The framing is a doctor watching a patient after surgery. The patient looks fine. The monitor is tracking heart rate, oxygen, and blood pressure so that something going wrong gets caught before it becomes a crisis.

AI agents fail the same quiet way. Traditional software crashes, and a crash is loud. A probabilistic system keeps answering, and the same question asked twice can come back two very different ways, so quality can slide for weeks without anything looking broken. Observability is the real-time view of how your agents are actually performing: whether answers are grounded in the right sources, whether quality is holding up, whether responses are taking longer than usual, and whether the system is consuming more tokens or calling tools more often than expected. Read together, those signals tell you whether the agent is behaving the way it was designed to. You need to know the problem to fix it, and you cannot fix what you cannot see.