How do you implement agentic RAG systems that can plan and execute multi-step retrieval?
Agentic RAG combines autonomous agent capabilities with retrieval-augmented generation for complex information needs. Architecture: the agent receives a question, plans a retrieval strategy (which sources to query, what sub-questions to answer), executes retrieval steps iteratively, evaluates whether sufficient information has been gathered, and generates a comprehensive answer. Implementation: equip an LLM agent with retrieval tools (vector search, keyword search, SQL queries, web search), let the agent decide which tools to use and in what order, and implement a reflection step where the agent evaluates retrieved information and decides whether to continue searching or synthesize an answer. Multi-step retrieval: for complex questions like "Compare the performance characteristics of PostgreSQL and MongoDB for real-time analytics", the agent: (1) retrieves information about PostgreSQL analytics capabilities, (2) retrieves information about MongoDB analytics capabilities, (3) retrieves benchmarks comparing the two, (4) evaluates if it has enough for a comprehensive comparison, and (5) generates a structured comparison. Adaptive query refinement: if initial retrieval returns insufficient results, the agent reformulates the query (try different terms, broader scope), decomposes the question into simpler sub-questions, or searches different knowledge sources. Implementation with LangGraph: create a graph with nodes for query planning, retrieval, evaluation, and response generation. Conditional edges route back to retrieval if the evaluation determines more information is needed. State tracks accumulated context and retrieval history. Benefits over basic RAG: handles complex multi-faceted questions, adapts retrieval strategy based on intermediate results, can combine information from multiple sources intelligently, and provides more comprehensive answers. Guardrails: limit maximum retrieval iterations (prevent infinite loops), set overall timeout, track token usage to stay within budget, and implement quality checks on the final answer.