Guide to designing, implementing and deploying production-ready AI agents using OpenClaw, covering architecture, tools, agent loop, memory, testing and observability.
Welcome — this section teaches how to design and ship production-ready AI agents.We use OpenClaw, a production-grade AI agent, as our running example. Studying OpenClaw lets us move beyond prototypes and understand the engineering and operational choices required to make agents reliable in real-world systems. We’ll examine the OpenClaw codebase and architecture, look at how it defines and exposes tools, trace the agent loop end-to-end, and review its memory and state management strategies.
In the second half of the lesson we shift from design to production practices that turn a demo into a dependable product: testing, monitoring, debugging, performance optimization, security hardening, and prompt engineering. Each concept is coupled with a hands-on lab so you won’t just read about best practices — you’ll apply them.
This course expects familiarity with basic Python and large language models. By the end of the section you will understand how to design tools, manage agent state, and apply production practices (testing, observability, performance, and security) to make agents reliable and maintainable.
