You've built the demo. It works. The LLM responds, the tools fire, the output looks great. Then you push it to production — and everything breaks. API calls fail with no retry logic. Identical queries hammer your LLM endpoint ten times per minute, burning through credits. A single bad response cascades into an agent loop. You have no idea what's happening inside because there's nothing to look at. So you start writing infrastructure. A retry decorator here. A cache manager there. A circuit-breaker wrapper you found on Stack Overflow. Eighty lines of boilerplate — just to make one tool call production-safe. This is the problem ToolOps was built to solve. The Production Gap Nobody Talks About Building AI agents has never been easier. Frameworks like LangChain, CrewAI, and LlamaIndex get you from idea to working prototype in an afternoon. But moving that prototype to production exposes a gap that frameworks don't fill: the reliability, cost, and observability layer that every real agent needs.…