A standard Large Language Model (LLM) interaction is a simple, one-shot exchange. You ask a question, and it provides an answer based on its training data. It's a powerful tool, but it's fundamentally reactive. An AI agent, however, is proactive. It doesn't just answer; it works to find the answer.

This shift from a static response to a dynamic process is powered by a reasoning loop. Instead of a single pass, the agent cycles through a sequence of thinking and acting until it reaches a goal. This allows it to tackle complex, multi-step problems that would stump a basic chatbot, like planning a trip or researching a complex topic.

The most fundamental implementation of this is the ReAct paradigm, which stands for "Reason + Act." It gives the LLM access to tools—like a web search, a calculator, or a code interpreter—and a structure for using them intelligently.

The ReAct loop operates in a clear, three-step cycle. Imagine you ask an agent: "What was the high temperature in San Francisco yesterday, and what is that in Celsius?"

A simple LLM might guess or state its knowledge is outdated. A ReAct agent, however, starts a process:

1. Thought: The agent first reasons about the goal. It thinks, "I need to find yesterday's high temperature in San Francisco. A weather API or a web search would be the right tool for this."

2. Action: Based on the thought, it executes an action. It might call a function like search("high temperature San Francisco yesterday").

3. Observation: The agent receives the output from the tool. Let's say the result is "65 degrees Fahrenheit." This is the observation.

The loop doesn't stop here. The agent re-evaluates.

Thought: "Okay, I have the temperature in Fahrenheit (65°F). Now I need to convert it to Celsius. The best tool for this is a calculator."

Action: It calls another tool: calculator("(65 - 32) * 5/9").

Observation: The result is "18.33."

Thought: "I have the temperature in both Fahrenheit and Celsius. I have fulfilled all parts of the user's request. I can now provide the final answer."

This entire internal monologue is recorded in a log often called a scratchpad. This running log allows the agent to maintain context, track what it has already done, and see the results of its past actions to inform its future ones.

A loop that never ends is a bug. An agent needs clear halting criteria to know when its job is done. Without them, it might endlessly search for information or get stuck repeating the same failed action.

Common stopping conditions include:

• Goal Completion: The agent concludes in a "Thought" step that it has all the information needed to form a final answer. This is the ideal exit.
• Maximum Iterations: To prevent runaway processes, a hard limit is set on the number of cycles (e.g., 10 or 15 loops). If the agent can't solve the problem within that limit, it stops and reports failure.
• Tool Error: If a tool repeatedly fails or returns errors, the agent might halt to avoid wasting resources.
• Stagnation: If the agent's thoughts and actions become repetitive without making progress, it can be programmed to recognize the pattern and stop.

This iterative process transforms the LLM from a zero-shot reasoner, which tries to answer everything in one go, into a deliberate, multi-step problem solver. Each loop provides new information, allowing the agent to refine its approach and build toward a solution it couldn't have reached in a single attempt.