Your AI agent works alone. No plan, no tests, no review. 118,000 developers found a fix.

A single AI coding agent can write code. But give it a big task, and it drifts. It skips tests. It forgets the plan. It says "done" when it is not. This is not a model problem. It is a workflow problem. Two open-source frameworks on GitHub found the same answer. Split the work across multiple agents. Make them check each other. Force a loop: plan, implement, review, fix. The bigger one has 118,624 stars. Why splitting the work stops the drift When one agent does everything, its context grows with every step. After 30 minutes, it loses track of the original plan. After an hour, it invents new requirements that nobody asked for. The failure patterns are predictable. The agent tries to build everything at once and the structure falls apart. It declares "done" halfway through. It marks features complete without writing tests. It leaves the environment in a broken state at the end of a session. Multi-agent orchestration fixes this by keeping each step small. A planning agent breaks the task