Multiagent Systems
A configuration where multiple AI agents work together on a shared task — each handling a distinct role, sharing state, and passing work between each other. Multiagent systems are how complex operational workflows get automated end-to-end without forcing one agent to do everything.
What are Multiagent Systems?
A multiagent system is an architecture in which multiple AI agents operate together to accomplish tasks that no single agent could or should handle alone. Each agent has a defined role, a specific set of tools, and a scope of responsibility. Together, they can handle processes that span multiple systems, require different types of reasoning, or need to run in parallel at scale.
This is different from one AI model doing multiple things. In a true multiagent system, agents are genuinely separate — they can run concurrently, fail independently, and be replaced or upgraded without rebuilding the whole system.
How Agents Divide Work and Share State
Multiagent systems use several patterns for coordination:
Sequential handoffs: Agent A completes its step and passes the result to Agent B. Used for processes with a clear order.
Parallel execution: Multiple agents run simultaneously on different parts of a task.
Shared state: Agents read from and write to a common context or memory store so each agent knows what others have already done.
Specialist routing: A coordinating agent routes each subtask to the agent with the right capability.
Multiagent Systems in Operations
In wholesale and manufacturing operations, few meaningful processes touch only one system or require only one type of decision. A procurement cycle involves supplier data, contract terms, stock levels, approval hierarchies, and ERP entry. A multiagent approach assigns clear ownership: one agent monitors incoming invoices, another validates against POs, another checks payment terms against the contract database, another triggers the payment run or flags for review. Each agent does its job reliably. The system as a whole handles the full cycle — with audit trails at every handoff and clear escalation paths when an agent needs human input.