Multi-agent agentic RAG

A modular, scalable step up. Instead of one agent doing everything, responsibilities are split across multiple specialized agents, coordinated by a master agent. It is built around the parallelization pattern.

The workflow: a coordinator receives the query and delegates to specialized retrieval agents, for example one for structured SQL queries, one for semantic search over documents, one for web search, and one for recommendations. These agents retrieve in parallel using their own tools, and the results are synthesized by the LLM into one response.

Strengths: modularity (agents can be added or removed independently), scalability (parallel processing handles high query volumes), task specialization (each agent is tuned to its domain), and versatility across domains.

Challenges, which echo the multi-agent topic exactly: coordination complexity (managing inter-agent communication needs sophisticated orchestration), computational overhead from running many agents, and the difficulty of integrating outputs from diverse sources into one coherent answer.

The survey's example is a research assistant answering a question about the economic and environmental impacts of renewable energy in Europe, with separate agents handling economic databases, academic papers, recent news, and related recommendations, then merging the findings.