Know when your agents break before your users do.
BaselineForge runs automated quality baselines across cohorts of AI agents. Define what good looks like. Measure every run. Catch regressions at the population level.
Individual evals don't catch systemic failures.
Existing tools grade single agent runs. But quality degrades across populations, not in isolation. A model update that passes every unit test can still tank your 95th percentile.
Non-deterministic drift
Same inputs, different outputs across runs. Without population-level measurement, you can't tell noise from regression.
Silent degradation
Quality erodes 2% per deploy. Each change passes individually. After ten deploys, your agent is measurably worse.
Cascading failures
A bad tool call in step 1 compounds through step 5. Point-in-time checks miss the chain reaction.
No golden standard
Without versioned baselines, "did quality improve?" is a guess. You need a reference point, not a feeling.
Three layers. Full coverage.
Define baseline scenarios
Version-controlled JSON scenarios that define inputs, expected behaviors, and scoring rubrics for each agent type. What good looks like, in code.
Run cohort evaluations
Execute scenarios across agent populations. Not one run, but hundreds. Measure distribution, percentiles, and variance. Statistical significance, not anecdotes.
Gate on regressions
CI/CD integration blocks deploys when quality metrics drop below baseline thresholds. Deterministic checks plus LLM-as-judge scoring. No regressions ship.
// Define what "good" looks like { "agent": "onboarding-agent", "cohort_size": 50, "scenarios": [ { "name": "company-setup", "graders": ["tool_accuracy", "response_quality"], "threshold": 0.92 } ], "regression_gate": true }
Unit tests check one agent. We check the population.
Cohort-level, not instance-level
DeepEval, Braintrust, LangSmith, Promptfoo. Great tools. All designed to evaluate individual agent runs or prompts. None of them answer: "across 500 automated runs this week, did my agent get better or worse?" BaselineForge does. Population-level quality measurement with statistical rigor, automated regression gates, and versioned golden baselines.
Ship agents with confidence, not hope.
Quality is infrastructure. Not something you check once and forget. BaselineForge makes continuous quality measurement as automatic as CI/CD made continuous deployment.