Microsoft researchers found that accumulated chat context can keep long-running AI agents focused on similar solutions. Their Fork-and-Flush method, which branches the agent and clears chat context while preserving the workspace, raised normalized average scores by 66.0 percent relative to single runs on the same compute budget.



