key={message.id}
                       message={message}
                       onOptionSelect={handleOptionSelect}
+                      onQuickConfirm={handleQuickConfirm}
+                      isLastAssistantMessage={isLastAssistantMessage}
+                      onTableSubmit={handleTableSubmit}
+                      onProcessMapSubmit={handleProcessMapSubmit}
                       onPromptSave={handlePromptSave}
                       externalUIComponent={isLastAssistantMessage ? pendingUIComponent : null}
                     />
diff --git a/src/frontend/src/pages/SimulationPage.tsx b/src/frontend/src/pages/SimulationPage.tsx
index 0b65bfd..4d78bae 100644
--- a/src/frontend/src/pages/SimulationPage.tsx
+++ b/src/frontend/src/pages/SimulationPage.tsx
@@ -14,13 +14,13 @@ import {
   deleteSimulation,
   SimulationModel,
 } from '../api/simulation-sessions';
-
-interface Message {
-  id: string;
-  role: 'user' | 'assistant';
-  content: string;
-  isStreaming?: boolean;
-}
+import { ChatMessage as ChatMessageBubble } from '../components/design-assistant';
+import type {
+  ChatMessage as ChatMessageType,
+  DataTableSubmission,
+  ProcessMapSubmission,
+  UIComponent,
+} from '../types/design-session';
 
 export function SimulationPage() {
   const { projectId } = useParams<{ projectId: string }>();
@@ -31,7 +31,8 @@ export function SimulationPage() {
   const [sessionId, setSessionId] = useState<string | null>(null);
   const [systemPrompt, setSystemPrompt] = useState('');
   const [selectedModel, setSelectedModel] = useState<SimulationModel>('sonnet');
-  const [messages, setMessages] = useState<Message[]>([]);
+  const [messages, setMessages] = useState<ChatMessageType[]>([]);
+  const [pendingUIComponent, setPendingUIComponent] = useState<UIComponent | null>(null);
   const [inputValue, setInputValue] = useState('');
   const [isLoading, setIsLoading] = useState(true);
   const [isStreaming, setIsStreaming] = useState(false);
@@ -89,64 +90,109 @@ export function SimulationPage() {
     messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' });
   }, [messages]);
 
-  const handleSend = useCallback(async () => {
-    if (!sessionId || !inputValue.trim() || isStreaming) return;
+  const sendMessageText = useCallback(
+    async (content: string) => {
+      if (!sessionId || !content.trim() || isStreaming) return;
 
-    const userMessage: Message = {
-      id: `user-${Date.now()}`,
-      role: 'user',
-      content: inputValue.trim(),
-    };
+      setPendingUIComponent(null);
 
-    setMessages((prev) => [...prev, userMessage]);
-    setInputValue('');
-    setIsStreaming(true);
+      const userMessage: ChatMessageType = {
+        id: `user-${Date.now()}`,
+        role: 'user',
+        content: content.trim(),
+        timestamp: new Date(),
+      };
 
-    // Add streaming placeholder
-    const assistantId = `assistant-${Date.now()}`;
-    setMessages((prev) => [
-      ...prev,
-      { id: assistantId, role: 'assistant', content: '', isStreaming: true },
-    ]);
+      setMessages((prev) => [...prev, userMessage]);
+      setIsStreaming(true);
 
-    try {
-      for await (const event of sendSimulationMessage(sessionId, userMessage.content)) {
-        if (event.type === 'TEXT_MESSAGE_CONTENT' && event.delta) {
-          // Use functional update to avoid stale closure - append delta to current content
-          setMessages((prev) =>
-            prev.map((msg) =>
-              msg.id === assistantId
-                ? { ...msg, content: msg.content + event.delta }
-                : msg
-            )
-          );
-        } else if (event.type === 'TEXT_MESSAGE_END') {
-          setMessages((prev) =>
-            prev.map((msg) =>
-              msg.id === assistantId
-                ? { ...msg, isStreaming: false }
-                : msg
-            )
-          );
-        } else if (event.type === 'ERROR') {
-          setError(event.message || 'An error occurred');
+      // Add streaming placeholder
+      const assistantId = `assistant-${Date.now()}`;
+      setMessages((prev) => [
+        ...prev,
+        { id: assistantId, role: 'assistant', content: '', isStreaming: true, timestamp: new Date() },
+      ]);
+
+      try {
+        for await (const event of sendSimulationMessage(sessionId, userMessage.content)) {
+          if (event.type === 'TEXT_MESSAGE_CONTENT' && event.delta) {
+            // Use functional update to avoid stale closure - append delta to current content
+            setMessages((prev) =>
+              prev.map((msg) =>
+                msg.id === assistantId
+                  ? { ...msg, content: msg.content + event.delta }
+                  : msg
+              )
+            );
+          } else if (event.type === 'TEXT_MESSAGE_END') {
+            setMessages((prev) =>
+              prev.map((msg) =>
+                msg.id === assistantId
+                  ? { ...msg, isStreaming: false }
+                  : msg
+              )
+            );
+          } else if (event.type === 'CUSTOM') {
+            const value = (event as { value?: UIComponent }).value;
+            if (value) {
+              setPendingUIComponent(value);
+            }
+          } else if (event.type === 'ERROR') {
+            setError(event.message || 'An error occurred');
+          }
         }
+      } catch (err) {
+        const errorMessage = err instanceof Error ? err.message : 'Failed to send message';
+        // If session not found, prompt user to refresh
+        if (errorMessage.includes('Not Found')) {
+          setError('Session expired. Please refresh the page to start a new simulation.');
+          setSessionId(null);
+        } else {
+          setError(errorMessage);
+        }
+        // Remove streaming message on error
+        setMessages((prev) => prev.filter((msg) => msg.id !== assistantId));
+      } finally {
+        setIsStreaming(false);
       }
-    } catch (err) {
-      const errorMessage = err instanceof Error ? err.message : 'Failed to send message';
-      // If session not found, prompt user to refresh
-      if (errorMessage.includes('Not Found')) {
-        setError('Session expired. Please refresh the page to start a new simulation.');
-        setSessionId(null);
-      } else {
-        setError(errorMessage);
-      }
-      // Remove streaming message on error
-      setMessages((prev) => prev.filter((msg) => msg.id !== assistantId));
-    } finally {
-      setIsStreaming(false);
-    }
-  }, [sessionId, inputValue, isStreaming]);
+    },
+    [sessionId, isStreaming]
+  );
+
+  const handleSend = useCallback(async () => {
+    if (!inputValue.trim()) return;
+    const content = inputValue.trim();
+    setInputValue('');
+    await sendMessageText(content);
+  }, [inputValue, sendMessageText]);
+
+  const handleTableSubmit = useCallback(
+    async (payload: DataTableSubmission) => {
+      setPendingUIComponent(null);
+      await sendMessageText(
+        `[DATA_TABLE_SUBMIT]${JSON.stringify(payload)}[/DATA_TABLE_SUBMIT]`
+      );
+    },
+    [sendMessageText]
+  );
+
+  const handleProcessMapSubmit = useCallback(
+    async (payload: ProcessMapSubmission) => {
+      setPendingUIComponent(null);
+      await sendMessageText(
+        `[PROCESS_MAP_SUBMIT]${JSON.stringify(payload)}[/PROCESS_MAP_SUBMIT]`
+      );
+    },
+    [sendMessageText]
+  );
+
+  const handleOptionSelect = useCallback(
+    async (selection: string) => {
+      setPendingUIComponent(null);
+      await sendMessageText(`I chose: ${selection}`);
+    },
+    [sendMessageText]
+  );
 
   const handleReset = useCallback(async () => {
     if (!sessionId) return;
@@ -154,6 +200,7 @@ export function SimulationPage() {
     try {
       await resetSimulation(sessionId);
       setMessages([]);
+      setPendingUIComponent(null);
     } catch (err) {
       setError(err instanceof Error ? err.message : 'Failed to reset');
     }
@@ -284,29 +331,20 @@ export function SimulationPage() {
               </div>
             ) : (
               <div className="space-y-4 max-w-3xl mx-auto">
-                {messages.map((message) => (
-                  <div
-                    key={message.id}
-                    className={clsx(
-                      'flex',
-                      message.role === 'user' ? 'justify-end' : 'justify-start'
-                    )}
-                  >
-                    <div
-                      className={clsx(
-                        'max-w-[80%] rounded-lg px-4 py-3',
-                        message.role === 'user'
-                          ? 'bg-blue-600 text-white'
-                          : 'bg-white text-gray-900 border border-gray-200',
-                        message.isStreaming && 'animate-pulse'
-                      )}
-                    >
-                      <div className="whitespace-pre-wrap text-sm leading-relaxed">
-                        {message.content || (message.isStreaming && '...')}
-                      </div>
-                    </div>
-                  </div>
-                ))}
+                {messages.map((message, index) => {
+                  const isLastAssistantMessage =
+                    message.role === 'assistant' && index === messages.length - 1;
+                  return (
+                    <ChatMessageBubble
+                      key={message.id}
+                      message={message}
+                      externalUIComponent={isLastAssistantMessage ? pendingUIComponent : null}
+                      onOptionSelect={handleOptionSelect}
+                      onTableSubmit={handleTableSubmit}
+                      onProcessMapSubmit={handleProcessMapSubmit}
+                    />
+                  );
+                })}
                 <div ref={messagesEndRef} />
               </div>
             )}
diff --git a/src/frontend/src/types/design-session.ts b/src/frontend/src/types/design-session.ts
index 0ff1d2c..8c4223e 100644
--- a/src/frontend/src/types/design-session.ts
+++ b/src/frontend/src/types/design-session.ts
@@ -18,7 +18,9 @@ export type ClaraCustomEventName =
   | 'clara:ask'
   | 'clara:agent_configured'
   | 'clara:blueprint_preview'
-  | 'clara:prompt_editor';
+  | 'clara:prompt_editor'
+  | 'clara:data_table'
+  | 'clara:process_map';
 
 export interface AGUIEvent {
   type: AGUIEventType;
@@ -214,6 +216,7 @@ export interface AskOption {
   id: string;
   label: string;
   description?: string;
+  requires_input?: boolean;
 }
 
 export interface AskUIComponent {
@@ -223,6 +226,32 @@ export interface AskUIComponent {
   multi_select: boolean;
 }
 
+export interface DataTableColumn {
+  name: string;
+  type: 'text' | 'number' | 'enum' | 'date' | 'url';
+  required?: boolean;
+  options?: string[];
+}
+
+export interface DataTableUIComponent {
+  type: 'data_table';
+  title: string;
+  columns: DataTableColumn[];
+  min_rows?: number;
+  starter_rows?: number;
+  input_modes?: Array<'paste' | 'inline' | 'import'>;
+  summary_prompt?: string;
+}
+
+export interface ProcessMapUIComponent {
+  type: 'process_map';
+  title: string;
+  required_fields: string[];
+  edge_types?: string[];
+  min_steps?: number;
+  seed_nodes?: string[];
+}
+
 // Agent configured UI Component (from agent_summary tool)
 export interface AgentConfiguredUIComponent {
   type: 'agent_configured';
@@ -242,7 +271,32 @@ export interface PromptEditorUIComponent {
 }
 
 // Union type for all UI components
-export type UIComponent = AskUIComponent | AgentConfiguredUIComponent | PromptEditorUIComponent;
+export type UIComponent =
+  | AskUIComponent
+  | DataTableUIComponent
+  | ProcessMapUIComponent
+  | AgentConfiguredUIComponent
+  | PromptEditorUIComponent;
+
+export type DataTableRow = Record<string, string>;
+
+export interface DataTableSubmission {
+  title: string;
+  columns: DataTableColumn[];
+  rows: DataTableRow[];
+}
+
+export interface ProcessMapStep {
+  step_name: string;
+  owner: string;
+  outcome: string;
+  edge_type?: string;
+}
+
+export interface ProcessMapSubmission {
+  title: string;
+  steps: ProcessMapStep[];
+}
 
 // Parse UI component from message content
 export function parseUIComponent(content: string): UIComponent | null {