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What is a multi-agent system?
A multi-agent system coordinates several specialized agents — research, write, review, send — with routing, shared context, and governance. It mirrors an org chart: many roles, one company, not one generalist chat handling everything.
In depth
Single-agent demos are easy; companies are multi-role by nature. A multi-agent system assigns specialists with narrow tools and clear outputs, plus orchestration that routes work, checks quality, and escalates exceptions.
Patterns: sequential pipeline (researcher → writer → editor), manager/worker (coordinator delegates subtasks), peer review (one agent critiques another), human in the loop (approver role).
Failure modes: duplicated work, circular delegation, inconsistent shared state, and agents with overlapping permissions. Production systems constrain each role's connectors and require explicit handoff artifacts.
Neuro OS is a multi-agent system at the company layer — org chart, role routing, Ask gates, and skills — not a single super-agent with every API key.
Examples
- Outbound — research agent enriches; copy agent personalizes; human approves send
- Support — triage agent categorizes; specialist agent drafts; human closes edge cases
- CrewAI-style crew in code vs Neuro OS org chart with standing jobs
- Manager routes task to CMO vs CTO based on intent
Related terms
FAQ
Is multi-agent always better than one agent?
No. Start with one well-scoped role. Add agents when queues are distinct and ownership is clear.
How do agents share context?
Shared artifacts, databases, and governed memory — not one giant shared prompt with all secrets.
Run governed agent roles on a company OS — not only definitions in a glossary.