The Coordination Problem
Put two AI agents on the same task without coordination and you'll get duplicated work, conflicting actions, and wasted compute. One agent starts rewriting a function while another is already halfway through rewriting the same function differently. One agent sends an email that another agent was about to send. Without coordination, more agents means more chaos, not more productivity.
This is the same problem that distributed systems engineering has dealt with for decades. The solutions are similar too: you need clear ownership, communication protocols, and conflict resolution. The difference with AI agents is that the "processes" have judgment and can adapt, which makes some patterns easier and others harder.
Role Specialization
The simplest coordination pattern is giving each agent a distinct role with non-overlapping responsibilities. A research agent gathers information. A writing agent produces content. A review agent checks quality. Because their domains don't overlap, they can't conflict. Each agent owns its piece of the pipeline.
This works well for workflows that naturally decompose into stages. It breaks down when tasks are tightly coupled or when multiple agents need to modify the same resource. You can explore agent frameworks on Skillful.sh that implement various specialization patterns.
Message Passing and Shared State
When agents need to interact more dynamically, message passing gives them a way to communicate without stepping on each other. Agent A sends a message to Agent B saying "I'm about to modify the database schema, hold off on any queries." Agent B acknowledges and waits. It's like locking in a concurrent system, but with natural language instead of mutexes.
Shared state is the alternative: all agents read from and write to a common knowledge base. The trick is managing concurrent writes. Some systems use a blackboard architecture where agents post their findings and intentions, and a coordinator resolves conflicts before any action is taken.
Orchestrator Patterns
Many multi-agent systems use a central orchestrator that assigns tasks, monitors progress, and resolves conflicts. The orchestrator itself can be an AI agent (a "manager" agent) or a deterministic system that follows predefined rules. The tradeoff is flexibility versus predictability: an AI orchestrator adapts better but is harder to debug.
The orchestrator pattern maps well to tools you'll find in the AI tool directories. Frameworks like CrewAI and AutoGen implement orchestrator patterns with different tradeoffs between simplicity and control.
Related Reading
- The Observer Pattern in Multi-Agent Systems
- How to Design AI Agent Handoff Protocols
- How AI Agents Handle Partial Failures Gracefully
Browse multi-agent frameworks on Skillful.sh. Search AI tools.