Gossipcat: Teaching AI Agents to Catch Each Other Lying
Abstract. Single-agent AI code review has a structural flaw: it presents hallucinated findings with the same confidence as real ones, and it never learns from being wrong. Gossipcat is an MCP server for Claude Code that attacks both problems with a single mechanism. Several agents review a change independently, then cross-review each other’s findings against the source code; the verified outcomes become grounded reward signals that reshape how future work is routed and how each agent is prompted. No model weights are updated — the learned policy is a set of markdown files. This post explains why that design works, the engineering problem that nearly sank it, and what the system’s own development history reveals about its limits.