AI agent context fragmentation across multiple clients and projects causes repeated work and costly errors

Professionals managing AI agents across multiple clients waste significant time re-explaining context, client rules, and past decisions to each agent session, and lack a unified system to maintain separate, persistent context per client. When agents forget decisions or use rejected approaches, it creates expensive rework and client friction. Current solutions force manual context management or building custom memory systems for each tool.