What is a Multi-Agent System?
A multi-agent system (MAS) is an architecture where multiple AI agents collaborate, compete, or coordinate to accomplish tasks that would be difficult or impossible for a single agent.
WHY IT MATTERS
Complex tasks often benefit from specialization. Rather than building one monolithic agent, multi-agent systems decompose work across specialized agents — a research agent, a coding agent, a financial agent — each with focused capabilities.
MAS architectures vary widely. Some use hierarchical delegation (a manager agent assigns tasks to workers). Others use peer-to-peer communication where agents negotiate and collaborate directly. Some even use competitive dynamics where agents propose solutions and the best one wins.
For financial operations, multi-agent systems introduce unique challenges. If a research agent identifies an investment opportunity and a trading agent executes it, who controls the budget? How do you prevent cascading failures when one agent's mistake triggers actions across the system?
HOW POLICYLAYER USES THIS
PolicyLayer manages spending policies across multi-agent systems — ensuring each agent has its own limits while sharing a common budget pool. Per-agent caps prevent any single agent from overspending, while fleet-level policies govern total exposure.