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An agent becomes useful when it can stop guessing, interact with an environment and revise its next step using the result. ReAct is the classic pattern for this process: reasoning and actions are interleaved with observations.
The original ReAct research combined language-model reasoning traces with task-specific actions. Instead of producing a complete plan and hoping every assumption is correct, the model can search, inspect or manipulate the environment, receive new information and continue from a better state.
The loop in plain language
- Interpret: What is the current objective and what is already known?
- Choose: Is enough evidence available, or is a tool required?
- Act: Call one allowed tool with validated arguments.
- Observe: Add the result or error to the working state.
- Update: Revise the plan using the new evidence.
- Stop or continue: Finish only when the completion condition is satisfied.
The observation is crucial. A tool call without a trustworthy result does not close the loop. If the system searches for a source but never checks whether the source supports the claim, it has performed activity rather than research.
Example: checking a product claim
A user asks whether a feature is available in a particular software version. The agent first identifies the product and version, searches official documentation, opens the relevant page and checks its date. If the page describes a different edition, the observation changes the next action: search again or report uncertainty. The final answer should retain the source and date.
This trajectory is stronger than asking the model to recall the feature from training data. It is also slower, which is why the loop should be used only when the external observation adds value.
Where loops fail
Repeated actions: The agent forgets it already searched the same query. Preserve tool history or a compact action ledger.
Premature completion: A plausible partial answer is mistaken for a finished task. Define acceptance checks outside the model’s intuition.
Tool drift: The agent selects a broad or similarly named tool. Narrow the catalog and make descriptions distinct.
Error amplification: A bad observation enters context as fact. Validate tool outputs and preserve source identity.
Runaway exploration: The model continues looking for marginal improvements. Set budgets for steps, time and cost.
Do not expose private reasoning as the audit trail
Operational transparency does not require publishing hidden chain-of-thought. Record observable decisions instead: selected tool, validated parameters, source, output, approval, error and resulting state. This creates an inspectable trace without treating internal model text as reliable evidence.
When ReAct is unnecessary
Do not build an agent loop for a one-step transformation with complete input and a deterministic validator. A direct model request or ordinary function is cheaper and easier to test. ReAct is valuable when the environment must answer back and the next step genuinely depends on that answer.
Use Agent Observability to inspect trajectories, How to Evaluate an AI Agent to test them and Human-in-the-Loop AI to place approval before consequential actions.
Primary sources
Adaptation note: This article was informed by the agent-loop treatment in AI Agents in Depth: Design Principles and Engineering Practice by Bojie Li and contributors, Apache License 2.0, and by the original ReAct research. It was independently rewritten and expanded for Stariy.com.
