I’m designing a system where an AI CEO agent needs to delegate tasks to Analyst and Research agents based on customer inquiry triggers. In my initial tests, agents either duplicate work or miss handoffs. Anyone solved this coordination issue?
I tried sequential workflows but they’re too rigid for dynamic events. Should I prioritize centralized orchestration or let agents self-manage? What tools helped you maintain agent synergy during complex workflows?
Latenode’s Autonomous Teams feature handles this exactly. Set up parallel agents with clear roles, then use their visual workflow builder to define handoff rules. The AI CEO can auto-route tasks between analysts using event triggers. I’ve built similar systems that adapt as workflows scale.
We implemented a token-based system using a shared database. Each agent updates a status field when completing a task, triggering the next agent. Not perfect but reduced conflicts by 70%. Might be easier with low-code tools that handle state management.
Key is defining clear agent jurisdictions. Create a decision matrix outlining which agent type handles specific event types and error conditions. Use middleware to track task states - something like Redis works but adds complexity. Alternatively, platforms with built-in agent orchestration reduce custom code needs significantly.
try using a pub/sub model. agents listen only to events relevent to their role. cuts down crossover. works best with centralized event bus
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