Agents dominate every AI conversation right now. Demos make them look like autonomous digital assistants that think, strategize, and solve problems without human help. Peel back the interface, though, and you will find something far simpler. An agent is just a language model running inside a loop. It is a design pattern, not a consciousness. Understanding this distinction matters because it changes how you build, debug, and trust these systems.

What an Agent Actually Is

A standard chatbot is a single-shot function. You type a prompt. The model predicts the next tokens and returns a block of text. Then it stops. It does not check if its answer was accurate. It does not verify whether a web link works or whether a calculation is correct. It gives you its best guess in one go and goes silent.

An agent breaks that single shot into a repeating cycle. The model still generates text, but now it operates inside a controlled loop where it can affect the outside world and react to what happens.

The cycle looks like this:

  • Assess the goal and reason about what to do.
  • Take an action, usually by calling a tool or API.
  • Observe the result of that action.
  • Reason again based on that new information.

This cycle repeats until the task is finished or the system hits a safety limit. That is the entire secret. There is no hidden reasoning engine. The magic comes from giving the model a chance to correct its own path using real data from real tools.

ReAct: The Thought-Action-Observation Cycle

The most common way to implement this loop is the ReAct pattern, which stands for Reason plus Act. On every pass through the loop, the model moves through three distinct stages.

First, the model produces a Thought. It reflects on the current situation, reminding itself of the original goal and deciding what it needs to know next. Second, it chooses an Action. This might be a web search, a database query, a calculator call, or a request to read a file. Third, it receives an Observation. The system executes the action outside the model and feeds the raw result back into the conversation history. The model then starts the next loop, using that fresh observation as its new reality.

Consider a simple example. Suppose you ask an agent to tell you whether it will rain tomorrow in Austin. The model might think, "I need the weather forecast for Austin." Its action is to call a weather API with the city name. The observation comes back as raw JSON: a temperature reading and a precipitation probability. The model then thinks again, "The forecast shows a seventy percent chance of rain," and its final action is to synthesize that into a plain English answer for you.

This structure matters because it grounds the model. If the model must issue a search and read the results before it continues, it cannot simply invent facts. The observation acts as a hard constraint on the next thought. The loop makes invention much harder because the model has to look before it speaks.

What You Need to Build a Reliable Loop

Running this pattern in production requires more than a clever prompt. You need three practical guardrails in place.

Set a step budget. Always define a maximum number of loop iterations. Without a ceiling, an agent can chase its own tail—searching, observing, reasoning, searching again—until it burns through your API budget. A step limit forces termination. You can decide whether to return a partial result, escalate to a human, or simply fail gracefully once the limit is reached.

Handle code execution yourself. The language model does not run tools. It only suggests actions, usually by outputting structured text or JSON that names a tool and provides parameters. Your code must