What is an AI agent and how do I build one?

AI agents are one of the most misunderstood concepts in the AI product space. Here is a precise definition and practical guide to building one.

How an AI Agent Works

An agent is built on a loop called the ReAct pattern (Reason, Act, Observe):

1. Reason -- the LLM receives the goal and current state, reasons about what action to take next
2. Act -- the LLM calls a tool (function) -- search the web, query a database, call an API, write a file
3. Observe -- the tool result is returned to the LLM as context
4. Repeat -- the loop continues until the LLM determines the goal is achieved or signals it cannot proceed

Tool Use (Function Calling)

Both Claude and OpenAI support tool use natively. You define functions your agent can call (with JSON Schema describing the parameters), the LLM decides when and how to call them, and your code executes the actual function and returns the result. This is the backbone of every production AI agent -- no framework required, though LangChain and similar tools provide pre-built tool integrations.

When to Use an AI Agent vs. a Simpler Integration

• Use an agent when the task requires multiple steps, the steps are not known in advance, and the LLM must decide the sequence based on intermediate results
• Use a simpler LLM call when the task is a single transformation (classify this document, summarize this text, extract these fields) -- agents add latency and cost without benefit for single-step tasks

Code and Trust builds production AI agents for business workflow automation. See our AI implementation services or contact us to scope your agent project.