AI Agents: Building Autonomous Systems
Build AI agents that plan, use tools, and complete multi-step tasks autonomously. ReAct, tool use, memory architectures, multi-agent systems, and production safety.
About This Course
AI agents represent the next frontier in AI application development. This course teaches you to build agents that can browse the web, write and execute code, manage files, call APIs, and coordinate with other agents to complete complex, multi-step tasks. You'll master the ReAct framework, tool design, memory systems, and the critical safety constraints that make agents trustworthy in production. LangChain and LangGraph are used as practical tools throughout this course — explained as needed, not assumed. If you want to go deeper into how these frameworks work internally (LCEL composition, LangSmith observability, LangGraph state machines), the LangChain & LangGraph course picks up directly where this one leaves off.
What You'll Learn
- Implement the ReAct (Reasoning + Acting) agent framework from scratch
- Design robust tool schemas that agents can reliably use
- Build agents with persistent short-term and long-term memory
- Create multi-agent systems with specialized agent roles
- Implement safety guardrails to prevent agents from taking harmful actions
- Handle agent failures, loops, and unexpected behaviors gracefully
- Build code-executing agents with sandboxed environments
- Deploy autonomous agents with proper human-in-the-loop checkpoints
Who Is This For?
Ready to go beyond chat interfaces and build truly autonomous AI systems
Want to replace complex workflow automations with flexible AI agents
Building and experimenting with agent architectures and multi-agent systems
Prerequisites
- Building AI-Powered Applications with APIs
- Python for AI