Automating agent selection in multi-agent systems using retrieval-based matching and LLM re-ranking improves reliability and scalability compared to manual composition, especially when a critique agent validates the full workflow.
This paper presents an automated framework for building multi-agent systems that replaces manual steps with AI-driven composition. It uses an LLM planner to break down user requests into tasks, then automatically selects the best agents from registries using a two-stage retrieval system (fast retriever + LLM re-ranker), with a critique agent validating the entire plan.