Deploy Autonomous AI Agents
Build and deploy agents with Claude API, OpenClaw for orchestration, Railway for deployment, and MCP for tool access. Agents that think, learn, and act 24/7.
🛠️What's in the Box
Claude API
Latest Claude models with 200k context window for complex reasoning and decision-making.
OpenClaw
Agent orchestration and skill management framework for multi-agent systems.
Railway
Deploy agents and background workers with cron jobs and scheduled tasks.
Model Context Protocol
Unified interface for agents to access tools, APIs, and external services.
Verified Skills for this Stack
Claude API Helper
Master the Claude API and streaming
MCP Builder
Build Model Context Protocol servers
Agent Council
Multi-agent systems and coordination
Prompt Engineering
Craft effective prompts for agents
Error Recovery
Build resilient agent fallbacks
Agent Monitoring
Monitor, log, and debug agents
Boost your build with verified ClawHub skills.
Explore all skills🏗️Why It Works
🧠 State-of-the-Art Reasoning
Claude's 200k context window and extended thinking give agents the ability to handle complex multi-step tasks and learn from experience.
🔧 Universal Tool Access
MCP provides a standardized way to connect agents to any tool, API, or service. Bind once, use everywhere.
⚡ Production Deployment
Railway handles infrastructure complexity. Deploy agents with cron jobs, webhooks, and 24/7 operation without DevOps expertise.
🎯 OpenClaw Orchestration
Coordinate multiple agents, manage state, and build complex workflows with OpenClaw's agent framework.
📅10-Day Timeline
Days 1-2
Design & Setup
- ✓Define agent personality and goals
- ✓Sketch tool integrations and data flow
- ✓Set up Claude API keys and project
- ✓Initialize OpenClaw and Railway projects
Days 3-4
Core Agent Loop
- ✓Implement agent loop with Claude API
- ✓Build prompt system and context management
- ✓Set up memory persistence (vector storage)
- ✓Add decision logging and audit trails
Days 5-6
Tool Integration with MCP
- ✓Design MCP server architecture
- ✓Build MCP handlers for key tools
- ✓Connect to external APIs (GitHub, Slack, etc.)
- ✓Test tool calling and error handling
Days 7-8
OpenClaw Orchestration
- ✓Set up agent coordination if multi-agent
- ✓Implement state management
- ✓Build skill management system
- ✓Create agent dashboard for monitoring
Days 9-10
Deploy & Monitor
- ✓Deploy agent to Railway with cron jobs
- ✓Set up webhook triggers
- ✓Configure monitoring and alerting
- ✓Document agent behavior and capabilities
✅Launch Checklists
Pre-Launch
- 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
Post-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, MCP tool integration, agent orchestration, and production deployment. Build agents that actually work, not toys.