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Agentic RAG
Agentic RAG is a family of retrieval-augmented systems in which a model or controller can choose retrieval actions, inspect results, and sometimes search again before answering.
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Overview
These loops can add evidence-seeking flexibility and also add latency, cost, and failure points.
Deep Dive
Classic RAG typically retrieves context for a query and supplies it to a language model. Agentic RAG refers to designs where an agent decides whether to retrieve, which query or source to use, and whether to continue after seeing results. A system may decompose a question, inspect evidence, filter or critique it, and reformulate a search. Research approaches differ: Self-RAG uses reflection tokens for retrieval and critique, while Adaptive-RAG selects retrieval strategies based on question complexity. These are specific methods, not a standard architecture or a guarantee that evidence supports the final answer.
Real-World Implementation
Break a question about two products into separate searches, then compare retrieved evidence.
Choose between a document index and a structured database when the question requires different data.
Check whether retrieved passages support a draft before returning it.
Stop retrieval when evidence is sufficient or a step budget is reached.
The Future of Agentic RAG
Agentic retrieval remains a design space with tradeoffs among answer quality, evidence coverage, cost, and latency. Evaluate on the target corpus and questions, track whether sources support generated claims, and compare against a simpler retrieval baseline.
Putting it into practice
Why it matters
- Build choices. Application-level design determines whether AI improves real outcomes.
- Team and workflow. Good workflow integration creates productivity gains users can trust.
- Risk and safety. Well-scoped use cases reduce change fatigue and implementation risk.
Watch out for
- Automating a broken process can amplify existing problems.
- Teams may over-automate and remove needed human judgment.
- Quality can drift if outputs are not continuously evaluated.
Next steps
- Map the current workflow and identify the highest-friction step.
- Define human checkpoints before full automation.
- Train users on prompts, escalation paths, and quality standards.
- Track task-level outcomes to confirm sustained value.
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Frequently asked questions
What is Agentic RAG?
Agentic RAG is a family of retrieval-augmented systems in which a model or controller can choose retrieval actions, inspect results, and sometimes search again before answering. These loops can add evidence-seeking flexibility and also add latency, cost, and failure points.
Compared with a simple single-shot retrieve-and-generate baseline, what can agentic RAG add?
Agentic methods can make retrieval an iterative decision; classic RAG has multiple formulations, so this contrast is scoped to a simple single-shot baseline.
How does agentic RAG handle a difficult multi-hop question?
Decomposing into sub-questions and retrieving evidence for each is a core strength of agentic RAG.
In agentic RAG, retrievers are best described as what to the agent?
The agent treats each retriever as a tool and decides whether and how to use it on each turn.
What does an optional 'grading' step do in agentic RAG?
Grading relevance of retrieved chunks reduces the risk that off-topic context misleads the model.
What is the main cost of agentic RAG compared to single-shot RAG?
Iterative retrieval can improve coverage for some questions, but it adds calls, latency, and cost and can still retrieve or reason incorrectly.
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