Swarm Architecture

The defining characteristic of a swarm is that intelligence emerges from the collective behavior of many agents, none of which has the full picture. A swarm for competitive intelligence analysis might deploy fifty parallel agents simultaneously — each reading and summarizing a different competitor's recent press releases, patent filings, or job postings — and then aggregate their outputs into a synthesis. The same task run sequentially by a single agent would take fifty times as long.

Swarm architecture diverges from hierarchical multi-agent systems in its coordination model. Hierarchical systems have an explicit chain of command: an orchestrator delegates to sub-agents and consolidates results. Swarms use peer coordination — agents may broadcast results to a shared context pool, bid on sub-tasks from a work queue, or vote on conclusions through a consensus mechanism. This makes swarms more resilient (no single point of failure) but harder to debug and govern, since the source of any given output may be spread across dozens of agent interactions.

Enterprise swarm use cases cluster around tasks with high parallelizability: large-scale document review (contract analysis across a merger's entire document room), multi-source research synthesis, large codebase refactoring (parallel agents working on independent modules), and real-time threat monitoring (a swarm of detection agents watching different signal streams). The key design decisions are: how to decompose the task so agents can work independently, how to aggregate and de-duplicate results, and how to handle agent failures gracefully without restarting the entire swarm.