Agentic Task-Environment & Harness Design

Evaluating or training a multi-step agent requires more than a static question-answer dataset: the agent operates inside an execution harness (prompt templates, observation formatting, retry loops, context compaction and tool interfaces) connected to a stateful task environment (containers, file systems, mock databases or browser sessions). Small changes in harness scaffolding or leaked environment artifacts can swing measured success rates by double digits or allow an agent to pass a check via reward hacking without solving the task. This skill designs hermetic, resettable task environments paired with independent state verifiers and instrumented harnesses so capability measurements reflect genuine task completion.

Package each evaluation task as a deterministic container or state snapshot with seeded databases, realistic distractor data and explicit permission boundaries. Write outcome verifiers that inspect ground-truth environment state (database rows, git diffs, service state and absence of forbidden side effects) after execution rather than trusting the agent self-report or brittle string matching. Hold the harness constant when comparing models, and hold the model constant when ablate-testing harness changes. Audit failed and passed trajectories for environment shortcuts—such as reading test assertions from disk or exploiting mock API defaults—and report multi-trial reliability (such as pass@k and pass^k) across clean resets.

High-fidelity task environments are expensive to author and maintain as production schemas evolve. Mock services that oversimplify latency, rate limits or partial failures can overstate real-world readiness, while overly rigid state verifiers may penalize valid alternative solution paths. Environment design must balance realism, reset speed and verifier completeness.