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Agentic Reasoning Loops

From Responder to Reasoner

A standard Large Language Model (LLM) works like a highly advanced autocomplete. It takes a prompt and predicts the most probable next sequence of words. This is a request-response model. You ask, it answers. But what happens when a task requires multiple steps, access to external information, or the ability to self-correct? That’s where we move from simple chains of prompts to autonomous agents.

An AI agent doesn't just respond; it acts. It can break down a complex goal into smaller steps, use tools to gather new information, and adjust its plan based on the results. The core mechanism enabling this shift is the agentic reasoning loop. Instead of just outputting a final answer, the model maintains an internal state and iterates through a cycle of thinking and acting until the goal is achieved. This transforms the LLM from a passive text predictor into an active controller of its own logic.

The key difference is autonomy. A simple LLM call is a one-off transaction. An agentic loop is a persistent process that drives towards a goal.

The ReAct Framework

One of the most influential patterns for creating these loops is called ReAct, which stands for "Reason + Act". It’s an elegant framework that prompts an LLM to generate not just an action, but also the reasoning behind that action. This process creates an explicit that we can observe, making the agent’s behaviour more transparent and debuggable.

The ReAct loop follows a simple, powerful cycle:

  1. Thought: The LLM first reasons about the task. It assesses what it knows, what it needs to find out, and what its next step should be.
  2. Act: Based on its thought, the LLM chooses an action. This action isn’t just generating text; it’s typically a command to use a tool, like performing a web search, querying a database, or running a piece of code.
  3. Observation: The agent executes the action and receives an observation back. This is the result of the tool use, such as the text from a webpage or the output of a calculation.

The agent then feeds this new observation back into the loop, starting a new "Thought" step. It continues this cycle, refining its understanding and plan with each iteration, until it determines that the original goal has been met.