While Chains provide a structured way to execute a predetermined sequence of calls to LLMs or other utilities, many real-world tasks require more dynamic decision-making. The exact steps needed might not be known upfront and depend on the outcomes of previous actions. This is where LangChain Agents come into play.

Instead of following a fixed path like a Chain, an Agent employs an LLM as its core reasoning engine. Think of the LLM not just as a text generator, but as a decision-maker. Given a user's objective, the Agent uses the LLM to determine what action to take next, which tool to use for that action, and what input to provide to that tool.

The Agent Loop: Thought, Action, Observation

At the heart of an Agent's operation is an iterative loop:

Thought: Based on the initial objective and any previous steps, the LLM reasons about the current situation. It considers what information it has, what it still needs, and what action would be most appropriate to move closer to the goal. This internal "monologue" is often explicitly generated by the LLM when prompted correctly.
Action: Based on its thought process, the LLM decides on a specific action to perform. An action typically involves selecting a Tool and specifying the input for that tool. Tools are functions or services that allow the agent to interact with the outside world, perform calculations, retrieve information, or execute code. Examples include web search, Python REPLs, database query interfaces, or custom functions you define.
Observation: The chosen Tool is executed with the specified input. The result of this execution is captured as an Observation. This observation could be the text content from a webpage, the result of a calculation, data from an API call, or an error message if the tool failed.
Repeat: The Observation is fed back to the LLM along with the original objective and the history of previous Thought-Action-Observation steps. The LLM then starts the next cycle of reasoning (Thought), potentially choosing a different action based on the new information obtained from the Observation.

This loop continues until the LLM determines that the original objective has been fully achieved or until a predefined stopping condition (like a maximum number of steps) is met.

The iterative process an agent follows, using an LLM to decide actions based on observations from tool executions.

Why Use Agents?

Agents offer significant advantages over simpler Chains when dealing with complexity and uncertainty:

Adaptability: They can dynamically adjust their strategy based on intermediate results. If one approach fails or yields unexpected information, the agent can reason about it and try a different tool or action.
Tool Integration: Agents provide a natural framework for granting LLMs access to external capabilities, overcoming the LLM's inherent limitations (like lack of real-time information or inability to perform precise calculations).
Complex Problem Solving: They can break down intricate objectives into a sequence of smaller, manageable tasks executed via tools, mimicking how a human might approach the problem.

In essence, Agents empower LLMs to move beyond simple input-output transformations and act as autonomous problem solvers that can interact with their environment to achieve goals. While Chains excel at predictable, linear workflows, Agents thrive in situations demanding reasoning, planning, and interaction with external resources. The following sections will detail how to equip agents with tools and build them using LangChain.