Current development priorities and immediate roadmap
For our complete long-term vision and version strategy, see 00-Vision.md
<<<<<<< HEAD All pre-merge requirements completed and merged to main:
- ✅ JSON parsing robustness with comprehensive unit tests
- ✅ React error boundaries with user-friendly error UI
- ✅ Real performance metrics reading from log data
- ✅ Enhanced code quality and integration testing
- ✅ Railway deployment updated and functional
=======
Goal: Investigate if MCP can exceed REST API performance beyond initial expectations
- ✅ Basic MCP integration: Complete with universal adapter pattern
- ✅ Performance framework: A/B testing infrastructure ready
- 🔄 Optimization investigation: Explore advanced MCP capabilities
- ❓ Performance ceiling: Can MCP exceed REST by more than 20%?
Current Status: Foundation complete, optimization research beginning
Key Research Questions:
- Can persistent SSE connections provide >20% improvement?
- Are there undocumented MCP optimizations available?
- Is there potential for parallel tool execution?
- Can streaming responses reduce perceived latency?
- Are there MCP-specific caching opportunities?
4468b20 (feat: integrate FastIntercomMCP with universal adapter architecture)
Current Status: Major UI redesign for conversational experience
Goal: Enable rich conversational responses with customer references
- ✅ Session state management: Full backend/frontend session handling
- ✅ Follow-up detection: Pattern matching for conversational queries
- ✅ Conversation context: Reuse previous conversations for follow-ups
- 🚧 Rich text responses: Modify follow-up responses to include conversation IDs naturally
<<<<<<< HEAD
Goal: Transform from structured cards to chat-like experience
- ✅ Frontend store fixes: Resolved React hook errors, unified Zustand store
- 🚧 Chat interface: Add conversational flow after initial structured response
- ❌ Response format switching: Structured cards → free text for follow-ups
- ❌ Customer link detection: Parse emails in responses, create interactive elements
- ❌ Card collapse: Initial cards collapse when chat begins ======= Optimization Investigation Plan:
- Phase 1: Baseline performance measurement (REST vs basic MCP)
- Phase 2: Connection optimization (persistent SSE, pooling)
- Phase 3: Tool execution optimization (parallelization, streaming)
- Phase 4: Advanced features exploration (caching, pre-fetching)
- Phase 5: Document findings and performance ceiling
4468b20 (feat: integrate FastIntercomMCP with universal adapter architecture)
Goal: Clickable customer references with actions
- ❌ Rich text parsing: Detect emails/conversation IDs in AI responses
- ❌ Customer components: Hover popups with Copy/Open actions
- ❌ Conversation linking: Direct links to Intercom conversations
- ❌ Reset functionality: Clear conversation, start fresh
Goal: Clean, minimal, conversational interface
- ❌ Chat styling: Light, airy design for conversation flow
- ❌ Session management: Prominent but subtle reset functionality
- ❌ Response enhancement: Ensure AI includes conversation references naturally
- ❌ Future features: Save conversations, multiple chat sessions
- Rich text parsing: AI returns natural text, frontend parses for interactive elements
- Customer detection: Email patterns converted to React components with hover actions
- Conversation flow: Initial query → structured cards → conversational chat interface
- Session scope: One conversation thread per session with reset capability
- Response format: Structured insights first, then free-text follow-ups with customer references
Goal: Bulletproof one-command deployment for developers
- ✅ Docker setup complete: Multi-stage build with health checks and security
- ✅ End-to-end tested: Full deployment validation with real Docker daemon
- ✅ Build issues resolved: Frontend TypeScript and Poetry 2.x compatibility fixed
- ✅ CI/CD pipeline: GitHub Actions workflow with automated Docker testing
- ✅ Documentation updated: Clear README and setup guide
- ✅ Environment template: Comprehensive .env.example
- ✅ Developer workflow:
git clone→cp .env.example .env→docker-compose up→ working app
Goal: Enable hosted SaaS for non-technical users
- ✅ Platform decision: Railway selected for developer experience + agent marketplace alignment
- Deployment pipeline: GitHub → Railway automatic deployment using existing Docker setup
- Production instance: Get first hosted version live for user testing
- Hosted features: Landing page, usage analytics, enhanced UI
- Production monitoring: Railway's built-in metrics + error tracking
- Template marketplace: Create Railway template for discoverability
Goal: Improve usability based on user feedback from deployed versions
- Follow-up questions: Port CLI's conversation memory to web interface
- Drill-down analysis: "Tell me more about verification issues"
- Export functionality: CSV/PDF reports, shareable insights
- Usage analytics: Track popular queries and optimize performance
- Better onboarding: Guided setup with example queries
Goal: Handle larger user base and improve response times
- MCP integration: Model Context Protocol for faster queries (<30s target)
- Caching layer: Cache frequently accessed conversations
- Rate limiting: Smart throttling for hosted version
- Enterprise features: Team collaboration, custom branding
Goal: Expand beyond Intercom (post-MVP)
- Slack integration: Analyze team discussions about customers
- Linear integration: Connect customer feedback to product decisions
- Cross-platform queries: "What customer issues are blocking our roadmap?"
- Universal agent: Skills-based architecture for extensibility
-
Railway Setup:
- Sign up for Railway account with GitHub integration
- Use $5 trial credit for initial deployment
- Connect ask-intercom repository
-
Deployment Configuration:
- Create Railway project from Docker template
- Configure environment variables from .env.example
- Set up automatic deployments from main branch
- Configure custom domain (optional)
-
Testing & Validation:
- Deploy existing Docker setup
- Verify API endpoints working
- Test real-time progress features
- Monitor performance metrics
-
Template Creation:
- Package as Railway template
- Add to Railway marketplace
- Include setup documentation
- Agent marketplace alignment: Enterprise-grade scaling (112 vCPU/2TB RAM)
- Developer experience: Project Canvas, auto PR environments, built-in DB UI
- Template marketplace: Discovery channel for developers
- Security & compliance: Built-in secrets management for API keys
- Cost structure: Pay-as-you-go aligns with variable agent workloads
- Future-proof: Container-first architecture ready for multi-LLM support
- v0.4: Docker deployment working + cloud platform chosen + hosted app live
- v0.5: Follow-up questions working + streamlined UI
- v0.6: Multi-platform queries functional
- Full multi-platform support (Intercom + Slack + Linear)
- Skills marketplace and plugin system
- Enterprise features and team collaboration
- Deploy to Claude Apps, GPT Store
- White-label platform options
- Advanced analytics and reporting
- Predictive insights and recommendations
- Integration ecosystem and API platform
- AI-driven workflow automation
- User-driven: Build what people actually need
- Quality first: Each version should be solid before adding more
- Simple by default: Complex features should be optional
- Open source: Community contributions and transparency
- ✅ Railway deployment: Live at https://ask-intercom-production.up.railway.app/
- Pre-merge tasks (HIGH PRIORITY):
- Test JSON parsing fix with real query on live app
- Add unit tests for structured JSON parsing
- Add error boundaries to React frontend
- Add basic performance metrics collection
- Verify all improvements work end-to-end
- Post-merge tasks (MEDIUM PRIORITY):
- Create Railway template for marketplace
- User testing and feedback collection
- Performance monitoring dashboard
Critical for monitoring live deployment:
- Live logs:
railway logs | grep -i "error\|json\|parse" - Web logs API:
GET https://ask-intercom-production.up.railway.app/api/logs?lines=100 - Debug status:
GET https://ask-intercom-production.up.railway.app/api/debug - Health check:
GET https://ask-intercom-production.up.railway.app/api/health
Common Issues to Monitor:
- JSON parsing failures ("Failed to parse structured response")
- API rate limits or authentication errors
- Performance degradation (response times > 2 minutes)
- Memory/resource issues in Railway dashboard
- MCP Performance Ceiling: Can MCP optimization exceed REST API by 50%+ or more?
- Advanced MCP Features: What undocumented/experimental capabilities exist?
- Parallel Execution: Can multiple MCP tool calls run simultaneously?
- Streaming Architecture: Is real-time response streaming possible with MCP?
- Performance vs Complexity: Is optimization worth the implementation complexity?
- Market fit: Is Intercom analysis the right starting point, or should we go multi-platform sooner?
- Business model: Open source with hosted option, or SaaS from the start?
- AI model options: Support for Claude, local models, etc.
- Advanced filtering: Sentiment analysis, customer segments, etc.
- Automation: Scheduled reports, alert systems
- Integrations: Zapier, webhooks, API access
- Team features: Shared insights, collaboration tools
- User feedback system: Users can provide feedback on individual analytics responses
- Bug reporting: Users can report bugs with full metadata and log context for analysis
- Quality improvement loop: Associate feedback/bugs with session data for iterative improvements
Last updated: June 22, 2025 - Conversational UI redesign in progress