improve

Improve one agent profile field

improve() runs a complete optimization method against one profile field. The method receives train and selection cases. Runtime keeps the final-test cases private, compares the selected candidate with the baseline, and returns a detached candidate. It never changes the input profile.

pnpm tsx examples/improve/improve.ts

Runs offline, no credentials.

What it does, step by step

1. Runtime extracts the exact profile field selected by surface.
2. Runtime binds saved work to executionRef plus the complete baseline profile.
3. The supplied OptimizationMethod generates and selects a candidate using train and selection cases.
4. Runtime scores the baseline and candidate on the untouched final-test cases.
5. Runtime returns ship only when the paired confidence interval clears the required lift.
6. Approval and activation remain separate operations.

What you'll see

improve() proposed a detached prompt candidate and measured it on final-test scenarios
decision: ship  lift: 1.000
candidate prompt: PROMOTED
live prompt unchanged: BASELINE

The starting prompt is BASELINE; the candidate is PROMOTED. The final-test lift is 1.000.

How it stays offline

The example supplies a deterministic complete method, agent, and judge. The method returns PROMOTED; the judge scores that literal string as 1. The partition firewall, final comparison, cost receipts, and confidence interval are production code.

Going live

Replace scriptedWinner with officialGepa(...), officialSkillOpt(...), or another complete method from @tangle-network/agent-eval. The root README documents the optional Python installation for official GEPA.

Files

file	what it is
improve.ts	The profile, complete method, three partitions, agent, judge, and result

The same path is covered by src/improvement/improve.test.ts.