How We Build Agent Environments & Tasks

A practical eval-engineering approach describes how to create synthetic agent environments and benchmark tasks through a two-step pipeline that first produces detailed task specifications from traces, code, and human input, then converts those specifications into runnable Harbor-format tasks. The process distinguishes task-specific specs, which define inputs, environments, and scoring rubrics, from reusable world specs containing shared domain knowledge, data schemas, scripts, service APIs, and guidance for generating realistic data and evaluations. World specs are developed iteratively while building initial tasks, using coding agents to inspect repositories, analyze production traces, map tools and credentials, and identify user patterns, with human feedback ensuring the resulting tasks reflect real-world needs. The approach supports scaling by having agents generate and review many specs and tasks, while validating environments through agent trajectories and calibrating difficulty across model tiers. Human judgment remains important for refining domain fidelity and preventing benchmarks from becoming overly easy, and the framework is intended to support continuously updated evaluations for prompt tuning, agent-harness improvements, post-training, and cost or capability analysis as production data and models evolve.

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