A standard Large Language Model (LLM) works in a straight line: you provide a prompt, and it generates a single, continuous response. An AI agent breaks this linear model. Instead of one big leap, an agent takes many small steps, operating in a loop. This iterative process is what allows an agent to tackle complex problems that are too difficult to solve in a single pass.

The agent assesses a goal, makes a plan, executes a step, observes the outcome, and then revises its plan based on the new information. This cycle repeats until the goal is achieved. The most common framework for structuring this process is called ReAct, which stands for Reasoning and Acting. It's the core engine that drives autonomous behavior.

The ReAct framework breaks down an agent's process into a simple, powerful, and repeatable loop with three distinct phases.

1. Thought This is the agent's internal monologue. The LLM reasons about the overall goal, reviews what it has done so far, and strategizes about the best next step. It's a moment of self-reflection where the model might say, "I've found the author's name, but I still need to find their birthdate. The best way to do that is to search for them specifically."

2. Action Based on the thought, the agent chooses a specific tool and decides what input to give it. This isn't just generating text; it's a structured command. For example, the action might be search('when was Marie Curie born?'). The action is the agent's attempt to interact with the outside world to gather new information.

3. Observation This is the output from the chosen tool. The agent executes the action and receives a result, which is the observation. For the search action above, the observation might be a snippet of text: "Marie Curie was born on November 7, 1867." This new information is then fed back into the loop, starting the next "Thought" phase. The agent now knows the birthdate and can decide its next step, perhaps to find her place of birth.

We can't just tell an LLM to "start a loop." We have to guide it by structuring the prompt in a specific way. There are two main approaches.

An unconstrained loop is a recipe for disaster. It could run forever, wasting time and computational resources. To build a production-grade agent, you need reliable ways to terminate the loop.

Stop Sequences The most common method is to teach the agent to output a specific phrase when it believes the task is complete. A typical stop sequence is "Final Answer:". Your code that runs the agent loop will constantly scan the LLM's output for this phrase. When it's detected, the loop breaks, and the text following "Final Answer:" is returned to the user.

Max Iterations This is a crucial safety mechanism. You define a maximum number of cycles the loop can run, for example, 10 or 15 iterations. If the agent hasn't reached a "Final Answer" by then, the loop is forcibly terminated. This prevents the agent from getting stuck in a repetitive or unproductive cycle.

You also need to handle errors gracefully. What if the agent hallucinates a tool that doesn't exist, like predict_stock_market()? Or what if it calls a real tool with badly formatted input? In these cases, the "Observation" fed back to the agent shouldn't be a crash. It should be a helpful error message, like "Observation: Error, tool 'predict_stock_market' not found. Available tools are: [search, calculator]." This allows the agent to recognize its mistake in the next "Thought" step and correct its course.