Build Autonomous AI Agents
Create intelligent agents that learn, remember, and take action. This stack gives you persistent memory, tool integration, monitoring, and 24/7 operation on production infrastructure.
🛠️Tools
Claude API
Latest Claude models with 200k context window for complex agent tasks.
Supabase
Vector storage, message history, and agent state persistence.
Railway
Deploy agent workers, cron jobs, and background tasks.
LangChain / LlamaIndex
Agent orchestration, tool binding, and memory management frameworks.
Verified Skills for this Stack
Agent Council
Complete toolkit for creating autonomous AI agent systems
Claude API Helper
Build integrations with the Claude API
MCP Builder
Build Model Context Protocol servers
LangChain
Avoid common LangChain mistakes — LCEL gotchas, memory, chains
Web App Testing
Test web applications automatically
Boost your build with verified ClawHub skills.
Explore all skills🏗️How It Works
Days 1-2
Design & Setup
- ✓Define agent persona, goals, and constraints
- ✓Sketch tool integrations and data flow
- ✓Set up Claude API project and keys
- ✓Create Supabase project with vector tables
Days 3-4
Core Agent
- ✓Implement agent loop with Claude API
- ✓Build tool-binding system for external APIs
- ✓Set up vector embeddings for memory
- ✓Implement decision logging and tracing
Days 5-6
Tool Integrations
- ✓Connect to required external services (GitHub, Slack, etc.)
- ✓Build tool handlers and error recovery
- ✓Implement MCP connections for rich tool access
- ✓Test agent with real tool calls
Days 7-8
Learning & Iteration
- ✓Implement feedback loops and learning mechanisms
- ✓Add monitoring and performance metrics
- ✓Tune prompt and system instructions
- ✓Build agent dashboard for oversight
Days 9-10
Deploy & Monitor
- ✓Deploy agent workers to Railway or similar
- ✓Set up cron jobs for periodic tasks
- ✓Create alerting for failures
- ✓Configure logging and audit trails
✅Checklists
Before Production
- Agent decision logs are persistent and auditable
- All external API calls have timeouts and retries
- Error handling for failed tool calls implemented
- Rate limiting on Claude API calls configured
- Memory/vector storage is working correctly
- Agent doesn't loop infinitely or get stuck
- All secrets secured in environment variables
- Monitoring and alerting configured
- Agent behavior tested with various inputs
- Rollback plan if agent misbehaves
After Launch
- Monitor agent decision quality daily
- Collect feedback and edge cases
- Refine system prompts based on behavior
- Improve tool selection and parameters
- Analyze cost per agent run
- Plan next agent capabilities
- Document agent behavior for team
- Set up automated testing for consistency
- Plan multi-agent coordination features
- Build user-facing agent interface
Ready to build AI agents?
This stack gives you everything: Claude API, persistent memory, tool integration, and deployment infrastructure. Build agents that actually work, not toys.