ReAct is an agent pattern that interleaves reasoning and actions. The model considers the current problem, chooses a tool, observes the result, and continues with updated information.
The pattern was influential because it connected language-model reasoning with external evidence and actions in one understandable loop. It reduced the need for a complete plan before any real information was gathered.
Modern tool APIs may hide private reasoning and represent actions as structured calls, but the core loop remains. Production versions need stronger controls than the original prompting pattern: typed tools, permissions, step limits, idempotency, error handling, and traces of observable actions.
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