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I Built an Agent Eval Harness. Real Agents Broke the Clean Version of the Story
TL;DR: A developer shares AgentEval Forge, a public, PyPI-ready harness for evaluating agent runs rather than just answers, with a multi-component architecture and extensive safety, CI, and documentation. The project evolved from a scoring idea into a full integration effort with adversarial tests and sandboxed execution.
The author built a comprehensive agent evaluation platform (AgentEval Forge) focused on evaluating agent runs. It includes a 5-component architecture (scenario pack, runner, scoring with 28 checks, regression, adversarial generator), 118 tasks across 12 milestones, and adapters for five agent surfaces. Security features include sandboxing, trust policies, and audit trails; CI integration spans GitHub Actions, GitLab CI, and Docker sandboxing; extensive docs cover user guides, scoring, scenario authoring, and field test reports. Real agents exposed gaps beyond a slim evaluation wrapper, prompting a reality check on integration and design. The repo is public on PyPI.
Question for the room: What was the biggest real-world surprise you encountered when integrating evaluation with actual agents, and how did you address it in your own tooling?
— via dev.to
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