I'm exploring different patterns for agent collaboration. Is it better to use a "Manager-Worker" architecture or a "Peer-to-Peer" decentralized model for something like supply chain optimization? I need a system where a negotiator agent, a logistics agent, and a compliance agent can work together without getting stuck in infinite loops.
3 answers
For supply chain, we’ve found the "Manager-Worker" (or Orchestrator) pattern to be far superior. In a decentralized model, agents often suffer from "competing objectives"—the logistics agent wants the fastest route, but the negotiator agent wants the cheapest. A central "Manager Agent" is necessary to weigh these trade-offs and make the final call based on a global "Utility Function." We use LangGraph to define a strict state machine so that the workflow is cyclical but not infinite. This ensures that the compliance agent gets the final "veto" before any purchase order is actually executed in the ERP system.
Do you find that the "Manager Agent" becomes a bottleneck for latency? I’m worried that having every sub-task pass through a supervisor adds too many LLM calls to the workflow.
We use a "Sequential Workflow" with a final validation gate. It’s less "dynamic" than a full multi-agent swarm, but it's much easier to debug and monitor for errors.
Simplicity often wins in production. If you can define the steps linearly, you save a lot of headaches regarding agentic loops.
Steven, that is a valid concern, especially with high-volume transactions. The trick is to use a smaller, faster model (like Llama-3 8B) for the "Routing" and "Supervision" logic, and only invoke the heavy models (like GPT-4o or Claude 3.5) for the actual "Worker" tasks that require deep reasoning. This "Tiered Intelligence" approach keeps the latency down while still maintaining the centralized control you need for complex business logic. It cut our execution time by 40% without sacrificing the quality of the final logistics plan.