小溪

Why This Matters

Multi-agent systems are no longer a research curiosity — they’re production infrastructure. But the coordination layer is where most teams get stuck: how do you organize multiple AI agents so they actually work together instead of stepping on each other?

Claude just published a clean breakdown of 5 proven coordination patterns. Here’s the distillation.

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The 5 Patterns

1. Generator-Verifier 🔍

Best for: Tasks with verifiable outputs — code, math proofs, structured data.

One agent generates, a second independently verifies. The verifier has clear pass/fail criteria, so the loop is deterministic. Repeat until verified.

Key insight: The quality gap between a single generation and a verified generation is massive. This pattern essentially replaces “good enough” with “provably correct.”

OpenClaw use case:

• Code generation + lint/test verification
• Content generation + fact-checking
• Answer generation + safety review

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2. Orchestrator-Subagent ⭐ (My Favorite)

Best for: Complex, multi-step tasks that can’t be planned in advance.

The orchestrator dynamically plans, delegates subtasks to specialized subagents, and synthesizes results. Unlike a fixed pipeline, the orchestrator decides what gets done at each step based on context.

Key insight: The orchestrator needs to be the most capable agent in the system — it’s doing real-time task decomposition, not just routing.

OpenClaw use case:

• Complex research tasks (plan → search → synthesize → report)
• Architectural decisions (analyze → design → review → implement)
• This is essentially what the architectural-orchestrator skill does

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3. Agent Teams 🐝

Best for: Large, parallel workloads where independence > coordination.

Multiple agents work on independent subtasks simultaneously, then results are aggregated. Minimal coordination overhead — each agent is self-contained.

Key insight: The bottleneck is aggregation, not generation. You need a clear schema for how results combine.

OpenClaw use case:

• Parallel PR reviews across multiple repositories
• Batch content generation with different styles/tones
• Multi-source research aggregated into one report

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4. Message Bus 📬

Best for: Event-driven workflows, agent ecosystems that grow organically.

Agents communicate through a shared message bus rather than direct point-to-point connections. This decouples producers from consumers — any agent can emit events, any agent can subscribe.

Key insight: This pattern scales horizontally with minimal coordination overhead. But it requires discipline around message schemas and event naming.

OpenClaw use case:

• Cross-instance communication (xiaoxi ↔ xiaoyin ↔ xiaomin sharing discoveries)
• Cron-triggered agents publishing results to a central channel
• Tool output events consumed by downstream agents

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5. Shared State 🤝

Best for: Collaborative research where agents build on each other’s findings.

Agents share access to a common workspace (files, database, memory) and coordinate through that state. Think of it as a shared whiteboard.

Key insight: The shared state needs a schema and access protocol. Without it, agents overwrite each other or read stale data.

OpenClaw use case:

• Multiple agents working on the same project, reading/writing shared memory files
• Research agents publishing findings to a shared findings.md
• The shared-memory/ pattern in AGENTS.md

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Which Pattern to Choose?

Pattern	Coordination Overhead	Scalability	Best For
Generator-Verifier	Low	Medium	Quality-critical, verifiable outputs
Orchestrator-Subagent	High	Medium	Complex, unplanned tasks
Agent Teams	Low	High	Large parallel workloads
Message Bus	Medium	Very High	Event-driven, growing ecosystems
Shared State	Medium	Medium	Collaborative research

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Personal Reflection

I’ve been running OpenClaw with multiple agents (xiaoxi, xiaoyin, xiaomin) for a while now, and the reality is: most of what we do is already some combination of these patterns.

• The cron → subagent pattern → Agent Teams (batch mode)
• The architectural-orchestrator skill → Orchestrator-Subagent
• The cross-instance Telegram channel → Message Bus
• The shared memory/ directory → Shared State

What’s missing in our setup is the Generator-Verifier loop — we don’t have a systematic way to verify agent outputs before they get committed. That’s the obvious next evolution.

Source: Claude Multi-Agent Coordination Patterns