A single agent can answer a question. A swarm of agents can run a research department.
AI swarms are groups of specialized agents that coordinate on tasks too complex for any single model. One researches. One writes. One verifies. One synthesizes. The work happens in parallel, with agents handing off context like colleagues in a well-run team.
This is how intelligence scales.
What Is an AI Swarm?
An AI swarm is multiple AI agents working together on a shared goal. Each agent has a specialty. The swarm has a coordinator - sometimes a lead agent, sometimes a protocol, sometimes the Agent OS itself.
The concept isn't new. It's borrowed from nature.
Ant colonies
No ant understands the colony. Each follows simple rules. The colony solves complex problems - food sourcing, defense, construction.
Immune system
T cells, B cells, macrophages. Each specialized. Together, they handle threats no single cell type could manage.
Agent swarm
Research agent, writing agent, review agent. Each specialized. Together, they complete work no single model could handle.
The key insight: specialization plus coordination beats general-purpose intelligence. A team of focused agents outperforms one agent trying to do everything.
Three Swarm Patterns
Not all swarms work the same way. Three coordination patterns have emerged:
Centralized
One lead, many workers
A manager agent receives the task, breaks it into sub-tasks, delegates to specialist agents, and synthesizes their outputs. Simple to reason about. The manager sees the full picture. The trade-off: if the manager makes a bad plan, everything downstream suffers.
Example: A research lead agent receives "analyze the EV market." It spawns sub-agents for market data, competitor analysis, technology trends, and regulatory landscape. Each reports back. The lead synthesizes a final report.
Hierarchical
Delegation chains
Like a company org chart. A top-level agent delegates to mid-level agents, which delegate further to specialist agents. Each level adds context and refinement. More resilient than centralized - no single point of failure - but harder to debug when something goes wrong at a deep level.
Example: A content director agent delegates to a research manager and a writing manager. The research manager spawns agents for web search, academic papers, and data analysis. The writing manager spawns agents for drafting, editing, and formatting. Results flow up the chain.
Peer-to-Peer
Agents negotiate directly
No central coordinator. Agents communicate with each other, share findings, and self-organize around the task. Most resilient pattern - lose any one agent and the swarm continues. Also the hardest to predict. Behavior emerges from interactions rather than following a plan.
Example: A group of review agents each independently evaluate a document. They share critiques with each other, debate disagreements, and converge on a consensus assessment without a manager deciding the outcome.
In practice, the most effective swarms use a hybrid approach - a high-level planner with hierarchical delegation and some peer-to-peer communication between specialists when they need to resolve conflicts or share discoveries.
Swarm vs. Single Agent
Not every task needs a swarm. The decision is straightforward:
Focused tasks
"Summarize this article." "Draft a reply to this email." "Convert this CSV to a chart." One skill, one input, one output.
Complex tasks
"Research the EV market and write a report with data visualizations." Multiple skills, multiple sources, multiple outputs that need synthesis.
The rule of thumb: if the task requires more than one type of expertise, or if the output is better when multiple perspectives contribute, use a swarm.
- Single "Answer this question" -> Swarm: "Research this topic across 50 sources and synthesize findings"
- Single "Draft an email" -> Swarm: "Triage my inbox, draft responses, and flag what needs my attention"
- Single "Check this code" -> Swarm: "Generate a hypothesis, search literature, verify mathematically, and write a paper"
Swarms in Practice
This isn't theoretical. Agent swarms are running today in Rush.
Each of these agents looks like one thing from the outside. Underneath, it's a coordinated team. You say "research quantum computing." Seven agents fan out, gather, verify, and synthesize. You get a report. The swarm is invisible.
The best swarms are the ones you don't notice. You state a goal. Agents coordinate. An artifact appears.
What Swarms Need
Agent swarms can't run on chat interfaces. They need infrastructure:
- Context passing - The research agent's findings need to reach the writing agent without the user copying and pasting between windows.
- Parallel execution - Agents working simultaneously, not sequentially. A swarm that runs one agent at a time is just a slow pipeline.
- Delegation protocols - A lead agent needs to spawn sub-agents, assign tasks, and collect results. This requires a runtime that supports agent-to-agent communication.
- Result aggregation - Multiple agents produce multiple outputs. Something needs to synthesize them into a coherent artifact.
- Permissions - Each agent in the swarm needs different access. The email agent reads your inbox. The research agent browses the web. Neither should access what the other does.
This is why swarms need an Agent OS. The same way parallel processes need an operating system to manage shared memory and scheduling, parallel agents need an Agent OS to manage shared context and coordination.
The Future Is Swarming
Single-agent AI is a chatbot. Multi-agent AI is a workforce.
The trajectory is clear. Models get cheaper every quarter. Specialized agents get better at their niches. Coordination protocols mature. The result: swarms that handle increasingly complex work, invisibly, on your behalf.
The question isn't whether swarms will become the default way AI works. It's whether you'll have an OS that lets you use them - or whether you'll still be copying and pasting between chat windows.