You Do Not Need a Server for Agent Evals (medium.com)

A new approach to agent evaluations has emerged that eliminates the need for dedicated server infrastructure, as researchers share their workflow to run evaluations directly from Git repositories. By storing datasets, scripts, and result files alongside the source code, users can use version control to manage changes, enhancing collaboration and reproducibility. This method involves three core components: datasets, runner scripts for executing tests, and result files, all of which can be easily maintained and reviewed using Git, thus reducing dependency on external evaluation platforms. This shift is significant for the AI/ML community as it empowers developers to leverage existing tools and practices, streamlining the evaluation process. Although building a server-based evaluation system has benefits for large-scale tests, the author emphasizes that for most evaluations—typically involving a few hundred cases—this server overhead is often unnecessary. The integration of agent-driven reviews allows users to interact with results directly in their coding environment, promoting a more flexible and efficient evaluation process. With tools like Mira that facilitate this workflow, practitioners can focus on refining their models without added complexity.