A standard large language model (LLM) is like a brilliant but lazy consultant. You give it a prompt, it gives you a single, well-crafted response, and then it waits. This one-shot interaction is powerful, but it's not autonomous. The model doesn't check its work, use tools, or adapt its plan based on new information.

To build something that can tackle complex, multi-step problems, we need to move from a linear conversation to a continuous cycle. This is the core idea behind AI agents. Instead of just responding, an agent operates in a loop, constantly assessing its situation, thinking about what to do next, and then taking action.

This cycle, often called the perception-reasoning-action loop, mimics how humans solve problems. First, you observe the state of the world (perception). Then, you figure out what your observation means and what you should do about it (reasoning). Finally, you do it (action). The result of your action changes the world, creating a new observation and starting the loop all over again. This iterative process allows an agent to build on its progress, correct mistakes, and navigate unforeseen obstacles on its way to completing a goal.

The agentic loop is a great concept, but how do you actually implement it with an LLM? One of the most effective and popular patterns is called ReAct, which stands for "Reason" and "Act." It provides a simple, structured way to get a model to perform both parts of the loop within a single generation.

Instead of just asking the LLM for the final answer, you prompt it to follow a specific format. It must first generate a thought, which is its reasoning about the problem and its plan. Then, it must generate an action, which is a concrete step it can take, like using a specific tool. This forces the LLM to show its work, creating what's known as a reasoning trace that is both human-readable and machine-parseable.

Let's see how it works. Imagine you ask an agent: "What was the score of the last game played by the winner of the 2023 Super Bowl?" A ReAct agent wouldn't just answer. It would loop through a series of thoughts and actions.

Each "Action" is executed by the system, and the result is fed back as the next "Observation." This creates a powerful self-correction loop. If the first search had failed, the agent's next thought would have been about how to fix its query, rather than just giving up.

This might sound similar to Chain of Thought (CoT) prompting, where you ask a model to "think step-by-step." And it is! ReAct is an evolution of CoT. The key difference lies in the interaction with the outside world.

Chain of Thought is pure reasoning. The LLM lays out its logical steps to arrive at an answer based on the knowledge it already has. It's a closed-loop monologue. Agentic reasoning using ReAct is an open-loop dialogue. The agent thinks, acts, and then gets new information from its actions. It can use tools, search the web, or query a database, constantly updating its understanding of the problem.

Think of it this way: CoT is like a student solving a math problem on paper, showing their work. ReAct is like a scientist in a lab, forming a hypothesis (thought), running an experiment (action), and analyzing the results (observation) before planning the next experiment. The ability to act and observe makes all the difference for solving real-world problems.

By engineering prompts that require the ReAct format, you give the LLM a scaffold for complex reasoning. You are no longer just asking for an answer. You are providing a protocol for how to find the answer, empowering the model to decompose big goals into small, executable steps and to correct itself along the way.

This structure is the foundation for building capable AI agents that can do much more than just chat.