I learned this the hard way. A pipeline processing 10,000 job listings daily, scoring each one with GPT-4 function calling, and it was a black box. When something broke, I had no idea what broke, why it broke, or how much it cost while it was breaking.
That's not a production system. That's a prototype running on production infrastructure.
The difference between a demo and a reliable AI agent isn't better prompts or a fancier model. It's observability. Logging, cost tracking, error monitoring, and latency metrics turned that fragile pipeline into something I could trust. Here's exactly what I did and why.
The Problem: An LLM Pipeline With No Dashboard
The system scored 10,000+ job listings daily using GPT-4 function calling. Each listing went through a multi-step pipeline: fetch from source, normalize the data, call the LLM for scoring, parse the structured output, and store the result.
