Agentic RAG Architecture: How to Diagram Adaptive Retrieval Systems

Classic RAG gave teams a way to ground LLM responses in a knowledge base without fine-tuning. But the original pattern has a hard constraint: it follows a fixed, one-shot pipeline. The system retrieves once, injects the results, and generates — regardless of whether the retrieved context was relevant, complete, or even necessary. Agentic RAG breaks that constraint. The LLM moves from being a passive endpoint at the end of a pipeline to an active participant that decides when to retrieve, what to retrieve, how many rounds of retrieval are needed, and whether the results are good enough to generate a response.

The payoff is measurable: research in 2025 showed that agentic RAG systems augmented with knowledge graphs reduced hallucination rates by roughly 62% compared to naive RAG. That's not a small incremental gain — it reflects what happens when you replace a fixed pipeline with a genuine reasoning loop. This guide covers the full architecture of agentic RAG, how it differs from classic patterns, the five components every diagram must include, and ready-to-use prompt templates for generating accurate diagrams in seconds.

Classic RAG vs. agentic RAG: the key architectural difference

If you've already read our complete guide to RAG architecture diagrams, you know the classic pipeline: embed a query, retrieve top-k chunks from a vector store, inject them into a prompt, generate. That pattern — sometimes called naive RAG or vanilla RAG — works well for simple, single-hop questions over a curated knowledge base. Advanced RAG extended it with reranking, query expansion, and hybrid search, but the control flow remained linear: retrieve once, then generate.

Agentic RAG introduces a fundamentally different architecture. The three changes that define it:

• Decision-making about retrieval: The LLM evaluates whether retrieval is needed at all. For straightforward factual questions it already knows, it can answer directly. For ambiguous or multi-part questions, it retrieves before responding.
• Multi-step retrieval: Complex queries are decomposed into sub-queries. Each sub-query triggers a separate retrieval pass, potentially against different sources (vector store, knowledge graph, SQL database, live web). The results are fused before generation.
• Corrective retrieval: After retrieval, the LLM evaluates the relevance and completeness of the returned context. If the context is poor, it reformulates the query and retrieves again — or escalates to a web search — before proceeding.

The result is a control loop, not a pipeline. Your architecture diagram must reflect this: instead of a linear left-to-right flow, agentic RAG diagrams have feedback edges, decision nodes, and explicit loop conditions.

The five components of an agentic RAG architecture

Every production agentic RAG system can be decomposed into five components. The dominant 2026 stack uses LlamaIndex for ingestion and retrieval with LangGraph for the orchestration layer, but the component boundaries hold regardless of framework.

1. Orchestration layer

The orchestration layer is the brain of the system. It maintains a state machine that tracks where the system is in the reasoning loop: has retrieval been attempted, how many rounds have run, what is the current confidence level, is a tool call in flight. In LangGraph, this is implemented as a typed graph with nodes for each decision point and conditional edges for branching. The orchestration layer also manages session state — conversation history, intermediate reasoning steps, and retrieved context — so that multi-turn conversations maintain coherence across retrieval rounds.

2. Planner

Before any retrieval happens, the Planner decomposes the incoming query into a retrieval plan. For a simple question it may generate a single sub-query. For a multi-hop question like “How did our Q3 revenue compare to competitors in the same segment?” it generates independent sub-queries: one for internal revenue data, one for market data. Query decomposition and sub-query generation are distinct from the original query reformulation in advanced RAG — the planner is reasoning about the structure of the problem, not just rephrasing for better retrieval.

3. Retriever

The Retriever in an agentic system is multi-source. A single query plan may dispatch retrieval to a vector store for semantic search, a knowledge graph for relational traversal, a SQL database for structured queries, and a web search tool for real-time information. Unlike classic RAG where retrieval is a single synchronous call, the agentic Retriever manages parallel sub-retrievals and returns structured results with provenance metadata — source type, confidence score, retrieval timestamp — so that the Context Fusion layer can make informed decisions.

4. Context Fusion

Context Fusion takes the raw outputs of the multi-source Retriever and produces a ranked, deduplicated context window. This includes re-ranking (cross-encoder models score each chunk against the original query), deduplication (chunks from different sources covering the same fact are merged), and relevance scoring (chunks below a threshold are dropped rather than injected, preventing the LLM from being distracted by marginally relevant noise). The output of Context Fusion feeds back to the Orchestration layer: if no chunks pass the relevance threshold, the orchestrator triggers another retrieval round rather than proceeding to generation.

5. Tool Agent

The Tool Agent is what separates agentic RAG from a retrieval system with a smarter retrieval step. When the Planner determines that the answer requires executing an action — running a SQL query, calling an external API, reading a file, executing code to transform data — it dispatches to the Tool Agent. The Tool Agent manages code execution (sandboxed Python or JavaScript), API calls with authentication and rate limiting, and database queries with schema awareness. Results are returned as structured observations that feed into the next context fusion cycle.

Agentic RAG control loop patterns

Three named patterns have emerged for the agentic RAG control loop. Each has a distinct diagram shape.

Corrective RAG (CRAG): evaluate → retrieve → correct

CRAG adds an evaluation node after retrieval. The retrieved context is scored for relevance; if the score is low, a corrective action is triggered — typically a web search that supplements or replaces the vector store results. CRAG diagrams have a branching node after the Retriever: the “relevant enough” branch proceeds to generation, the “not relevant” branch routes to a web search tool, and the “ambiguous” branch combines both sources with deduplication before proceeding.

Self-RAG: self-reflect on the need to retrieve

Self-RAG inserts reflection tokens at generation time. The LLM predicts whether retrieval is needed (Retrieve), whether the retrieved passages are relevant (IsRel), whether the generated response is supported by the passages (IsSup), and whether the response is useful (IsUse). Each reflection token is a decision point in the diagram. Self-RAG diagrams look like a generation loop rather than a retrieval loop: the LLM is both generating and evaluating its own output in real time, with retrieval triggered conditionally mid-stream.

Modular RAG: composable pipeline stages

Modular RAG treats each stage — routing, retrieval, fusion, generation, verification — as a swappable module with a defined interface. The orchestrator selects and chains modules dynamically based on query classification. A simple factual query routes through a minimal chain; a complex analytical query routes through decomposition, multi-source retrieval, fusion, and verification stages. Modular RAG diagrams resemble workflow DAGs and are well-suited to teams that need to A/B test retrieval strategies or swap components between environments.

Prompt templates for agentic RAG architecture diagrams

The following prompt templates are designed for use with ArchitectureDiagram.ai. Paste them in and get a production-ready diagram in seconds.

LangGraph agentic RAG with CRAG pattern

Self-RAG with reflection tokens

Multi-source agentic RAG with Tool Agent

When to use agentic RAG vs. standard RAG

Agentic RAG is more capable than standard RAG but also more expensive and higher latency. The decision criteria are straightforward:

• Query complexity: If your users primarily ask single-hop questions with a clear answer in one document, standard RAG is sufficient. If questions routinely require synthesizing information from multiple sources or require multi-step reasoning, agentic RAG is warranted.
• Multi-step reasoning: Questions like “Compare our churn rate this quarter with the industry benchmark and identify the top three drivers” require decomposition, multi-source retrieval, and synthesis. Standard RAG cannot handle these reliably; agentic RAG is designed for them.
• Latency tolerance: Each retrieval round and reflection step adds latency. Standard RAG can respond in under 1 second; agentic RAG with multiple retrieval rounds may take 5–15 seconds. For real-time chat, tune the number of allowed retrieval rounds; for async research workflows, latency is rarely a constraint.
• Cost tolerance: Multi-step retrieval, reranking, and tool calls each add cost per query. For high-volume consumer applications, the per-query cost of agentic RAG may be prohibitive. For low-volume, high-value queries (enterprise research, legal analysis, financial modeling), the quality improvement justifies the cost.
• Hallucination tolerance: In regulated domains — healthcare, legal, finance — where hallucinated facts carry serious consequences, the corrective retrieval loop of agentic RAG is not optional. The 62% reduction in hallucination rates compared to naive RAG makes it the only responsible choice for these use cases.

Agentic RAG with knowledge graphs

The 62% hallucination reduction benchmark cited above comes specifically from agentic RAG systems that incorporate a knowledge graph as a retrieval source alongside a vector store. The pattern is called GraphRAG, and it addresses a core weakness of pure vector search: semantic similarity does not capture explicit relationships between entities.

In a GraphRAG architecture, the knowledge graph stores entities (people, organizations, products, concepts) and the typed relationships between them. When the Planner generates a sub-query that is relational in nature — “Which suppliers are affected by this regulation?” — the Retriever dispatches a graph traversal query (Cypher for Neo4j, SPARQL for RDF stores) rather than a vector similarity search. The structured result from the graph is fused with the semantic results from the vector store in the Context Fusion layer.

The hallucination reduction is explained by the nature of knowledge graph data: relationships are explicitly asserted, not inferred from embedding similarity. When the LLM generates a claim about a relationship between two entities, it can be traced back to an explicit graph edge rather than a fuzzy vector neighborhood. Agentic RAG diagrams with knowledge graphs should clearly distinguish the graph traversal path from the vector retrieval path and show how results converge at the Context Fusion node.

For a dedicated deep-dive on the GraphRAG pattern, see our GraphRAG architecture diagram guide.

Frequently asked questions

What is the difference between agentic RAG and standard RAG?

Standard RAG follows a fixed pipeline: retrieve once, inject context, generate. Agentic RAG replaces the fixed pipeline with a control loop where the LLM decides whether to retrieve, what sources to query, how many rounds of retrieval are needed, and whether the retrieved context is good enough to generate a response. The result is higher accuracy on complex queries at the cost of higher latency and per-query cost.

What is the best framework for agentic RAG in 2026?

The most common production stack in 2026 is LlamaIndex for document ingestion, embedding, and retrieval combined with LangGraph for the orchestration layer and control loop. LlamaIndex provides mature abstractions for multi-source retrieval and context fusion; LangGraph provides the state machine primitives needed to implement corrective retrieval loops, reflection steps, and human-in-the-loop gates. Teams that are already deep in the LangChain ecosystem often use LangGraph exclusively for both layers.

How do I diagram the corrective retrieval loop in an agentic RAG system?

The corrective retrieval loop requires three diagram elements that are absent from standard RAG diagrams: a relevance evaluation node after retrieval, a conditional branch that routes to a fallback source (typically web search) when relevance is below threshold, and a feedback edge that reconnects the fallback output to the Context Fusion node. Use a decision diamond or a labeled conditional edge to make the branch condition explicit (“score < 0.5”). The most common mistake is drawing the corrective path as a separate linear flow rather than a loop that feeds back into the same generation step.

Related guides: RAG architecture diagram, GraphRAG architecture diagram, context engineering diagram, multi-agent orchestration patterns, and LangGraph architecture diagram.