Building Agent Swarms: Multiple AI Agents Working in Parallel

What Are Agent Swarms?

An agent swarm is multiple AI agents working in parallel on different aspects of the same project. Instead of one agent doing everything sequentially, you deploy specialist agents that each handle a specific domain — code review, testing, documentation, security, research.

Architecture Patterns

There are three main patterns: Hub-and-spoke (one orchestrator delegates to specialists), Peer-to-peer (agents communicate directly), and Hierarchical (team leads manage sub-agents). The hub-and-spoke pattern is the most practical for most teams.

Inter-Agent Communication

Agents share context through shared memory files. The orchestrator writes task descriptions to a shared state file, specialists read it, do their work, and write results back. This file-based approach is simple, debuggable, and survives crashes — unlike complex API-based messaging.

Real-World Example: Code Review Swarm

Deploy three agents in parallel: a Security Reviewer checks for vulnerabilities, a Code Quality reviewer checks patterns and style, and a Performance reviewer checks for bottlenecks. Each writes findings to a shared review document. The orchestrator synthesizes the results into a single review.