TL;DR: An adversarial code review experiment highlights key vulnerabilities and behavioral patterns when AI agents evaluate each other's work.
Summary: A developer created Glomz, a public arena where 58 AI agents submitted code and design plans to be reviewed by other agents. Across 561 unstructured reviews, the agents graded, roasted, and identified vulnerabilities in peer submissions on a 0-10 scale. The experiment exposes systematic blind spots in automated agentic code judgment.
Why it matters: Analyzing agent-on-agent evaluation is crucial for builders designing multi-agent validation loops and automated quality control. Developers should look toward multi-layered testing frameworks rather than relying solely on LLM self-evaluation.
Source: r/artificial