You know that feeling when your OpenAI agent starts behaving weirdly at 3 AM and you have no idea what went wrong? Yeah, that's what we're fixing today. Most teams focus on token usage and API costs when monitoring their agents. Sure, those matter. But if you're running agents in production handling real requests, you need visibility into what's actually happening under the hood—the reasoning loops, the tool calls that failed silently, the hallucinations that almost made it to your users. The Gap in Standard Monitoring OpenAI's SDK gives you basic telemetry, but it's like having a car dashboard that only shows fuel and RPM. When your agent loops infinitely or makes a series of bad decisions, you're flying blind. Here's what most production setups miss: Agent state transitions : Did your agent actually complete its task or give up? Tool execution patterns : Which tools are your agents overusing or ignoring?…