What is the best way to handle communication between multiple AI agents in a single workflow?

All answers to this question.

To stop infinite loops, you need to implement a "State Controller" or a "Max Iterations" hard cap. In frameworks like CrewAI or AutoGen, you can designate a specific "Manager Agent" whose only job is to oversee the handoffs. This manager doesn't do the work but decides if the "Definition of Done" has been met. If the reviewer and coder have gone back and forth three times without the loss function or error count decreasing, the manager should intervene and either escalate to a human or pick the best version and move on. This structural oversight is key to production-grade MAS.

   Answered 2025-09-22 by Melissa Wagner

Have you considered using a "hierarchical" vs a "sequential" process? Sometimes giving the agents a shared "blackboard" memory helps them stay aligned better than direct messaging.

   Answered 2025-09-23 by Ryan Cooper

• Ryan, the blackboard approach sounds interesting, but doesn't that increase the risk of the context window filling up too fast? Matthew, a simpler fix is to give the Reviewer Agent a strict "Stop Signal" keyword. If the code passes 90% of the unit tests, the Reviewer must output "FINALIZE" which the system recognizes as the termination trigger.

     Commented 2025-09-24 by Scott Peterson

I usually just set a strict budget for tokens or a counter. If the agents take more than 5 turns, the system automatically shuts down the task and sends the current log to my dashboard.

   Answered 2025-09-25 by Laura Higgins

• Same here, Laura. Safety first! I also find that giving the Reviewer a more critical "persona" helps it give better feedback the first time, which usually avoids the loop altogether.

     Commented 2025-09-26 by Matthew Brennan