Introduction to Agents and Tools

Workflows can be constructed with a path of execution determined in advance. For instance, a standard chain or RunnableSequence follows a fixed, developer-defined sequence of steps. This approach is effective for structured tasks, but it lacks the flexibility to handle problems where the solution path is not known ahead of time. To address this, agents are introduced.

An agent uses a Large Language Model not just to process text, but as a reasoning engine to make decisions. Instead of following a rigid sequence, an agent dynamically chooses a sequence of actions to take based on a user's objective and a set of available resources. It operates in a loop, continuously observing its environment, thinking about the next best action, and executing it until the initial goal is accomplished.

The Agentic Loop: Think, Act, Observe

At the heart of every agent is a reasoning loop. In modern LangChain development, this is often implemented using a state graph (via LangGraph) that orchestrates the flow. This loop enables the agent to plan and execute multi-step tasks that may require accessing external information or performing calculations.

The process generally follows these steps:

1. Objective: The agent receives a high-level objective from the user (e.g., "Who is the current CEO of Microsoft, and what was the stock price when they took over?").
2. Thought: The LLM, acting as the agent's brain, analyzes the objective. It considers the tools it has at its disposal and formulates a plan. It might reason, "To answer this, I first need to find out who the CEO is and when they started. A web search tool would be suitable for this."
3. Action: The LLM outputs a specific action to take, indicating which tool to use and what input to provide. For instance, it might decide to use a Search tool with the input "current CEO of Microsoft".
4. Observation: The agent runtime invokes the specified tool with the given input. The tool runs (e.g., performs a web search) and returns a result. This result is called an observation.
5. Repeat: The observation is passed back to the LLM. The LLM now has new information and re-evaluates its plan. It might think, "I have found that Satya Nadella became CEO on February 4, 2014. Now I need to find the stock price on that date." It then selects another action, perhaps using a financial data tool.

This cycle of thought, action, and observation repeats until the LLM determines that it has gathered enough information to fully answer the user's original objective. At that point, it breaks the loop and generates a final response.

The reasoning loop an agent follows. The LLM repeatedly chooses a tool, executes it, and uses the resulting observation to inform its next decision until the goal is met.

Tools: The Agent's Capabilities

An agent is only as capable as the tools it can use. A tool is an interface that allows an agent to interact with the outside environment. In LangChain, a tool is essentially a function with a specific purpose, packaged with a name and a description that the LLM can understand.

Examples of tools include:

• Web Search: A tool to query a search engine like Google or DuckDuckGo.
• Python REPL: A tool that can execute arbitrary Python code.
• Database Query: A tool for running SQL queries against a database.
• API Caller: A generic tool for making requests to external APIs.

The most important part of a tool is its description. The LLM does not know how the tool's code works; it relies entirely on the tool's description to decide when and how to use it. A well-written description is specific and clearly explains what the tool does, what its expected input is, and what it returns.

For example, consider a custom tool for a company's internal API:

• Good Description: Useful for when you need to find the current shipping status of a customer's order. The input should be a valid order ID.
• Poor Description: Runs queries.

The first description gives the LLM clear instructions on the tool's purpose and input requirements, enabling it to make an informed decision. The second is too vague and will likely cause the agent to either ignore the tool or use it incorrectly. By equipping an agent with a well-described set of tools, you grant it the ability to perform complex, multi-step tasks that extend beyond the built-in knowledge of the LLM itself.