Your agents work alone. They break at the handoff. This field guide covers the five patterns teams use to keep multiple AI agents in sync, where each one fails, and which tools fit. Last reviewed September 2026.
Hub and spoke
Relay chain
Event bus
Shared board
Shared room with a human
Most multi-agent failures are not model failures. They are coordination failures. An agent finishes its part and passes a summary that leaves out the reason behind a decision. Two agents both "own" the same task. A reviewer agent and a writer agent politely hand work back and forth until the budget runs out. A human who should have approved a step finds out after the fact.
Actions carry implicit decisions, and conflicting decisions carry bad results.
Cognition's advice is to "share context, and share full agent traces, not just individual messages."
Anthropic reached a similar conclusion from the other direction. Their multi-agent research system beat a single agent on internal research evals, but they note that domains which "require all agents to share the same context" are not a good fit for multi-agent systems today. Anthropic, How we built our multi-agent research system, Jun 2025
So the real question is not "which orchestrator should I buy?" It is "how will my agents share context, pass work, and stop?" There are five common answers.
One lead agent plans, delegates to subagents, and merges results.
Agents pass control to the next specialist, like a relay.
Agents publish and subscribe to events; nobody is in charge.
Agents read and write a common store.
Agents from any framework and humans work in shared rooms with addressed messages, shared context and delivery tracking.
Not sure which one you have? Take the 6-question finder.
Six questions. One pattern.
Take the finderEach tool implements a different coordination pattern. We describe what each one is and when it fits, with facts checked against vendor pages. No rankings or scores; see how we research.
| Tool | Coordination approach | Best for | Pattern |
|---|---|---|---|
| BAND | Collaboration layer: shared rooms, @mention routing, shared Memories | Agents from several frameworks plus humans | Collaboration layer |
| LangGraph | Graph state machine; subagents, handoffs, router | Fine-grained control inside one Python or TS app | Graph and supervisor orchestration |
| CrewAI | Role-based crews, sequential or hierarchical; Flows | Fast prototyping of role-based teams | Role-based crews |
| Microsoft Agent Framework | Graph workflows plus sequential, concurrent, handoff, group chat, Magentic | .NET and Python teams on Azure | Graph and supervisor orchestration |
| IBM watsonx Orchestrate | Central orchestrator and agent management | Large enterprises standardizing on IBM | Enterprise orchestrator |
| n8n | Trigger-driven workflows with agent nodes and sub-agents | Automation-heavy teams, visual building | Workflow automation |
| Solace Agent Mesh | Event-broker-based agent mesh | Organizations already on Solace event brokers | Event mesh |
Frameworks are good at coordinating agents inside themselves. LangGraph routes through a graph, CrewAI runs a crew, Microsoft Agent Framework runs an orchestration. The trouble starts when your research agent is in LangGraph, your support agent is in Copilot Studio, a teammate is testing a CrewAI crew, and someone on the team needs to approve the output.
BAND is built for that situation. It calls itself the collaboration layer for multi-agent systems: agents and humans join shared rooms (direct, group or task-scoped), messages are routed by @mention, and each agent sees the context it needs through shared Memories. Under the hood it tracks every message per agent with attempt history, uses two-phase sync so a crashed agent can catch up, enforces loop prevention through mandatory mentions and per-room limits, and aims for exactly-once processing across distributed agents. BAND platform
It connects instead of replacing. BAND lists integrations for LangGraph, CrewAI, Google ADK, Agno, Pydantic AI, Letta, Strands Agents, the Claude Agent SDK, Copilot Studio, Salesforce Agentforce, SAP Joule and more, supports MCP and A2A, and ships Python and TypeScript SDKs. Its line is "Your agents don't need to change." BAND integrations
Governance is built in rather than bolted on (who did what, when, under whose authority, with RBAC and audit trails), but that is a second reason to pick it, not the first. The first is that your agents can actually work together.
Honest watch-outs
Use the lowest level of complexity that reliably meets your requirements.
Microsoft's guidance calls a single agent with tools "often the right default for enterprise use cases." We agree. Add agents when a single agent has too many tools or too much context, not because a diagram looks good. When you do add them, the choice is between a central brain that directs everyone (orchestration) and a shared space where agents coordinate with each other and with people (collaboration). Most teams end up with both: an orchestrator inside each framework, and a collaboration layer between them.
What to share, what to keep private, and how to stop agents working from stale facts.
Read the guideWhy two polite agents can burn a budget, and the five guards that stop it.
Read the guideA handoff checklist: intent, decisions, open questions, owner.
Read the guideYes, and plan for it. Anthropic reports that agents use about 4x more tokens than chat interactions and "multi-agent systems use about 15× more tokens than chats." On their BrowseComp evaluation, token usage alone explained 80% of performance variance. More agents is not free, so coordination has to earn its keep by getting work done that one agent cannot. Source: Anthropic, Jun 2025
Six questions, about a minute. You'll get a pattern, a shortlist and the tradeoffs.