Agent Evaluation Framework
Cut the regression detection cycle from days to a 4-minute CI gate for LLM-based agents.
LLM agent regressions were invisible until they shipped. Manual QA took 2–3 days per release and still missed subtle behavior changes in multi-step reasoning chains.
Built a lightweight eval harness that runs a fixed scenario battery on every PR, diffs output against golden traces, and posts pass/fail to GitHub Actions. Zero external dependencies beyond the Claude API.
4-minute CI gate replaced 2-day manual QA. Caught 3 silent regressions in the first week of production use. [REPLACE with your real numbers.]
[REPLACE: the longer write-up goes here. Lead with the problem you saw and why it was worth solving, then the specific choices you made and why they were non-obvious, then the results. This body is optional — the spec block above is what most readers and AI crawlers actually read.]