An AI agent does more than just respond to a prompt. It operates in a cycle, much like a person figuring out a tough problem. Instead of blurting out the first answer that comes to mind, a sophisticated agent thinks, acts, and then observes the result before deciding its next move. This is the core of a reasoning loop.

The foundational pattern for this is known as ReAct, which stands for Reason and Act. It’s a framework that forces the Large Language Model (LLM) at the agent's core to verbalize its reasoning before it takes an action. This creates a transparent, step-by-step thought process that we can observe and debug.

This process unfolds in a predictable cycle: Thought, Action, and Observation.

1. Thought: The agent analyzes the goal and its current state. It forms a hypothesis about what to do next. This is where it uses a technique called Chain-of-Thought reasoning to break down the problem. The output is a private, internal monologue, like, "I need to find the capital of France. I should use the search tool for that."
2. Action: Based on its thought, the agent executes a command. This isn't just generating text; it's calling a tool, like search('capital of France') or calculate(34 * 19).
3. Observation: The agent receives the result of its action. This could be a snippet of text from a web search, a number from a calculator, or an error message if the tool failed. This new information feeds directly into the next thought cycle.

Getting an agent to reason effectively depends entirely on how you structure its instructions. The system prompt is the constitution for your agent, defining its persona, its tools, and most importantly, its reasoning framework. To elicit a good reasoning trace, your prompt needs to be explicit.

This kind of prompt engineering transforms the LLM from a simple text generator into a methodical problem solver. It forces the model to externalize its thinking process, making the agent's behavior predictable and transparent.

What happens when an action fails or the observation isn't useful? This is where more advanced patterns like reflection and self-correction come in. After an observation, a sophisticated agent can pause and critique its own performance.

A reflection step might look like this:

Observation: search('author of Moby Dick') returned a page about whales. Reflection: "My search query was too broad. It was ambiguous. I should have been more specific and included the book title to get a better result."

This self-correction allows the agent to recover from errors without human intervention. It can refine its plan, retry a failed action with different parameters, or even ask the user for clarification if it's truly stuck. This ability to analyze its own reasoning trace and adapt is what separates a simple tool-using LLM from a truly autonomous system.

Let's check your understanding of these core agent concepts.

Mastering these reasoning patterns is the key to building agents that can tackle complex, multi-step tasks in a reliable way.