​Continuous evals on live traffic — how teams turn repeating failures into an improvement loop, not a scoreboard.

​For AI teams shipping agentic products.

​Most LLM eval suites go green. Then the same failure shows up again on live traffic.

​This session is about the gap between “the score passed” and “the customer stopped hitting the wall” — and a loop you can actually run: production traces → failure pattern → eval → change → re-measure on real traffic.

​We’ll cover:

• ​Why offline / pre-deploy evals lie (and when they don’t)

• ​How to turn a recurring production failure into an eval, not a ticket

• ​Continuous improvement that doesn’t stop at a PR — did the resolution rate move after merge?

​Bring a failure mode if you have one. We’ll work a concrete example end to end.

​Built by Selfship — we ingest traces, open eval-backed PRs, and re-verify the fix on live traffic. After the session: 14-day trial, no card. app.selfship.ai/sign-up