A production AI agent can look healthy while quietly failing the exact users you hoped it would help. The logs say 200 OK. The trace says the model answered. The dashboard says latency is fine. But the customer still left the conversation without finishing the job.

That gap is the blind spot.

Most teams monitor infrastructure first: token cost, latency, model errors, retry loops, and tool failures. Those metrics matter. But they do not answer the product question that decides retention: did the agent help the user complete the intent they came with?

This guide shows how to build an AI agent blind spot detector: a practical layer that reads real conversations, finds unresolved intents, clusters repeated failures, connects them to trace evidence, and turns them into fixes your product and engineering team can actually ship.

No vendor pitch. No magic “AI analytics” promise. Just a useful architecture for builders who need their agents to get better after launch.