Building Agent Swarms
Multiple agents working in parallel can compress hours of work into minutes. This guide covers architecture patterns, communication, and real-world examples.
What Are Agent Swarms?
An agent swarm is a group of AI agents working on the same goal in parallel or in a coordinated sequence. Each agent has a defined role, its own context window, and produces structured output that other agents can consume.
The key benefit is parallelism. A code review that takes one agent 90 seconds can be done in 30 seconds by three agents reviewing in parallel — security, logic, and test coverage simultaneously.
Use a swarm when...
- • The task has parallel-safe subtasks
- • Different subtasks need different expertise
- • Speed matters and tasks are independent
- • You want multiple perspectives on the same input
Use a single agent when...
- • Tasks are strictly sequential
- • State must be shared and mutable
- • Token cost is the primary constraint
- • Simplicity and debuggability outweigh speed
Architecture Patterns
Hub and Spoke
One orchestrator agent delegates to specialist sub-agents and aggregates results. Best for sequential pipelines with clear ownership.
Peer-to-Peer
Agents communicate directly with each other via a shared message bus or shared file. No single coordinator.
Hierarchical
Agents form a tree. Top-level agents decompose goals and hand off to teams of sub-agents, each of which may delegate further.
Defining Agent Roles
Every agent in a swarm should have one clearly defined role. Overlap leads to conflicting outputs and wasted tokens.
| Role | Responsibility |
|---|---|
| Orchestrator | Decomposes the goal, assigns tasks, aggregates outputs |
| Specialist | Deep expertise in one domain; produces structured output |
| Validator | Checks outputs from other agents for correctness or policy compliance |
| Aggregator | Merges parallel outputs into a coherent result |
| Monitor | Watches for failures, timeouts, or unexpected outputs; triggers retries |
Inter-Agent Communication
Agents communicate through structured outputs written to shared files. The simplest and most debuggable pattern is a shared JSON or markdown file in the workspace:
# Shared coordination file: .claude/swarm/review-results.json
{
"pr_number": 142,
"status": "in_progress",
"agents": {
"typescript_reviewer": { "status": "done", "issues": 2 },
"security_reviewer": { "status": "running", "issues": null },
"test_coverage": { "status": "done", "coverage": 84 }
},
"aggregate_ready": false
}The orchestrator polls this file. When all agents have written their results, it reads all outputs and synthesises the final review. No message queues, no network calls — just files.
Real-World Examples
Code Review Swarm
On every PR, four agents review in parallel. Results are merged by an orchestrator into a single review comment.
Research Swarm
Searcher and Summariser run in parallel. Fact Checker validates claims. Report Writer synthesises the final output.
Deployment Swarm
All four run in parallel after a merge to main. Deployment only proceeds if all agents return green.
Get a Pre-Built Swarm Setup
The AI Starter Package includes a 12-agent configuration with orchestration, inter-agent communication, and parallel execution pre-wired.
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