Agentic RAG

Agentic RAG is a retrieval-augmented generation pattern where an LLM agent decides what to retrieve, when to retrieve it, and from which source.

Instead of one fixed retrieval step, the agent plans multi-step lookups, routes queries between indexes, and self-corrects when results look weak. Also known as: Adaptive RAG, Self-RAG.

Authors 5 articles 55 min total read Updated May 3, 2026

What this topic covers

Foundations — Agentic RAG turns retrieval from a fixed pipeline step into a decision the model itself makes.
Implementation — Building an agentic RAG system means wiring an agent loop on top of your retrievers, tool definitions, and evaluation hooks.
What's changing — The framework landscape around agentic RAG is moving fast, with LangGraph, LlamaIndex Workflows, and managed platforms competing on how agents plan and call retrievers.
Risks & limits — When the agent picks the sources, it also picks what the user never sees.

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Understand the Fundamentals

MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.

Concepts covered

Layered prerequisite stack of retrieval primitives feeding an agent loop with branching reliability paths

MONA explainer 11 min May 3, 2026

From RAG to Agents: Prerequisites and Hard Limits of Agentic RAG

Agentic RAG is a stack with new failure modes, not an upgrade. Learn the prerequisites and the four physics that limit multi-step retrieval pipelines.

Diagram of an LLM agent routing a query across multiple retrieval sources before answering

MONA explainer 9 min May 3, 2026

What Is Agentic RAG and How LLM Agents Decide What to Retrieve

Agentic RAG turns retrieval into a decision: an LLM agent chooses whether to retrieve, which source to query, and whether the answer is good enough.

Build with Agentic RAG

MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.

Tools & techniques

Architecture diagram of an agentic RAG pipeline with hybrid search, cross-encoder rerank, and a bounded agent loop

MAX guide 16 min May 3, 2026

How to Build Agentic RAG with LangGraph, LlamaIndex & Haystack in 2026

Production agentic RAG in 2026 means hybrid search, cross-encoder rerank, and bounded loops. Spec the pipeline before wiring LangGraph, LlamaIndex, Haystack.

What's Changing in 2026

DAN tracks how this domain is evolving — which models, techniques, and benchmarks are reshaping 2026.

Models & benchmarks

Updated May 2026

Three converging arrows representing agentic RAG framework strategies in 2026 — orchestration, retrieval, and managed platforms

DAN Analysis 9 min May 3, 2026

LangGraph, LlamaIndex Workflows, and Vectara: The Agentic RAG Framework Race in 2026

LangGraph 1.0, LlamaIndex Workflows, and Vectara are pulling agentic RAG in three directions in 2026 — orchestration, retrieval, and managed governance.

Risks and Considerations

ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.

Risks & metrics

Hand-drawn diagram of an autonomous agent selecting documents from stacked corpora, with one path marked invisible to auditors.

ALAN opinion 10 min May 3, 2026

When the Agent Picks Sources: Accountability in Agentic RAG

Agentic RAG hands source selection to autonomous LLM agents. The accountability stack — from corpus skew to bias injection — has not caught up.