Quality Baseline Harness

Stop guessing if your agents work.
Start proving it.

Controlled cohort environments for deterministic AI agent quality measurement. Reproducible baselines. Regression detection on every release.

40%
of agentic AI projects will fail by 2027 due to inadequate evaluation
60%+
of AI-generated code requires human intervention
77.7%
of orgs use or plan AI in QA processes

Eval frameworks test prompts.
You need to test agents.

Existing tools evaluate isolated model outputs. But your agents operate in complex environments with state, tools, and multi-turn interactions. You need controlled company environments that agents run against, producing comparable results across every release.

// BaselineForge scenario
{
  "cohort": "onboarding-v3",
  "agent": "engineering",
  "fixture": {
    "company_type": "saas_startup",
    "task": "build_mvp",
    "complexity": 8
  },
  "baseline": {
    "success_rate": 0.94,
    "soft_failure": "< 0.33",
    "cost_per_task": "$0.42"
  }
}

How it works

◆

Cohort Generation

Spin up realistic company environments as controlled test fixtures. Deterministic scenarios that produce comparable measurements across agent versions.

■

Baseline Management

Track success rates, soft failure bands, and cost-per-task across releases. The Monte Carlo band model surfaces regressions before they reach production.

▲

CI/CD Gates

Gate every PR on baseline metrics. Task success rate, tool selection accuracy, faithfulness scores. No deployment without proof of quality.

The agent quality problem is a measurement problem.

If you can't measure it reproducibly, you can't improve it systematically. BaselineForge makes agent quality deterministic.