Having established the core execution model and customization possibilities within LangChain, we now turn our attention to constructing more autonomous systems: agents. Unlike predefined chains, agents employ a Large Language Model (LLM) as a reasoning engine to determine sequences of actions, often interacting with external environments through tools.

This chapter provides the foundation for building and managing these sophisticated agents. You will learn to:

• Compare and implement different agent architectures like ReAct, Self-Ask, and Plan-and-Execute.
• Develop custom tools that grant agents specific capabilities, such as interacting with APIs or databases.
• Implement strategies for handling errors that may occur when an agent attempts to use a tool.
• Design systems involving multiple collaborating agents.
• Utilize structured tool calling (function calling) features available in newer LLMs for more reliable agent-tool interactions.
• Trace and analyze the decision-making process of an agent to understand its behavior and debug issues.

By the end of this chapter, you will be equipped to design agents capable of tackling complex, multi-step problems that require dynamic planning and interaction with the outside world.