---
title: "What is agentic RAG?"
description: "Agentic RAG lets an agent call retrieval as a tool against a durable store of passages, frames, and transcripts, then retrieve again or answer from those rows."
url: "https://www.pixeltable.com/learn/what-is-agentic-rag"
updated: "2026-10-12"
vertical: "Retrieval"
doc: "https://docs.pixeltable.com/datastore/embedding-index"
---

# What is agentic RAG?

Agentic RAG is retrieval an agent can call as a tool, then decide whether to retrieve again, inspect a source, or answer. The corpus, the index, and the evidence live in a durable store, so later turns reuse the same rows instead of restuffing a growing prompt.

Updated: 2026-10-12
Part of [What is RAG?](https://www.pixeltable.com/learn/what-is-rag).

## On this page

- [How it works](https://www.pixeltable.com/learn/what-is-agentic-rag#how-it-works)
- [What it is not](https://www.pixeltable.com/learn/what-is-agentic-rag#what-it-is-not)
- [Comparison](https://www.pixeltable.com/learn/what-is-agentic-rag#comparison)
- [Where Pixeltable fits](https://www.pixeltable.com/learn/what-is-agentic-rag#where-pixeltable-fits)
- [Questions](https://www.pixeltable.com/learn/what-is-agentic-rag#questions)

## How it works {#how-it-works}


- The agent has a retrieval tool, not a single retrieve-then-generate chain.
- Evidence is stored as rows — passages, frames, or transcripts — that later turns can query again.
- The next action can retrieve, inspect a source file, or generate, using persisted artifacts rather than only the last prompt.

## What it is not {#what-it-is-not}

It is not a chat window that re-embeds a folder on every question, and it is not an agent framework with no store underneath.

## agentic RAG: this, and the thing it is confused with {#comparison}

|  | This | Not this |
| --- | --- | --- |
| Who retrieves | The agent, as a tool call | A single pipeline step |
| Memory | Rows that survive the turn | The current prompt |
| Evidence | Passages, frames, transcripts | A vector with no source file |

## Where Pixeltable fits {#where-pixeltable-fits}

Pixeltable is the durable multimodal backend those tools query. @pxt.query wraps similarity search; FastAPIRouter can expose it. Database, orchestration, and serving stay in one Python file. The same file deploys to Pixeltable Cloud (Limited Beta).

```python
import pixeltable as pxt
from pixeltable.functions.document import document_splitter
from pixeltable.functions.huggingface import sentence_transformer
from pixeltable.serving import FastAPIRouter

TableModel = pxt.model_base()
text_embed = sentence_transformer.using(model_id='all-MiniLM-L6-v2')

class Docs(TableModel, name='docs'):
    document: pxt.Document

class Chunks(
    TableModel,
    name='chunks',
    base=Docs,
    iterator=document_splitter(document=Docs.document, separators='sentence'),
):
    __indexes__ = [
        pxt.EmbeddingIndex(text, string_embed=text_embed),
    ]

@pxt.query
def retrieve(question: str, limit: int = 5):
    sim = Chunks.text.similarity(string=question)
    return Chunks.order_by(sim, asc=False).limit(limit).select(Chunks.text, sim)

api = FastAPIRouter(name='agent-rag')
api.add_query_route(path='/retrieve', query=retrieve)
```

## Questions {#questions}

### How do agents use a durable multimodal backend? {#faq-1}

The backend holds the corpus and the derived pieces as rows. The agent calls a retrieval function, gets passages, frames, or transcripts, and can retrieve again on the next turn. Pixeltable is that store: tables, indexes, and an HTTP route in one file.

### Is agentic RAG a new model? {#faq-2}

No. It is a retrieval loop the agent controls. The model still answers from retrieved evidence. What changes is ownership of the corpus: a durable table instead of a prompt that forgets.

### Do I still need a separate vector database? {#faq-3}

The agent needs an embedding index so similar pieces can be found. That index can live on the table that holds the chunks or frames. A separate vector database is optional; it is not the agent’s memory by itself.

## Related

- [Documentation](https://docs.pixeltable.com/datastore/embedding-index)
- [Turn Any Database Table into an AI Tool: Introducing pxt.retrieval_udf()](https://www.pixeltable.com/blog/retrieval-udf-database-ai-tool)
- [Context Graphs and Decision Traces: Building the Data Plane for Agentic AI](https://www.pixeltable.com/blog/context-graphs-decision-traces-agentic-ai)
- [Production Multimodal RAG with Pixeltable: Dev to Deployment](https://www.pixeltable.com/blog/multimodal-rag-production)
- [AI agents and MCP](https://www.pixeltable.com/use-cases/ai-agents-mcp)
- [Video search engine in Python](https://www.pixeltable.com/use-cases/video-search-engine)
- [Production RAG](https://www.pixeltable.com/use-cases/production-rag-implementation)