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Concept Fable

Reasoning-Action Paradigm

Conclusion first:ReAct is a prompting and Agent pattern that alternates reasoning and action. The model first performs short-step reasoning, chooses a tool or action, observes the result, then continues to the next round. It suits tasks requiring external information, tool calls and step-by-step decision-making. In enterprise applications, ReAct should be paired with a scratchpad, tool permissions, error handling, termination conditions and audit trails to avoid infinite loops or unauthorized actions.

ReAct · Reasoning/action pattern

Read the story first. There is no rush to give a definition here; let the question grow on its own first.

Fable

Every time the detective thinks one step, he goes out to investigate once

The detective in Fog Harbor used to like sitting in his office deducing cases. After hearing testimony, he would write long judgments on paper and finally send people to make arrests. Simple cases were fine, but complex cases were often misled by missing information.

In a jewelry theft case, the detective concluded from the maid's testimony that it was an outside boat passenger. He wrote three pages of reasoning, but did not go to the dock to check the boat, nor look at the footprints in the storeroom. When the boat passenger was stopped, the real thief had already left the city through the back door.

The detective first demanded that he think more thoroughly and write out every possibility. The paper grew thicker and thicker, but the facts were still absent. The second fix was to have the assistant check all clues at once, but many irrelevant materials piled up on the desk, which instead slowed judgment.

The old constable taught him to change rhythm: first think one step ahead and raise the doubt that most needs verification; then go out and check the evidence that one action can verify; come back, update the judgment, and then decide the next step. Reasoning and action alternate, not letting either side run too far.

The new rhythm worked in the second case. The detective first suspected the accountant, but after checking the books found the timing did not fit; he then switched to examining the mud prints at the back door and found the delivery cart tracks. Each action made the next thought more accurate.

Later, Fog Harbor had fewer wrongful cases. The detective understood that complex tasks cannot be solved by writing the full answer only in the mind, nor by blindly running around; one must think while investigating, let observation change judgment, and let judgment choose the next action.

The detective also learned to control the cost of action. Each time he went out, he only investigated the clues most likely to change his judgment, rather than turning the whole city upside down. If the evidence could not affect the next step, he held off; if the reasoning lacked a key fact, he went out at once.

Later, when his apprentice asked him the secret to solving cases, he pointed to two columns beside the case file: the left said 'what I currently believe,' the right said 'what I will verify next.' The two columns alternated repeatedly, which is what moved complex cases from guesswork to reliable conclusions. Later, when the detective trained apprentices, what he feared most was them writing out conclusions in one go. Cases will speak, but only when you go to investigate them. Each observation could overturn the previous thought, and this is exactly the rhythm complex tasks require. This rhythm kept the detective constantly correcting himself, rather than making his first guess more beautiful. Later he drew conclusions more slowly but approached the truth faster, because every step was corrected by the scene. The apprentice thus learned to write less speculation and bring back more evidence.

Reveal

This story is about: the reasoning-action paradigm

ReAct is a prompting and Agent pattern that alternates reasoning and action. The model first performs short-step reasoning, chooses a tool or action, observes the result, then continues to the next round. It suits tasks requiring external information, tool calls and step-by-step decision-making. In enterprise applications, ReAct should be paired with a scratchpad, tool permissions, error handling, termination conditions and audit trails to avoid infinite loops or unauthorized actions.

What matters is that it is not just a term, but a structural problem that repeatedly appears in real AI / Agent systems. Understanding it lets you judge when to add a model, when to change a process, and when to add governance.

Metaphor mapping

  • Pushing cases from inside the room: pure text reasoning
  • Writing long reasoning despite lacking evidence: CoT distortion without tool observation
  • Think, check once, then continue: the Reasoning-Action-Observation loop
  • Patrol log, keys, window locks: external tools and environment observation
  • Results overturn the idea: observation updates the next step of reasoning
  • Approval boundaries: tool permissions and approvals
  • Alternating trajectory: ReAct scratchpad / trace
  • Final insight: ReAct lets the Agent loop and correct between reasoning and action

Andy Ren Xu's judgment

The practical value of this concept is that it helps you break "AI features that look smart" into deliverable, verifiable, and governable units of work.

agent loopchain-of-thoughtscratchpadtool usereasoning traces

Related distinctions

This concept is easily confused with adjacent concepts; it is recommended to continue to the comparison page.

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