Optimal agentic LLM system design across use cases

Determine a clear, general approach for designing optimal agentic large language model (LLM) multi-agent systems across different application use cases, specifically establishing criteria and guidelines for selecting and orchestrating collaborating agents (e.g., Classifier, Retriever, Generator, Reviewer) within Generic Agentic RAG (GA-RAG) workflows to reduce hallucinations and improve task efficiency.

Background

The paper surveys multi-agent (agentic) LLM systems and notes that, despite promising results in various domains, there is no established methodology for choosing an optimal architecture for different tasks. To address this gap, the authors propose a Generic Agentic RAG (GA-RAG) workflow comprising Classifier, Retriever, Generator, and Reviewer agents, but they acknowledge that determining optimal design choices remains unresolved.

This open problem is motivated by the need to systematically specify how many agents to use, what roles they should play, and how they should be orchestrated to achieve reliable, efficient performance in knowledge-intensive, real-time retrieval-augmented generation systems for IoT and other domains.