Retrieval-Augmented Generation (RAG) has been the backbone of enterprise AI knowledge systems since 2023. But the “agentic RAG” category — where the retrieval strategy is itself controlled by an autonomous agent — has matured significantly by 2026, and the production patterns now look very different from the simple “embed + retrieve + generate” pipelines that dominated early implementations.

This guide covers the five core agentic RAG patterns you’ll encounter in 2026, the key tradeoffs between LangGraph and LlamaIndex as implementation frameworks, and how to build an evaluation pipeline that tells you if your RAG system is actually working.

⚠️ Accuracy note: This guide covers architectural patterns and conceptual frameworks. For specific API endpoints, configuration keys, or CLI commands, always refer to the official documentation for LangGraph, LlamaIndex, and your evaluation tools of choice. These projects move quickly and specific syntax can change between versions.

The Five Core Agentic RAG Patterns

1. Self-RAG

What it is: The model evaluates its own retrieved context before generating — deciding whether the retrieved documents are actually relevant, and whether its own generation is supported by those documents.

How it works: Self-RAG introduces “reflection tokens” — the model outputs special markers indicating its confidence that (a) retrieval was needed, (b) retrieved context is relevant, and (c) its output is faithful to the context. These markers allow the system to loop: if confidence is low, it retrieves again or routes to a fallback.

When to use it: High-stakes knowledge retrieval where citation accuracy matters — legal, medical, financial domains where hallucination has real consequences.

Tradeoffs: Slower (multiple model calls per query), more expensive, but materially more accurate in low-data or ambiguous retrieval situations.

2. CRAG (Corrective RAG)

What it is: CRAG adds a web search fallback to standard RAG. When retrieved documents score below a relevance threshold, the system falls back to real-time web search to supplement the knowledge base.

How it works: A lightweight relevance evaluator scores retrieved documents. Below the threshold → trigger web search. Above the threshold → proceed with standard generation. The scoring can be done by a small classifier or by the LLM itself.

When to use it: Systems where your knowledge base may be stale or incomplete — current events, product documentation that updates frequently, any domain where the question might exceed your indexed content.

Tradeoffs: Adds web search latency and cost. You need to decide your relevance threshold carefully — too low and you hit web search constantly, too high and you miss cases where retrieval is genuinely poor.

3. Adaptive RAG

What it is: A router that classifies query complexity and routes to different retrieval strategies accordingly. Simple factual questions get fast, cheap single-step retrieval. Complex, multi-faceted questions get more expensive multi-step strategies.

How it works: A query classifier (can be a small model or rule-based system) categorizes incoming queries. Simple → direct retrieval. Complex → Self-RAG or multi-hop. Ambiguous → CRAG with web fallback.

When to use it: Production systems serving diverse query types — enterprise knowledge bases, customer support, research tools. Adaptive RAG is essentially a cost optimization strategy layered on top of the other patterns.

Tradeoffs: Requires building and maintaining the query classifier. Misclassifications can send simple questions through expensive pipelines or route complex questions to insufficient retrieval strategies.

4. ReAct RAG

What it is: The agent interleaves reasoning (“thinking”) steps with retrieval actions — deciding what to retrieve based on intermediate reasoning, then updating its reasoning based on what it retrieves.

How it works: ReAct (Reasoning + Acting) is a general agent pattern. In the RAG context, the agent maintains a reasoning trace: “I need to answer X. To do that I need to know Y. Let me retrieve Y. [retrieves Y] Now I also need Z. [retrieves Z] Based on Y and Z, the answer is…”

When to use it: Multi-step research tasks, question answering over large knowledge graphs, any scenario where the optimal retrieval strategy isn’t clear until you start reasoning about the problem.

Tradeoffs: Very powerful but can be expensive and slow if the agent retrieves more than necessary. Requires careful prompt engineering to prevent retrieval loops.

5. Multi-Hop RAG

What it is: Structured multi-step retrieval where each retrieval informs the next query. Instead of one retrieval call, the agent executes a planned chain of retrievals to build a complete answer.

How it works: The agent breaks a complex question into sub-questions, retrieves answers for each sub-question in sequence (where later queries may depend on earlier results), then synthesizes a final answer across all retrieved context.

When to use it: Complex analytical questions that require joining information across multiple domains — “What are the security implications of X technology for Y industry given Z regulatory environment?”

Tradeoffs: Most accurate for genuinely complex multi-part questions but poorly suited for simple questions. Works best when questions can be decomposed into a predictable number of hops.

LangGraph vs LlamaIndex: Framework Verdict

Both frameworks are mature and production-ready in 2026. The choice depends on your priorities:

LangGraph is better when:

  • You need explicit, auditable control over agent state and decision flows
  • Your RAG pipeline is part of a larger multi-agent system
  • You want fine-grained control over when and how retrieval happens
  • Debugging and observability are critical (LangGraph’s graph visualization helps a lot here)

LlamaIndex is better when:

  • You want a higher-level abstraction that handles RAG-specific complexity for you
  • Your primary concern is index management — chunking strategies, embedding models, hybrid search
  • You’re building on top of existing document stores and need good connectors
  • You want faster prototyping with sensible defaults

In practice, many production systems use LlamaIndex for the indexing and retrieval layer and LangGraph (or a custom orchestration layer) for the agent decision logic. The two frameworks are not mutually exclusive.

Building Your Eval Pipeline

The biggest mistake teams make with agentic RAG is shipping without a structured evaluation pipeline. Before you go to production, you need to be able to answer these questions:

  1. Retrieval quality: Are the right documents being retrieved for a representative set of queries? This requires a labeled retrieval evaluation dataset.

  2. Answer faithfulness: Is the generated answer grounded in the retrieved context, or is the model hallucinating? Tools like Ragas provide automated faithfulness scoring.

  3. Answer relevance: Does the generated answer actually address the user’s query? Ragas covers this too.

  4. Latency distribution: What is the p50, p95, and p99 latency for your end-to-end pipeline? Agentic RAG with multiple retrieval hops can have very high tail latencies.

  5. Cost per query: Agentic RAG patterns like Self-RAG and multi-hop can cost 5–10x more per query than simple RAG. Know your cost profile before you scale.

For observability in production, Langfuse and Phoenix (by Arize AI) are the most commonly deployed evaluation and tracing tools in 2026 for LangChain/LangGraph-based systems.

Choosing the Right Pattern for Your Use Case

Use Case Recommended Pattern
Customer support FAQ Adaptive RAG (route simple questions to fast retrieval)
Legal document analysis Self-RAG (faithfulness over speed)
Current events queries CRAG (web search fallback)
Research synthesis Multi-hop RAG
General knowledge assistant ReAct RAG

The honest answer is that most production systems end up as hybrids: Adaptive RAG routing between CRAG for potentially-stale queries and Self-RAG for high-stakes answers, with ReAct available for complex multi-step requests.

Start with the simplest pattern that fits your primary use case. Add complexity only when your eval pipeline shows that simpler patterns are failing on real production queries.

Further Reading

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260505-0800

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