You Can’t Improve AI Agents If You Don’t Measure Them
Last Updated on February 17, 2026 by Editorial Team
Author(s): Gowtham Boyina
Originally published on Towards AI.
This agent-eval Framework Runs Controlled Experiments on Your Codebase
I’ve watched teams add MCP servers to their projects, rewrite documentation for AI agents, or switch from Sonnet to Opus — and then have no idea whether it actually helped. They ship changes based on vibes: “The AI seems better at generating components now.” Maybe it is. Maybe it’s placebo. You can’t tell without measurement.
The article discusses the need for a structured evaluation framework for AI coding agents. It introduces the open-source tool agent-eval, which enables developers to conduct controlled experiments on their codebases, facilitating better measurement of AI performance and aiding decision-making for enhancements to coding agents. The framework addresses challenges such as infrastructure requirements, controlled trials, and the differentiation of failures, ultimately providing a way for teams to improve agent performance through data-driven approaches.
Read the full blog for free on Medium.
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