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AI systems have long been divided into those that reason and those that act. The ReAct framework collapses that divide — enabling LLMs to plan, retrieve information, and course-correct in real time using a structured Thought-Action-Observation loop.

There is a version of a multi-agent AI system that works beautifully in a demo. Inputs are clean. External services respond on time. Each agent hands off to the next in the expected sequence, and the whole thing runs to completion without incident. Then there is production. In production, an API times out at step six of twelve. An agent receives a malformed tool response and has no policy for what to do next. A compliance officer asks to see the audit trail for a decision mad

As organizations explore more advanced uses of artificial intelligence, a critical distinction is becoming clear: not all AI systems...