Eval Engineering: The Missing Piece of Agentic AI Governance

As AI agents grow more powerful, keeping them on track is critical. Current governance solutions are too slow and expensive. The answer? Eval engineering.

Eval engineering builds validator agents that check other agents' performance. Using LLM-as-a-judge scoring, these evaluators assess accuracy, policy compliance, and task completion. The bottleneck is cost and speed in production.

Vendors like Maxim AI and Confident AI use sampling and async monitoring to reduce overhead. Galileo AI stands out with its ChainPoll methodology and Luna model, enabling 100% production sampling at low cost. Cisco is acquiring Galileo to boost Splunk's agentic monitoring.

The core takeaway: As LLMs get better, the industry must solve their growing cost and latency issues to make agentic governance viable at scale.