Observability 🔠is table stakes for any serious system. But, when you throw GenAI and LLMs into the mix it is no longer just nice to have. That’s the game at Invisible, where we’re wrangling LLMs in our multi-agent web automation platform on the daily. Let’s pop the hood and see how we made the leap from black box 🤷 to glass box🔎 with LLM Observability. Designing, building and integrating LLM observability into our stack was one of my favorite projects at Invisible.
“Without data, you’re just another person with an opinion.” ~ W. Edwards Deming
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🧠LLMs @ Invisible đź§
At the core of our platform is a multi-agent system for web automation that leans hard on LLMs. Debugging these agents and troubleshooting LLMs is not for the faint of heart ❤️. Sure, we’ve got logs, metrics, and distributed traces ✅. But when your AI starts hallucinating 🫠or goes rogue… that stuff just isn’t enough.
🕸️ Distributed Tracing 101 (And Why It Falls Short) 🕸️
Distributed tracing is awesome… for microservices. You get request paths, spans, timings, and can figure out why your API call took 3 seconds instead of 30ms. Tools like OpenTelemetry, Jaeger, and Zipkin make this easy (we use OpenTelemetry).