From Trace to Eval to Proof
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