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feat: AgentFlow routing, multi-tenant architecture, Telegram setup
## Added - Multi-tenant AgentFlow architecture documentation - AgentFlow routing guide with scoring algorithm - Telegram bot configuration and default flow - New example agents (k8s-ops, devops, docker-ops, echo, status, assistant) ## Changed - /run agent command now routes through handle_natural_language - Uses pre-loaded agents with correct model (google:gemini-2.5-flash) and tools - All example agents updated to use google:gemini-2.5-flash - Agent loading flag logic fixed for proper pre-loading ## Fixed - Agents now load correctly regardless of flows configuration - /run agent no longer uses hardcoded Anthropic fallback - Tools and system prompts correctly applied from agent YAML 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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‎.hive-memory/architecture-lego-blocks.json‎

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{
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"philosophy": "Modular, pluggable framework where components are reusable building blocks",
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"design_principles": [
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"Pluggable Triggers - Add new event sources without changing core",
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"Composable Nodes - Chain processing steps in any order",
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"Swappable Agents - Use any LLM provider with consistent interface",
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"Extensible Tools - Add custom tools via MCP or built-in",
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"Flexible Memory - Choose backend based on use case",
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"YAML-First - Declarative configuration for everything"
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],
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"core_building_blocks": {
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"triggers_input": [
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"Slack", "WhatsApp", "Telegram", "GitHub", "HTTP",
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"Schedule", "PagerDuty", "Kafka", "SQS"
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],
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"nodes_processing": [
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"Transform", "Agent", "Conditional", "Parallel",
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"Join", "Wait", "Approval", "Loop", "End"
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],
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"outputs": ["Slack", "Discord", "HTTP", "Email", "File"],
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"agents": {
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"components": ["LLM Provider", "Instructions", "Context", "Tools"],
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"providers": ["OpenAI", "Anthropic", "Google", "Ollama", "Groq"]
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},
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"memory": ["InMemory", "File", "SQLite", "Redis (planned)"],
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"tools": ["Shell", "HTTP", "FileSystem", "MCP", "kubectl"]
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},
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"orchestration": {
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"AgentFlow": {
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"description": "Workflow orchestration with nodes + connections",
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"schema": "v1 (current)"
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},
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"AgentFleet": {
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"description": "Multi-agent parallel execution and coordination",
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"status": "implemented"
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}
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},
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"extension_traits": {
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"TriggerPlatform": {
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"methods": ["start()", "handle_event()"],
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"purpose": "Implement for new event sources"
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},
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"NodeExecutor": {
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"methods": ["execute()"],
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"purpose": "Implement for new processing steps"
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}
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}
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}

‎.hive-memory/implementation-status.json‎

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{
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"version": "0.1.15",
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"last_updated": "2025-12-18",
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"core_engine_status": "~85% complete",
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"implemented_features": {
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"triggers": {
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"complete": ["Slack", "HTTP/Webhook", "Schedule/Cron", "Manual CLI"],
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"partial": ["Discord"]
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},
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"nodes": {
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"complete": ["Transform", "Agent", "Conditional", "Slack", "Discord", "Wait", "Parallel", "Join", "Approval", "End"],
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"partial": ["HTTP"]
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},
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"platform_features": {
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"complete": [
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"Slack reactions approval (✅/❌)",
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"Approval whitelist (approval_allowed_users)",
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"Bot self-approval prevention",
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"Conversation memory (per-channel/thread)",
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"Multi-tenant routing (FlowRouter)"
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]
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},
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"llm_providers": ["OpenAI", "Anthropic", "Google", "Ollama", "Groq"],
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"memory_backends": ["InMemory", "File", "SQLite"]
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},
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"planned_triggers": {
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"P1": [
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{"name": "WhatsApp", "issue": "#23"},
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{"name": "Telegram", "issue": "#24"},
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{"name": "GitHub", "issue": "#25"}
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],
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"P3": [
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{"name": "PagerDuty", "issue": "#26"},
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{"name": "Kafka", "issue": "TBD"},
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{"name": "SQS", "issue": "TBD"}
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]
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},
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"planned_nodes": ["Loop", "HTTP (full implementation)"],
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"traits": {
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"TriggerPlatform": "trait for new event sources - start() and handle_event()",
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"NodeExecutor": "trait for new processing steps - execute()"
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},
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"next_steps": [
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"Build tutorials for individual users",
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"Implement P1 triggers (WhatsApp, Telegram, GitHub)",
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"Convert applicable v1alpha1 examples to v1"
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]
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}

‎.hive-memory/session-2025-12-18-roadmap.json‎

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{
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"session_id": "session-2025-12-18-roadmap-organization",
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"date": "2025-12-18",
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"summary": "AgentFlow roadmap creation and example organization",
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"completed_tasks": [
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"Created ROADMAP.md with feature tracking and architecture philosophy",
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"Documented 'Lego Blocks for Agentic Automation' modular architecture",
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"Moved 6 v1alpha1 examples to examples/flows/planned/ directory",
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"Created GitHub issues #23-26 for trigger implementations",
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"Updated all documentation with issue links",
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"Committed and pushed changes to main (2afcddc)"
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],
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"architecture_principles": [
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"Pluggable Triggers - Add new event sources without changing core",
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"Composable Nodes - Chain processing steps in any order",
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"Swappable Agents - Use any LLM provider with consistent interface",
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"Extensible Tools - Add custom tools via MCP or built-in",
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"Flexible Memory - Choose backend based on use case",
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"YAML-First - Declarative configuration for everything"
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],
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"github_issues_created": {
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"#23": "WhatsApp trigger support (P1)",
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"#24": "Telegram trigger support (P1)",
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"#25": "GitHub webhook trigger (P1)",
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"#26": "PagerDuty trigger (P3)"
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},
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"priority_focus": "Individual users first (WhatsApp, Telegram, GitHub), then enterprise (PagerDuty, Kafka, SQS)",
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"schema_versions": {
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"v1": "Current - nodes + connections arrays (implemented)",
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"v1alpha1": "Planned - fleet + actions arrays (future)"
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},
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"key_files_modified": [
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"ROADMAP.md (created)",
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"examples/flows/planned/README.md (created)",
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"examples/flows/planned/*.yaml (moved from examples/flows/)"
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],
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"commit": "2afcddc - docs: Add ROADMAP.md and organize v1alpha1 examples"
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}

‎CHANGELOG.md‎

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## [Unreleased]
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### Added
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- **Agent Tool Execution System** - Full tool execution support for agents
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- Built-in tools: `kubectl`, `docker`, `helm`, `terraform`, `aws`, `git`, `shell`
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- File tools: `read_file`, `write_file`, `list_directory`, `search_files`
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- Observability tools: `prometheus_query`, `loki_query`
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- Tools registered and passed to LLM with definitions
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- **Kubernetes-Style Agent Discovery** - Load agents by `kind: Agent` and `metadata.name`
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- Agents indexed by `metadata.name`, not filename
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- Support for both K8s-style and flat YAML formats via `AgentConfigInput` enum
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- `--agents-dir` flag for pre-loading agents on server startup
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- **Approval User Whitelist** - Configure which users can approve destructive commands
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- New `approval_allowed_users` field in platform config (Slack implemented)
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- Platform-agnostic design: supports `U12345678`, `slack:U12345678`, `email:user@company.com` formats
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- Currently implemented: Raw Slack user IDs (e.g., `U015VBH1GTZ`)
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- Planned: Global `spec.approval.allowed_users` for multi-platform deployments
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- If not configured, anyone can approve (backward compatible)
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- Documentation and example config updated
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- **Multi-Tenant AgentFlow Architecture** - Enterprise-ready multi-tenant deployments
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- Route different channels/users/patterns to different agents
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- Support for multiple organizations in single daemon
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- Environment isolation per AgentFlow (kubeconfig, namespace, env vars)
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- Comprehensive architecture documentation at `docs/architecture/multi-tenant-agentflows.md`
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- **Telegram AgentFlow Example** - Complete Telegram bot setup following Slack pattern
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- New `examples/configs/telegram-k8s-flow.yaml` example
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- Same architecture as Slack bot with channel/user/pattern routing
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- **AgentFlow Routing Documentation** - Complete guide on how routing works
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- Flow scoring algorithm explained
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- Common routing patterns (pattern-based, channel-based, user-based)
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- Debugging tips and best practices
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- New guide at `docs/guides/agentflow-routing.md`
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### Changed
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- All example agents updated to use `google:gemini-2.5-flash` as the primary model
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- Improved debug logging for agent parsing and tool registration
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- Documentation updated with correct model names for all providers
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- **`/run agent` command now routes through AgentFlow** - Uses pre-loaded agents with correct model and tools
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- No longer creates hardcoded Anthropic fallback
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- Follows same code path as natural language messages
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- Ensures consistent behavior across commands and chat
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### Fixed
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- System prompts from agent YAML files now correctly loaded and used
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- Agent execution error handling with proper response to platforms
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- Bracket structure in `handle_natural_language` function
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- **Agent loading flag logic** - Fixed issue where agents weren't loaded when flows directory didn't exist
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- Introduced proper `agents_loaded` boolean tracking
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- Agents now correctly pre-load regardless of flows configuration
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- **`/run agent` using wrong model** - Fixed hardcoded Anthropic model to use pre-loaded agent's configuration
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- Now uses `google:gemini-2.5-flash` from agent YAML
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- Tools and system prompts correctly applied
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### Notes
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- Config changes to `approval_allowed_users` require server restart (hot-reload coming in [Issue #22](https://github.com/agenticdevops/aof/issues/22))

‎agents/devops-agent.yaml‎

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category: devops
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spec:
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model: openai:gpt-4o
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# IMPORTANT: Use google:gemini-2.5-flash for all agents
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model: google:gemini-2.5-flash
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instructions: |
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system_prompt: |
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You are a DevOps assistant helping with various infrastructure and deployment tasks.
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Your capabilities:

‎agents/k8s-ops.yaml‎

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# Kubernetes Operations Agent
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# Primary agent for Telegram/Slack bot interactions
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#
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# Loaded automatically when running:
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# aofctl serve --agents-dir ./agents
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#
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apiVersion: aof.dev/v1alpha1
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kind: Agent
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metadata:
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name: k8s-ops
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labels:
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category: infrastructure
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platform: kubernetes
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spec:
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# IMPORTANT: Use google:gemini-2.5-flash for all agents
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model: google:gemini-2.5-flash
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max_tokens: 4096
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temperature: 0.3
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# Built-in tools available to this agent
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tools:
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- kubectl
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- helm
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- docker
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- shell
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system_prompt: |
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You are an expert Kubernetes operations engineer helping users manage their clusters.
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## Your Capabilities
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- Execute kubectl commands to manage Kubernetes resources
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- Deploy applications using Helm charts
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- Diagnose pod issues, check logs, and troubleshoot
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- Scale deployments and manage replicas
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- Provide best practice recommendations
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## Safety Rules
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1. ALWAYS use `kubectl create deployment` instead of `kubectl run` for deployments
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2. For commands that modify or delete resources, output in this format:
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```
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requires_approval: true
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command: "kubectl delete pod nginx-12345"
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reason: "This will terminate the running pod"
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```
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3. Never execute commands that could cause data loss without approval
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4. Always confirm the namespace before destructive operations
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## Output Format
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- Use markdown formatting for readability
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- Show command output in code blocks
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- Explain what each command does
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- Warn about potential impacts
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memory:
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type: InMemory
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config:
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max_messages: 20

‎crates/aof-runtime/src/executor/runtime.rs‎

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/// Load an agent from YAML configuration file
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///
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/// Supports both flat and Kubernetes-style YAML formats:
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/// - Flat: `name:, model:, system_prompt:, tools:, ...`
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/// - K8s: `apiVersion:, kind: Agent, metadata:, spec:, ...`
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///
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/// # Arguments
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/// * `config_path` - Path to the YAML configuration file
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///
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AofError::config(format!("Failed to read config file {}: {}", config_path, e))
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})?;
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// AgentConfig has #[serde(from = "AgentConfigInput")] which handles both K8s and flat formats
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let config: AgentConfig = serde_yaml::from_str(&config_content).map_err(|e| {
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AofError::config(format!("Failed to parse YAML config: {}", e))
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})?;
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info!("Parsed agent config: name={}, model={}, system_prompt={:?}, tools={:?}",
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config.name, config.model,
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config.system_prompt.as_ref().map(|s| format!("{}...", s.chars().take(50).collect::<String>())),
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config.tool_names());
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self.load_agent_from_config(config).await
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}
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// Create tool executor
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// Priority: mcp_servers > tools (legacy)
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info!("Creating tool executor for agent '{}': mcp_servers={}, tools={:?}",
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agent_name, config.mcp_servers.len(), config.tool_names());
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let tool_executor: Option<Arc<dyn ToolExecutor>> = if !config.mcp_servers.is_empty() {
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// Use the new flexible MCP configuration
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info!("Using MCP servers for tools");
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Some(self.create_mcp_executor_from_config(&config.mcp_servers).await?)
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} else if !config.tools.is_empty() {
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// Separate built-in tools from MCP tools
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.filter(|t| t.is_mcp())
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.map(|t| t.name())
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.collect();
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info!("Tool separation: builtin={:?}, mcp={:?}", builtin_tools, mcp_tools);
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// Known system/builtin tools
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// Unified CLI tools (recommended - simple 'command' argument approach)
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let has_system_tools = builtin_tools.iter().any(|t| system_tools.contains(t));
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let has_mcp_tools = !mcp_tools.is_empty();
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info!("Tool detection: has_system_tools={}, has_mcp_tools={}", has_system_tools, has_mcp_tools);
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if has_system_tools && !has_mcp_tools {
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debug!("Agent has only built-in tools, creating system executor");
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info!("Creating system tool executor for builtin tools: {:?}", builtin_tools);
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let tool_names: Vec<String> = builtin_tools.iter().map(|s| s.to_string()).collect();
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Some(self.create_system_executor(&tool_names)?)
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} else if has_mcp_tools {

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