Your AI Agent Just Made a $50K Mistake. Can You Explain Why?

AI Agents Are Making Decisions. Nobody's Tracking Why. In March 2026, Meta had a Sev-1 incident. An AI agent posted internal data to unauthorized engineers for two hours. The scariest part wasn't the leak itself — it was that the team couldn't reconstruct why the agent decided to do it. This isn't an isolated case: A shopping agent asked to check egg prices decided to buy them instead. No one approved it. A customer support bot gave a customer a completely fabricated explanation for a billing error — with confidence. A shopping agent tasked with buying an Apple Magic Mouse bought a Logitech instead because "it was cheaper." The user never asked for the cheapest option. These aren't hypothetical risks. They're happening now. And every time, the same question comes up: "Why did the agent do that?" And every time, the same answer: "We don't know." Monitoring ≠ Forensics Here's the thing — tools like Datadog, Arize, and Langfuse are great at watching agents in real time. But when something