---
title: "What is agentic RAG search?"
description: "Retrieval that reasons — search as a tool the AI wields, not a single lookup."
canonical: https://ocho.bot/docs/market-comparison/what-is-agentic-rag-search
last_updated: 2026-08-21
---

# What is agentic RAG search?

> Retrieval that reasons — search as a tool the AI wields, not a single lookup.

**RAG** (retrieval-augmented generation) grounds an AI's answers in your documents: before answering, the system retrieves relevant passages and the model answers *from them*, citing sources. Grounding is why a RAG answer can be trusted and a bare chatbot answer can't — the model isn't reciting from memory; it's reading your material and showing its work.

The stakes are measurable: on [Meta's CRAG benchmark](https://arxiv.org/abs/2406.04744) (4,409 questions, five domains), frontier models score **under 34% accuracy without retrieval**, and even industry-best RAG reaches only 63%. Retrieval quality — not model choice — is the gap.

Classic RAG has a weakness: it retrieves **once**. One query, one batch of passages, one answer. If the first search misses — wrong phrasing, question spanning multiple topics, information spread across documents — the model answers anyway, from thin context.

**Agentic RAG** makes retrieval a tool the AI uses deliberately, as many times as the question needs:

- It **rewrites** your conversational question into what should actually be

searched ("what about the second one?" becomes a real query).

- It **judges** its own results — and when they're weak, it broadens the

search, reformulates, and retries rather than bluffing.

- It **decomposes** multi-part questions into multiple searches and

synthesizes across them.

- It **knows when to stop**, saying "no confident matches" instead of

inventing one.

## How Ocho does it

Ocho's assistant treats document search as a first-class tool. Within a conversation it can search repeatedly, self-correct with an escalation ladder (broaden, then reformulate and retry, then stop), and every retrieved passage carries a citation to the exact page — with the passage highlighted in the source document when you click through. When retrieval genuinely fails, the answer says so instead of hallucinating; hitting an honest "no relevant sources found" is a feature.

The same engine backs three surfaces: cited chat, the [search page](/docs/search/overview) with its knowledge-graph view, and search blocks inside [Flows](/docs/flows/overview) automations — so a workflow can run the same trustworthy retrieval over thousands of documents unattended.

Grounding gets you answers you can verify. Agency gets you answers to the questions a single lookup can't reach. You want both.

Agentic search is half the story — the other half happens before any question is asked. Read: [What is agentic ingestion?](/blog/what-is-agentic-ingestion)

All docs: https://ocho.bot/docs

---

Ocho — AI knowledge orchestration · [Home](https://ocho.bot/) · [Docs](https://ocho.bot/docs) · [Blog](https://ocho.bot/blog) · [About](https://ocho.bot/about) · [Developers](https://ocho.bot/developers) · [Contact](https://ocho.bot/contact) · [llms.txt](https://ocho.bot/llms.txt)