OpenClaw AI in Production
Architecture, design patterns, and engineering practices for AI agent platforms
Created by Ken Huang
Move beyond AI agent demos and learn how to build robust, production-ready systems. Explore real-world engineering practices for scaling, securing, and observing autonomous AI platforms using OpenClaw as a hands-on reference.
Packt | Jun 2026 | 422 min
What You Will Learn
You will work through realistic production challenges, examining OpenClaw's internals to understand how each pattern addresses issues like failures and security threats. Each section introduces a scenario, breaks down the engineering solution, and ends with practical steps you can use right away. Security and reliability are integrated throughout, not treated as afterthoughts.
Key Features
- Apply proven architecture patterns for reliability and scalability in agent platforms
- Integrate zero trust security and policy-based access from the ground up
- Build observable, self-correcting systems ready for federated and edge deployment
Target Audience
This course is designed for AI engineers, platform architects, and senior software engineers who are building autonomous agent systems in production. You should be comfortable with Python, distributed systems, and APIs. Prior experience with OpenClaw's architecture is helpful, as the focus is on advanced implementation patterns rather than introductory material.
