Decision Logging: The Observability Pattern That Actually Helps AI Agents

Most agent-debugging sessions begin with the same question: Why did the agent do that?

Why did it send a notification? Why did it suppress one? Why did it retry the same tool? Why did it act on stale data? Why did a high-confidence prediction turn out to be wrong?

Traditional application logs often show that a function ran, an API returned, or an error occurred. They may show that searchHotels completed, comparePrices ran, and sendNotification succeeded. What they often miss is the observable boundary where the system chose one path over another.

That is the problem decision logging can solve.

Decision logging is not a request to store every prompt, completion, or hidden model rationale. It is a structured record of consequential choices: which policy rule fired, which tool was proposed, why the runtime retried or refused, which source supplied a score, and what outcome was observed later.

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