Build Agents with OpenClaw

AI agents are moving from demos to practical systems that can plan work, call tools, and complete tasks with minimal supervision. In this course, learn how OpenClaw transforms a large language model (LLM) into an autonomous digital workforce agent, and where agentic systems fit into today’s AI trends. Instructor Ray Villalobos goes over core agent components—planning, tool use, and execution loops—then shows you how to build a basic OpenClaw agent that completes a multistep task in a live environment. Ray also explains how to configure external tools and services; how OpenClaw manages reasoning and action selection across steps; and how to modify an existing agent to improve reliability and address common security issues. Along the way, you apply prompting with agentic techniques and design a simple autonomous workflow for an everyday business scenario.

Learning objectives
Explain how OpenClaw transforms a large language model (LLM) into an autonomous digital workforce agent.
Describe the core components of an OpenClaw agent, including planning, tool use, and execution loops.
Build a basic OpenClaw agent that completes a multistep task in a live environment.
Configure an OpenClaw agent to use external tools and services and demonstrate reasoning and action selection across task steps.
Modify an existing OpenClaw agent to improve reliability and address common security issues, then design a simple autonomous workflow for a real-world business scenario.