AI will misbehave. Will you notice?
Speaker
About this event
Engineers are shipping AI features fast, often on lean teams without a dedicated AI specialist. The good news is AI is still engineering. The discipline that keeps software reliable is what keeps AI reliable too.
Running production AI well requires knowing what your AI is actually doing once it's live, and using that to keep making it better. Every feature you ship is one more thing that can fail quietly. To catch it before users do, AI observability should cover two levels: the LLM layer and the whole system around it. You need both because when something breaks, a tool that sees only one leaves you guessing about the other.
Pydantic Logfire is AI observability for production: LLM calls, costs, prompts, tool calls, and eval scores, alongside every other step that produced the response, in one view. It's built on OpenTelemetry, so the model and the rest of your stack, in Python, TypeScript, Rust, or anything OTel-compatible, land in one place.
A 45-min session, with time for your questions at the end.
What you'll learn
- Why production AI needs visibility at both the LLM layer and across the full system around it
- How traces, dashboards, and alerts surface cost, latency, and behavior across real traffic
- How offline and online evals measure whether answers are any good, before you ship and once you're live
- How a failure in production becomes a test, so the system improves as it runs
Save your spot
Register today and we'll send you the link to join, reminders, and the recording afterwards.
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