Why AI Agent 'Completed' Status Does Not Mean the Outcome Has Succeeded

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When an AI coding agent marks its execution as COMPLETED, that state only describes what happened to the runtime process — not whether the intended real-world outcome was achieved. A pull request generated at 10:00, for example, may not be merged until 10:17, after tests pass and a human reviewer approves it. The core architectural problem is that execution state and outcome evidence operate on separate, independent timelines. Treating a COMPLETED status as proof of success can create misleading data models, especially when confirming evidence must arrive from an external system after the execution has already ended. A more accurate approach separates execution state from outcome state, explicitly allowing for an UNKNOWN outcome until sufficient evidence is received.

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