What is Agentic RAG

How It Works

Agentic RAG moves beyond single-shot retrieval by introducing an agent layer that reasons about retrieval strategy. Instead of executing one fixed query against a vector store, the agent analyzes the user question, decomposes it into sub-questions if needed, selects appropriate data sources and retrieval methods for each, evaluates intermediate results for completeness and relevance, and decides whether additional retrieval rounds are needed. This iterative, autonomous approach produces higher quality answers for complex questions that span multiple topics, modalities, or knowledge domains.

Technical Details

Agentic RAG architectures typically include a planning component (an LLM that decomposes queries and selects tools), a retrieval toolkit (multiple search endpoints, filters, and data sources the agent can invoke), and a synthesis component (an LLM that combines retrieved context into a final response). The agent uses tool-calling capabilities to execute retrieval actions and observe results before deciding next steps. Mixpeek's composable retriever pipelines and multi-stage search capabilities provide the retrieval toolkit that agentic systems need, supporting filtered searches, cross-modal queries, and reranking as distinct tools the agent can invoke.

Best Practices

• Provide the agent with diverse retrieval tools -- vector search, keyword search, metadata filters, and cross-modal queries -- so it can select the best strategy per query
• Set clear stopping criteria to prevent the agent from over-retrieving or looping indefinitely on ambiguous questions
• Log agent reasoning traces for debugging and evaluation of retrieval strategy quality
• Start with simple agent architectures (ReAct, plan-and-execute) before adding complexity

Common Pitfalls

• Building overly complex agent architectures that add latency and failure modes without improving answer quality
• Not providing enough retrieval tool diversity, forcing the agent to use a single search method for all queries
• Ignoring agent evaluation -- testing the final answer without analyzing whether the retrieval strategy was optimal
• Letting agents execute unbounded retrieval loops that consume excessive compute and time

Advanced Tips

• Implement retrieval caching so the agent can reference results from previous steps without re-executing queries
• Use smaller, faster models for the planning step and reserve larger models for final synthesis
• Build specialized sub-agents for different retrieval domains that the main agent can delegate to
• Evaluate agentic RAG systems with metrics that measure retrieval efficiency (steps taken) alongside answer quality