Dzone iconDzoneSep 28, 2026

The Agent Changed Its Plan Mid-Run: Reconciling AI Decisions With Completed Temporal Activities

Tool results expose missing facts, external systems change, policies arrive through human input, and a model may discover that an earlier assumption was wrong. ReAct-style agents were explicitly designed around this interleaving of reasoning, action, observation, and plan updates rather than one immutable plan.

The Agent Changed Its Plan Mid-Run: Reconciling AI Decisions With Completed Temporal Activities

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Useful takeaways from this story.

Tool results expose missing facts, external systems change, policies arrive through human input, and a model may discover that an earlier assumption was wrong.

ReAct-style agents were explicitly designed around this interleaving of reasoning, action, observation, and plan updates rather than one immutable plan.

The engineering difficulty begins when those actions are durable side effects.

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The useful part

Tool results expose missing facts, external systems change, policies arrive through human input, and a model may discover that an earlier assumption was wrong. ReAct-style agents were explicitly designed around this interleaving of reasoning, action, observation, and plan updates rather than one immutable plan. The engineering difficulty begins when those actions are durable side effects.

How it works

  • An AI agent rarely follows a long plan exactly as first proposed.
  • Replanning therefore has to reconcile a new decision with an already-real past.

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