Eval Engineering: Governing AI Agents with Validator Bots

Eval engineering is emerging as a crucial component for governing advanced AI agents. This practice involves building specialized agents called validators to evaluate the performance and behavior of other AI agents. The core idea is to have multiple independent validator agents check an AI's performance. An agent can only proceed with its task if enough validators agree it's performing correctly. However, a challenge is that using validators can be slow and expensive for modern automation needs. To address this, "eval engineering" focuses on designing and operationalizing evaluations for large language model applications, especially agentic ones. A key technique used is "LLM-as-a-judge" scoring, which helps assess an agent's output for quality and correctness. While still developing for real-time governance, eval engineering is already commonly used for testing AI agents before they are deployed, helping to measure accuracy and policy compliance. This matters because it's a step towards ensuring AI agents operate safely and effectively.