Agent Swarm

Agent Swarm is an AI architecture using multiple specialized agents (sub-agents) coordinated by a central orchestrator, instead of one multi-purpose agent. Each agent has narrow specialization—such as content planning, EAV (Entity-Attribute-Value) extraction, or keyword clustering—and operates with its own dedicated toolset including MCP protocols, APIs, and files. The Orchestrator decides which agent handles each pipeline step.

This offers better results through specialization—your clustering agent doesn't need to understand knowledge graphs. However, it introduces coordination complexity and higher token costs due to inter-agent communication. In semantic audits, agent swarms are typically implemented as sub-agents within AI agent platforms or specialized tools like Dify.

Consider an SEO audit pipeline: a crawler agent initiates the process, followed by embedding, clustering, and reporting agents. Each functions as a specialist. In practice, start with one multi-skilled agent and switch to a swarm only when single-agent complexity becomes unmanageable.