The Evaluation Factory: How We Make Agents Measurably Better, Run After Run

Komodor describes an “Evaluation Factory” for improving production AI agents through measured, controlled experimentation rather than informal testing, with the aim of balancing accuracy in high-risk tasks against cost in routine work. It argues that agents are harder to evaluate than conventional software because their outputs, tool-use paths, and real-world side effects can vary between runs, allowing early errors to compound while still producing plausible conclusions. The proposed workflow versions each hypothesis-driven change, tests candidates through golden datasets, benchmarks, and non-acting shadow runs, analyzes traces and costs, and uses a constrained coaching agent to generate and test variants without allowing it to alter grading, access held-out tests, or deploy changes. Evaluation combines automated checks and LLM judges with expert review and confirmed real-world outcomes, while separating development, regression, and held-out test cases to limit overfitting and pinning evaluator versions for fair comparisons. Human owners retain control over goals, budgets, evaluator acceptance, and final promotion decisions, receiving reports on improvements, regressions, and costs. Komodor plans to validate the approach first on bounded SRE and coding benchmarks, then extend it to production-driven optimization and customer-built agents.

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