Agentic RAG
- Definition
- A retrieval-augmented generation system where an AI agent autonomously decides when, what, and how to retrieve information — dynamically choosing between multiple knowledge sources, reformulating queries, and iterating on results rather than making a single fixed retrieval call.
- Why it matters
- Standard RAG is a one-shot lookup: embed the query, find similar documents, stuff them into the prompt. It works for simple questions but falls apart on complex, multi-part queries that require synthesizing information across sources. Agentic RAG is an iterative research assistant — it can decide its initial retrieval was insufficient, reformulate the query, try a different knowledge base, and keep going until it has what it needs. This is the architecture pattern behind every enterprise AI assistant shipping in 2026. If your RAG system cannot handle questions like 'compare our Q3 performance against the three competitors mentioned in last month's board deck,' you need agentic RAG, not more prompt engineering.
- In practice
- IBM published its agentic RAG framework in 2025, demonstrating multi-step retrieval workflows that outperformed single-pass RAG by 25-40% on complex enterprise queries. Microsoft's Semantic Kernel and NVIDIA's NeMo Retriever both ship agentic retrieval capabilities. LangGraph's retrieval agents can orchestrate across vector databases, SQL databases, and web search in a single query resolution. Weaviate introduced multi-step retrieval primitives that let agents decompose queries and retrieve iteratively. In enterprise deployments, agentic RAG reduced 'I don't know' responses by 35-50% compared to standard RAG, particularly on questions requiring cross-document reasoning or temporal analysis.
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Related terms
RAG (Retrieval-Augmented Generation)
A technique that retrieves relevant documents from an external knowledge base and feeds them to a model alongside the user's query. RAG reduces hallucination and keeps responses grounded in current, factual data.
Agent
An AI system that can autonomously plan, use tools, and execute multi-step tasks on behalf of a user. Agents are the next major product paradigm after chatbots, with every major lab shipping agent frameworks.
Orchestration
The coordination layer that manages the flow of data, context, and control between multiple AI models, tools, and data sources within a complex application. Orchestration frameworks handle routing, error recovery, state management, and multi-step workflows.
Vector database
A database optimized for storing and querying high-dimensional vectors (embeddings). Vector databases like Pinecone, Weaviate, and Chroma are critical infrastructure for RAG, search, and recommendation systems.
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