Deterministic evaluation

AGL 0.4 keeps ordinary test blocks fast and deterministic and adds dataset-driven eval declarations for quality and operational regressions.

eval answer_regression {
  pipeline: answer,
  dataset: "eval/answers.jsonl",
  trials: 5,
  baseline: "eval/answers.baseline.json",
  assert_schema: true,
  assert_expected: true,
  max_latency_ms: 2000,
  max_cost_usd: 0.02,
  semantic_grader: grade_answer
}

The dataset is JSON Lines. Each row contains an input object and may contain an expected value. assert_expected is the built-in exact predicate. A semantic grader names a Bool-returning pipeline and receives actual and expected inputs.

Run an evaluation with agl program.agent --eval answer_regression. A report contains pass rate plus mean, p50, p95, minimum, and maximum latency and cost. Trials use deterministic seeds, so mock handlers and retry jitter are reproducible. Registry::from_trace creates replay handlers from recorded task results without contacting a provider.

Baseline files declare min_pass_rate, max_mean_latency_ms, and max_mean_cost_usd. Any regression makes the command fail, which makes it suitable for CI. Use --update-baseline only when intentionally accepting the current report.