# Bringing custom knowledge to agents with AutoRAG

DevFeed: [Bringing custom knowledge to agents with AutoRAG](<https://devfeed.tech/articles/bringing-custom-knowledge-to-agents-with-autorag-12353.md>)

Original publisher: [Read original article](<https://developers.redhat.com/articles/2026/09/11/bringing-custom-knowledge-agents-autorag>)

Author: Diego Alvarez Ponce, Diego Garcia Perez

Published: 2026-09-11T13:01:50Z

Content type: article

Language: en

Sources: [Red Hat](<https://devfeed.tech/sources/red-hat.md>), [Red Hat Developer](<https://devfeed.tech/sources/red-hat-developer.md>)

Topics: [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Model Context Protocol (MCP)](<https://devfeed.tech/topics/model-context-protocol-mcp.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [database](<https://devfeed.tech/tags/database.md>), [developer](<https://devfeed.tech/tags/developer.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [model-context-protocol-mcp](<https://devfeed.tech/tags/model-context-protocol-mcp.md>), [rag](<https://devfeed.tech/tags/rag.md>), [red-hat](<https://devfeed.tech/tags/red-hat.md>), [terminal](<https://devfeed.tech/tags/terminal.md>)

## AI overview

This guide explains how to use AutoRAG in Red Hat OpenShift AI to optimize retrieval-augmented generation for enterprise, domain-specific data. It also shows how to integrate a Model Context Protocol server that retrieves customer information from an internal database, using a fictional banking scenario.

## Source excerpt

The large language models (LLMs) that power generative AI work by drawing upon the patterns and information present in their training data. Without access to the right data, LLMs struggle to comprehend context--like our internal corporate vocabulary--and as a result, start to hallucinate. The post Bringing custom knowledge to agents with AutoRAG appeared first on Red Hat Developer.