Enterprise codebases pose challenges for AI agents due to long-horizon tasks requiring multiple interactions. Existing multi-agent systems struggle with real-time mid-task coordination. AgentRadio, an asynchronous message-passing layer, allows agents to communicate without interrupting their work. This enables mid-course corrections in interdependent subtasks, preventing agents from pursuing incorrect paths. Tests showed AgentRadio nearly doubled task accuracy for four Claude Code agents compared to independent operation. It also outperformed single agents using more advanced models. AgentRadio highlights that effective coordination structures can be more impactful than raw compute power or larger models. The coverage problem in single-agent systems causes initial plans to become difficult to revise as context grows. Codebase understanding tasks are rarely cleanly decomposable, necessitating real-time agent coordination. AgentRadio enables this by providing agents with passive awareness of each other's progress.