Multi-Agent System

Multi-Agent Systems decompose complex tasks across multiple AI agents, each with defined roles, tools, and scopes of responsibility. Common architectural patterns include: pipeline architectures (Agent A produces output that Agent B refines), parallel architectures (multiple agents work simultaneously on independent subtasks with results merged), hierarchical architectures (a manager agent delegates to specialist worker agents), and debate architectures (agents produce independent answers that a judge agent evaluates). Each pattern has different strengths: pipelines are simple but linear; parallel execution is fast but requires tasks to be genuinely independent; hierarchical systems are flexible but add coordination complexity; debate patterns improve accuracy but double the compute cost.

The primary motivation for multi-agent systems over single agents is specialization and scalability. A research pipeline where a web search agent, a document analysis agent, and a synthesis agent each do what they're optimized for typically outperforms a single general-purpose agent trying to do all three. Multi-agent systems also improve reliability through cross-validation: one agent generates a response and a second agent reviews it for errors — a "maker-checker" pattern that catches mistakes the first agent wouldn't catch in self-review. The tradeoff is coordination overhead: state must be passed between agents, failures in one agent can block dependent agents, and debugging a multi-agent interaction is substantially more complex than debugging a single-agent one.

For B2B teams building sophisticated AI automation, multi-agent systems are appropriate when task complexity exceeds what a single agent can reliably handle in a linear workflow. A content production system where a research agent gathers facts, a writing agent drafts content, a fact-checking agent verifies claims, and a style agent polishes the output is a multi-agent system that produces more reliable results than any single agent could achieve alone. The practical challenge is that multi-agent systems require more careful engineering, testing, and monitoring than single-agent applications — the right tradeoff depends on whether the task complexity genuinely requires it or whether a well-designed single agent with good tools would suffice.