AI Agents & Harnesses Foundations
Building from ReAct Loops to Agent Harnesses with Harness Engineering, LangChain, and LangGraph
Created by Eden Marco
Explore the foundations of building AI agents using LangChain and LangGraph. You'll work hands-on with core concepts like agent loops, tool calling, and context engineering while learning to design, debug, and improve modern agent systems for real-world applications.
Packt | Sep 2026 | 260 min
What You Will Learn
You'll work through practical projects that introduce key concepts step by step. Each section guides you from building simple agents to more advanced harnesses, helping you understand the reasoning behind design decisions. By applying what you learn to real APIs and scenarios, you'll gain a solid grasp of how modern agent systems are developed and maintained.
Key Features
- Build and debug AI agents using LangChain, LangGraph, and LangSmith
- Implement ReAct reasoning loops, tool calling, and structured outputs
- Apply context engineering, MCP integration, and RAG to real-world agent workflows
Target Audience
This course is designed for software developers, AI engineers, data scientists, and technical professionals who want to build and understand AI agents with LangChain and LangGraph. If you're comfortable with Python, Git, and APIs, and want to move beyond basic coding to practical agent implementation, you'll find this course a great fit. No prior machine learning experience is required.
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