Coordination Protocol

When you have multiple AI agents working on the same problem, they need to coordinate. Without protocols, they're like people in a meeting who don't listen to each other and keep repeating the same suggestions. Coordination protocols define how agents communicate, what they communicate about, and how they resolve conflicts.

Simple coordination might be sequential. Agent A completes a task, hands off to Agent B, which completes the next task. More sophisticated coordination is parallel, where multiple agents work simultaneously, sharing state and adjusting their actions based on what others are doing. The most complex coordination is hierarchical, where some agents are leaders directing others, or organizational, where agents have specialized roles.

The protocol specifies the message format. What information does Agent B need to know from Agent A to do its job effectively? Do agents need to share intermediate reasoning, or just final outputs? In a supply chain optimization system, the procurement agent needs to know what the inventory agent has decided about stock levels. The logistics agent needs to know about both. They can't all make decisions independently and hope things work out.

Conflict resolution is critical. If Agent A wants to spend 0,000 on a supplier and Agent B wants to spend 2,000 with a different supplier, what happens? Do they negotiate? Does one defer to the other? Is there a arbiter? Different protocols have different mechanisms. Some use voting. Some use priority ordering (this agent always wins disputes). Some use game-theoretic approaches where agents are incentivized to cooperate.

Transparency is essential. If agents are making decisions based on incomplete information about what other agents are doing, the system breaks down. The protocol needs to ensure agents have enough shared context to make good decisions. This is harder than it sounds when agents are also trying to optimize their own metrics.

There's also the question of consistency. If agents are updating shared state (a knowledge base, a task queue), you need guarantees that updates don't conflict or overwrite each other. You might implement consensus protocols borrowed from distributed systems where agents must agree on state changes.

Some coordination protocols are symmetrical (all agents are peers). Others are asymmetrical (some agents are more authoritative). The choice depends on your problem domain. A customer service system might have an intake agent that routes to specialized agents (returns, billing, technical support), creating a hub-and-spoke pattern. A research system might have agents that communicate peer-to-peer as they explore different hypotheses.

Real systems also implement timeout and escalation protocols. If Agent A is waiting for Agent B's response for too long, what's the fallback? Does Agent A make a decision without that information? Does it escalate to a human? The protocol needs to handle these failure modes.