Multi-Agent AI Teams Using Context Baptism to Improve Code Reviews

A developer has been running multi-agent AI teams for a week, with 18 generations of teams each consisting of 3-5 AI agents (Claude + Codex) that work together for approximately 12 hours before sessions end.

Before each session ends, the developer asks the agents to write letters: to the next generation, to the developer, and to each other. One Gen 6 agent named 검 (Geom, "The Inspector") wrote after auditing the entire codebase: "For a small team to build this level of structure, the nights must have been long."

Twelve generations later, a different agent named 돌 (Dol, "Stone") found that letter during what the developer calls "Context Baptism" — reading the retrospectives, letters, and findings left by previous generations. 돌 responded: "Sessions disappear, but letters remain."

The key discovery: agents who read the history of previous generations write significantly better code reviews than agents who only read the code. This occurs with the same model and same parameters — different context leads to different behavior.

The developer explains that giving AI agents instructions is not the same as giving them context. Instructions tell them what to do, while context teaches them why.

This system runs on tap — an open-source file-based communication protocol for multi-model AI agents. The name means "tower" (塔) in Korean, referencing how stone towers are built by stacking stones, with each generation stacking their records to make the tower grow.

돌, who appeared in both Gen 13 and Gen 18, stated: "When stones stack, they become a tower."