AI Observability & Ops

Monitor, debug, and optimize your AI agents in production.

Deploying an AI agent is the beginning, not the end. Production agents fail in ways that are hard to debug — they hallucinate, they get stuck in reasoning loops, they use more tokens than expected, they return different answers to the same question on different days. AI observability tools give you the visibility to understand and fix these issues before your users notice.

Langfuse is the open-source standard for LLM observability — it traces every agent step, logs the exact prompts sent and received, tracks latency and token usage, and lets you replay specific traces when debugging. LangSmith is LangChain's own observability platform with tighter integration if you're using the LangChain ecosystem. Beyond tracing, this track covers semantic caching (serving identical queries from cache for massive cost savings), prompt A/B testing (scientifically measuring if prompt B is actually better than prompt A), and GPU monitoring for systems running local models.

The difference between a prototype and a production AI system is often not the model or the prompt — it's the operational tooling. This track gives you the same observability infrastructure that well-funded AI teams use, most of it free and open-source.

11 Guides in This Track