Adapt it to the work
Test changes against real past cases
Review a changed agent beside representative recorded runs before trusting the new release.
Compare a draft with representative completed sessions without replaying their external tool effects.
What this changes for your team.
Golden evals turn a finished session into a reusable comparison case. Yekar.AI replays its turns against the current draft, answers tool calls from the original recording instead of touching live systems, and produces a side-by-side report. The report deliberately leaves the verdict to a person because model output is nondeterministic and a textual difference is not automatically a regression.
How it works in practice.
- 01
Mark a representative finished agent session as a golden case.
- 02
Replay the current draft using the recorded tool responses, without re-executing those external calls.
- 03
Compare replies, tool sequences, token use, and model rounds side by side before deciding whether to publish.
What you can plan around.
The behaviour you can design against, stated concretely.
Golden cases are durable references to recorded sessions and can be replayed against later drafts.
Recorded tool responses are reused during a replay; an unmatched new call is reported as a divergence and is not executed.
Replay reports show a comparison rather than an automatic pass or fail verdict, so a person interprets meaningful changes.
Bring one real process
See how Yekar.AI fits the way you work.
Start with a job your team already owns, plus the tools and decisions around it.
Talk to us