Your AI Agent Looks Fine in Staging. Production Is a Different Story.

I've spent 15+ years building enterprise security infrastructure. SSO, SCIM provisioning, zero-trust networking, AI-powered threat detection. The kind of systems where a failure at 2 AM means someone's getting paged and something important is broken. Over the past year, I've watched a pattern repeat itself across engineering teams building with AI agents. The pattern goes like this: agent works great in development, passes all the evals, gets shipped to production, and then quietly starts doing things nobody expected. Not crashing. Not throwing errors. Just... drifting. The problem nobody talks about Traditional monitoring tools are designed for deterministic systems. A request comes in, code executes, a response goes out. If something breaks, you get a stack trace. You know exactly what happened and where. AI agents don't work that way. They make decisions. They chain together multiple LLM calls, pick tools, reason through multi-step workflows, and produce outputs that can vary every