TL;DR Once you start running multiple AI coding agents, coordination eventually becomes its own bottleneck. For long-running maintenance workflows, the cost of conversations, context, and tokens starts to add up. We call the pattern Gotanda Style : agents do not talk to each other directly. Instead, they leave small structured signals in a shared environment. Other agents read those signals later. This is a lighter introduction to a workflow we use on a roughly 200,000-line Python repository, connecting Sentry alerts to GitHub issues and improvement pull requests. Are we making AI agents attend too many meetings? If you are building with AI coding agents, you will probably hit the coordination problem sooner or later. When people design a multi-agent system, the first idea is often: "let the agents talk to each other." A Planner discusses the plan. A Researcher investigates. A Coder implements. A Reviewer comments. A Supervisor keeps the whole thing on track. That feels natural.…