What is agentic RAG and Graph RAG? Query rewriting, multi-step retrieval, knowledge graphs, and when a plain RAG pipeline is not enough

What is agentic RAG?

Agentic RAG wraps the retrieval pipeline in a decision loop run by the model: decide whether retrieval is needed at all, rewrite or decompose the query, choose which index or tool to search, inspect what came back, and retrieve again or differently if the evidence is weak, before generating. A plain RAG pipeline does one fixed retrieve-then-generate pass [1]; an agentic one lets the model steer, using patterns from ReAct and its descendants [9]. It answers harder, multi-step questions at the cost of more model calls, more latency and more ways to fail.

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RAG.NextUpgrad, the production platform I built, is deliberately not agentic in the full sense: it has one pass with hybrid retrieval, reranking and a confidence gate, and that pass scores above ninety percent recall on the labelled set [15]. This page is about what the agentic and graph layers add, what they cost, and the test I use to decide whether a system needs them. My notes on multi-hop questions and query rewriting are on the portfolio [13][14].

Every agentic RAG system I have audited that was struggling had a chunking or ranking problem underneath. Fix the pipeline first. Add planning when the pipeline is good and the questions still fail. Pranjul Rathour

How does agentic RAG differ from a plain RAG pipeline?

A plain pipeline is a fixed sequence: embed, retrieve, rerank, generate, once. An agentic system inserts model decisions between the stages: a router that picks the retriever, a rewriter that improves the query, a grader that checks whether retrieved passages actually answer the question, and a loop that retries with a different query or source. The decisions are prompts or small classifiers; the loop is a graph with a step limit. Every decision is a place for the model to be right or wrong, and a place to measure.

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What is query rewriting, and what is HyDE?

Query rewriting has the model turn the user's question into a better retrieval query: expanding abbreviations, adding likely terms, splitting a compound question into sub-questions, or removing conversational noise. HyDE, hypothetical document embeddings, goes further: the model writes a short hypothetical answer, and that text is embedded and used to search, because an answer-shaped passage is closer in vector space to real answer passages than a short question is [5]. Both are one extra model call and often the biggest agentic-style gain available.

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Query rewriting is the one agentic element I add to almost every system, because conversational follow-ups such as "and for the other plan?" are unretrievable as written [14].

What are Self-RAG and corrective RAG?

Self-RAG trains a model to emit reflection tokens that decide whether to retrieve, whether a passage is relevant, whether the generation is supported, and whether it is useful, so retrieval and self-critique happen inside generation [3]. Corrective RAG adds a lightweight evaluator that grades retrieved documents as correct, incorrect or ambiguous and triggers web search or query refinement when retrieval looks wrong [4]. Both formalise the same idea a confidence gate implements more simply: check the evidence before you trust it.

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The confidence gate in RAG.NextUpgrad is the minimal version of this family: it inspects reranker scores and passage agreement and refuses when support is weak, with no extra model call [15]. Start there; graduate to graded loops when the refusal rate on answerable questions is measurably too high.

What is Graph RAG?

Graph RAG builds a knowledge graph from the corpus, extracting entities and the relationships between them with a model, clusters the graph into communities, writes a summary for each community, and then retrieves through the graph: by entity for specific questions, by community summary for global ones. Microsoft's Graph RAG paper showed gains on query-focused summarisation, questions such as "what are the main themes across these documents", where chunk retrieval has nothing to retrieve because the answer is not in any single chunk [6][7].

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RAPTOR reaches a similar goal, multi-level retrieval, by recursively clustering and summarising chunks into a tree, without extracting an explicit graph [8]. Both trade index-time cost for the ability to answer questions about the whole.

When do I actually need agentic RAG or Graph RAG?

When the plain pipeline is measurably good and specific question types still fail. Multi-hop questions that need two retrievals, "who leads the team that built the tool mentioned in the June report", need interleaved retrieval and reasoning, which IRCoT and agentic loops provide [10]. Global questions about a corpus need Graph RAG or RAPTOR. Questions spanning several systems need a router. Everything else, which is most questions in most products, needs chunking, hybrid search and reranking done well.

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How do I build an agentic RAG system?

Model it as a graph with explicit state: nodes for rewrite, retrieve, grade, generate and refuse, edges chosen by the grader's output, a step limit and a token budget. LangGraph is the common framework for this shape [11]; the same loop is a couple of hundred lines without one. Log every decision. Evaluate the agentic system against the plain pipeline on the same labelled set and keep it only where it measurably wins. Anthropic's guidance applies: the simplest architecture that works, and the model's judgement only where it is actually needed [12].

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What does agentic RAG cost?

Several model calls per question instead of one, plus latency that users notice, plus a larger failure surface: a rewriter that drifts, a grader that is too strict, a loop that runs to its limit. A rewriter and a grader roughly double cost; a full multi-agent design can multiply it several times. Graph RAG shifts cost to index time, with many model calls to extract entities and write summaries, which repeats when documents change. Budget both before building, and measure whether the gain in recall or answer quality pays for it.

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What are common mistakes with agentic and Graph RAG?

Adding an agent loop to a pipeline with bad chunking. No step limit, so a hard question loops until the budget dies. A grader that rejects good passages and triggers endless retries. Graph RAG for a corpus of precise procedures where chunk citations were the requirement. Not logging decisions, so failures cannot be traced. Evaluating the agentic system alone instead of against the plain baseline. Every one of these is avoided by measuring first and adding layers only where the measurement demands them.

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Agentic RAG interview questions

Define agentic RAG and Graph RAG and say what each fixes. Explain query rewriting and HyDE. Describe Self-RAG or corrective RAG in a sentence. Give a question a plain pipeline cannot answer and say which layer fixes it. Explain how you would decide whether to add an agentic layer, and how you would measure the result. Describe the cost trade-offs. The strongest answer includes a pipeline you measured and a layer you chose not to add.

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Where should I start?

Measure your plain pipeline's recall on twenty labelled questions. Read the misses. If they are conversational follow-ups, add query rewriting. If they are multi-hop, add a retrieval check and one retry. If they are global questions about the corpus, prototype RAPTOR or Graph RAG on a subset and compare. Add nothing you cannot measure. For a hands-on session on retrieval systems from the notebook to the operator level, email pranjulrathour41@gmail.com or use pranjulrathour.scult.in/invite.

Pranjul Rathour

GenAI Engineer · Kanpur, Uttar Pradesh, India

GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).

Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.

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