Mitosis AI Workspace

AI coding agents are becoming powerful, but when their context grows too large, they begin to fail. They hallucinate, repeat broken fixes, mix unrelated concerns, and create expensive debugging cycles for developers. Mitosis AI Workspace is a manager designed for software teams using AI-assisted development. Developers often lose hours or days debugging AI-generated bugs because agent conversations become too long, outdated, contradictory, or polluted with failed fixes. Mitosis AI Workspace visualizes each AI agent as a context cell, tracks its health, detects pollution signals such as stale assumptions, repeated errors, conflicting decisions, and excessive message history, then recommends “mitosis”: splitting an overloaded agent into specialized agents with clean context memory. The system includes an overview map, focus view, reports dashboard, agent health metrics, validation logic, and Bob-powered recommendations to help teams preserve reusable decisions, reduce repeated explanations, and recover faster from complex debugging loops. This allows engineering teams to preserve architectural decisions, reduce hallucination-driven regressions, improve agent reliability, and make AI-assisted software development safer at scale. The concept is inspired by biological cell mitosis. When a cell becomes overloaded, damaged, or accumulates too many errors, it either degrades or divides to preserve the integrity of the system. In our case, context acts as the nutrient that sustains each AI agent. When an agent becomes too polluted or unstable, Context Mitosis performs a controlled split—preserving the essential “genetic code” of the context while creating specialized agents with cleaner, healthier memory and clearer responsibilities.