Agentic RAG
Agentic RAG is a retrieval-augmented generation approach in which an agent dynamically decides how and when to retrieve information, often iterating or using tools before generating a response.
AI Agents in Action, Second Edition
- How it is used
- Uses agent decisions to route retrieval, assess context, and ground answers to questions about external knowledge.
- How deep it goes
- Builds from vector retrieval to hybrid tools and guardrails, while leaving hybrid tool implementations aside.
- Examples
- Uses an indexed movie script, benchmark questions, grounding guardrails, and a customer-support retrieval workflow.
- What you can do after
- Build it from scratch with retrieval tools, then evaluate answers and block outputs unsupported by retrieved context.
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Domain-Specific Small Language Models
A retrieval-augmented generation setup that uses an AI agent to plan, select, and combine retrieval actions across one or more sources before generation.
- How it is used
- Coordinates retrieval and tool use before generation in support, research, knowledge, and domain-specific workflows.
- How deep it goes
- Defines the pattern, contrasts it with traditional RAG, and sketches tool-driven workflows without detailing agent internals.
- Examples
- Uses a vector-database-first fallback to web search, plus CodeAgent examples with BM25 search, semantic search, and hybrid search.
- What you can do after
- Apply it via an agent that can choose search strategies, combine tools, and improve retrieval quality before answering.
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Essential GraphRAG
Agentic RAG is a retrieval-augmented generation pattern in which agents and routing mechanisms select and coordinate retrieval actions to answer a question.
- How it is used
- Routes questions across multiple data sources and validates retrieved answers when generic retrieval is insufficient.
- How deep it goes
- Defines routing, retrieval, and answer criticism, while stopping short of advanced multi-step task planning.
- Examples
- Uses generic vector retrieval, specialized text2cypher, and parameterized database queries as alternative retriever agents.
- What you can do after
- Build retrieval workflows that select suitable sources and assess answer quality before returning results.
Building Reliable AI Systems
A retrieval-augmented generation approach in which an AI agent controls the retrieval process, including deciding when multiple retrievals are needed.
- How it is used
- Combines document retrieval with agent-directed searches, external tools, and real-world tasks.
- How deep it goes
- Explains multi-source retrieval decisions and practical tool integration through a travel-assistant implementation.
- Examples
- Uses policy-and-review comparisons and travel planning with a weather service to illustrate retrieval needs.
- What you can do after
- Build a travel assistant that combines document retrieval with real-time services using LangChain.
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Related concepts
- AI agentuses · An AI agent is a computational system that uses artificial intelligence to interpret information, select actions, and pursue goals through interaction with an environment.
- information retrievaluses · Information retrieval is the process of locating and selecting relevant information from an available collection in response to a query or information need.
- retrieval-augmented generationkind of · Retrieval-augmented generation is a technique that retrieves relevant information from external sources and supplies it as context to a generative model when producing an output.
- routinguses · Routing is the selection of a path, destination, or processing component to direct an item, request, or flow toward an intended endpoint.
- LangChainuses · LangChain is an open-source software framework for building applications that compose language models with prompts, data sources, tools, and other components.
- similarity searchuses · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.
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