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Multi-agent systems help LLMs stay focused by separating planning, execution, and verification. Each agent owns a narrow responsibility and hands off structured outputs.
Agent roles
A planner creates the task breakdown, an executor performs tool calls, and a reviewer checks results against requirements. This structure reduces hallucinations and improves traceability.
RAG pipeline design
Retrieval-augmented generation keeps responses grounded. Store trusted documentation in a vector index, then filter by relevance before each agent step.
Orchestration patterns
Queue-driven workflows and explicit state tracking make multi-agent systems predictable. Each handoff should include context, decisions, and known risks.
Evaluation and guardrails
Measure accuracy, latency, and cost. Add policy checks, fallback strategies, and human review where the impact is high.