Why Parallel AI Agents Drift Apart and How to Keep Them Coherent

Developers running multiple AI coding agents simultaneously face a hidden coordination problem: each agent operates within its own isolated context window, with no shared memory across sessions. This causes three distinct types of drift — previously made decisions, evolving interface contracts, and overlapping file edits — none of which are visible to other agents in real time. Relying on the developer to manually relay decisions across agents works at small scale but quickly becomes a cognitive bottleneck as agent count grows. Partial solutions exist on a spectrum, from shared startup configuration files to per-task briefings, each addressing some drift types but not all. The core challenge is ensuring decisions made once automatically bind every current and future agent without requiring constant human intervention.