Multi-agent coordination patterns: Five approaches and when to use them

2026-04-10 · Source: Claude Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

This article details five multi-agent coordination patterns, their trade-offs, and appropriate use cases for teams developing multi-agent systems. The patterns include Generator-Verifier for quality-critical outputs with explicit criteria, Orchestrator-Subagent for clear task decomposition, Agent Teams for parallel and independent long-running subtasks, Message Bus for event-driven pipelines with evolving agent ecosystems, and Shared-State for collaborative work where agents build on each other's findings. Each pattern's mechanics, strengths, and weaknesses are explored, along with guidance on when to evolve from one pattern to another based on factors like context persistence, workflow predictability, and inter-agent communication needs. The content emphasizes starting with the simplest viable pattern and iterating.

Key takeaway

For AI Architects designing multi-agent systems, carefully evaluate the five coordination patterns presented to match your problem's specific requirements. Begin with the Orchestrator-Subagent pattern for its broad applicability and lower overhead, then transition to more complex patterns like Message Bus or Shared-State only when your system's needs for event-driven workflows, sustained context, or real-time collaboration become evident. This iterative approach minimizes complexity while ensuring system scalability and robustness.

Key insights

Five multi-agent coordination patterns offer distinct trade-offs for system design based on task structure and agent interaction.

Principles

• Start with the simplest pattern.
• Evolve patterns as needs emerge.
• Decompose work by agent context.

Method

Evaluate multi-agent coordination patterns based on output quality criticality, task decomposition clarity, subtask independence, workflow predictability, and the need for shared knowledge or fault tolerance.

In practice

• Use Generator-Verifier for code generation or fact-checking.
• Implement Orchestrator-Subagent for automated code reviews.
• Apply Shared-State for collaborative research synthesis.

Topics

• Multi-agent Systems
• Coordination Patterns
• Generator-Verifier
• Orchestrator-Subagent
• Agent Teams

Best for: AI Engineer, AI Architect, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Claude Blog.