Towards Automating Eval Engineering

Eval Engineering Skill is a newly launched tool designed to help coding agents build evaluations by utilizing context from a repository and analyzing agent traces. The skill systematically inspects the structure of an agent, identifies patterns from available traces, and proposes abilities to test, while involving user feedback to iteratively approve each evaluation. It generates executable evaluations in Harbor format, which includes a task instruction, an environment setup defined by a Dockerfile, and a verifier to assess task completion. The process begins with mapping the agent's components such as prompts and tools, and understanding data and services that influence its behavior. Users can guide the eval creation by selecting which tools should operate live or be simulated, especially for cost-incurring tasks. The iterative design process allows for refining verifiers based on the agent's trajectory and verifier's reasoning to prevent reward hacking, ensuring evaluations measure the intended capabilities accurately. Containerized evals facilitate rapid experimentation by maintaining stable environments even as agent configurations change, allowing parallel testing and direct comparison of results against fixed targets. This skill is part of the langchain-ai/langchain-skills repository and aims to streamline the building of evaluations, enabling continuous improvement of agent capabilities through reproducible and representative testing environments.

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