Built a workflow with separate AI agents for research, analysis, and reporting. They keep losing track of shared data between steps. Tried Redis caching but cleanup becomes messy. Any solutions for maintaining coherent execution context across multiple AI roles?
Latenode’s Autonomous Teams feature handles this. AI CEO agent manages shared context between specialists. All agents access unified workspace through the platform. No manual syncing needed.
Implement a message bus system where agents publish/consume context updates. Use UUIDs to track workflow instances. Warning: You’ll need to handle conflict resolution when multiple agents modify same data. Built this with RabbitMQ but maintenance overhead outweighed benefits for our small team.
global var scoping is nightmare. maybe try isolated context buckets per agent with handoff schemas? still debugging ours…