Closing the loop: using evals and observability to ship reliable AI products
AI product and engineering teams are shipping faster, but AI systems introduce failure modes that traditional QA and monitoring do not capture. Teams that consistently ship quality AI treat evals and production observability as a single system. Evals define release confidence, and production signals surface edge cases, regressions, and opportunities to improve.
Join us in San Francisco for an exclusive dinner to explore how teams connect evals and observability in practice so they can improve AI quality continuously, reduce operational overhead, and build trust as AI systems reach more users.
Key discussion topics:
- What to evaluate, and when - how teams define eval coverage across development stages, including pre-merge, pre-release, rollout, and post-release
- Turning production signals into eval coverage - how logs, traces, user feedback, and incidents become datasets and regression tests
- Operational patterns that work - how teams implement eval gates, release scorecards, and on-call workflows that reduce time to detect and resolve issues
- Aligning product and engineering on quality - how teams define and revisit quality, latency, cost, and safety, and make tradeoffs explicit
- Measuring impact beyond velocity - metrics teams rely on, including regression rate, time to detect, time to resolve, incident volume, and reduction in engineering overhead
This invite-only event is designed to create an intimate space for a small group of Engineering, Product, and AI leaders to discuss challenges, exchange best practices, and build meaningful connections.
We hope to see you there!
Questions? Contact [email protected]
