feat(eval): ondemand simulate — replay a dataset, evaluate synchronously - #2071
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## refactor #2071 +/- ##
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What
eval ondemand simulate— replay a dataset against a runtime, then evaluate the sessions synchronously, client-side (scores print inline). The on-demand twin ofbatch-evaluation simulate.Pipeline:
invokeDataset(replay) →getTracesForAgent(CloudWatch) →evaluate(Evaluate API).Follows the batch-evaluation simulate pattern
--ingestion-wait-ms(default 180000; 0 to skip)invokeDatasetcompositionfailures[0])sessions[](exampleId ↔ sessionId) +failures[]Differs by design: no
--name/--kms-key-arn(no async job); output is inline scores, not a job id.Output
{ "sessionsEvaluated": 1, "results": [ { "evaluatorId": "Builtin.Helpfulness", "value": 0.83, "label": "Very Helpful", "explanation": "…", "tokenUsage": {…} } ], "examplesInvoked": 1, "examplesFailed": 0, "sessions": [ { "exampleId": "greet", "sessionId": "…" } ], "failures": [] }Tests (batch pattern)
ondemand.test.tsx(TestCoreClient): required-flag validation, refuse-when-nothing-invoked,--ingestion-wait-mspassthrough.ondemand.fixture.test.tsx: real invoke → CloudWatch traces → Evaluate, recorded against a live agent, replayed offline viamatchGolden. Deterministic via the injectednewSessionIdseam plus anow()clock seam — on-demand's trace-query window is otherwiseDate.now()-based, so itsStartQueryfixture key would drift between record and replay (batch has no client query;evaluatepins an explicit--start-time/--end-time).Notes
ignoredReferenceInputFields); the handler still forwards them (adapter exercised).refactor(was stacked on the pre-merge batch-simulate work).