📢 Announcing Built-In Knowledge for Azure Logic Apps

Today at Microsoft Build 2026, we are announcing the Public Preview of built-in Knowledge for Azure Logic Apps. It is a managed knowledge layer that transforms your documents into a ready-to-use knowledge base, removing the need to build custom Retrieval-Augmented Generation (RAG) pipeline, operate a vector store, or maintain retrieval logic. The result is grounded, accurate answers for the agents and workflows you are building today.

Most organizations hold a significant amount of institutional knowledge such as HR policies, product manuals, support runbooks, contracts, and specifications distributed across documents, spreadsheets, and internal systems. The challenge has rarely been the availability of content. It has been making that content reliably and accurately retrievable by AI agents and workflows.

Until now, addressing this challenge required building a RAG pipeline in-house. As any team that has implemented one can attest, a production-grade RAG pipeline involves substantial engineering effort and ongoing operational overhead.

The complexity of building RAG in-house

A production-grade RAG pipeline is not a single component. It is a set of interdependent systems that must be designed, integrated, and maintained:

• Ingestion: parsing multiple file formats, chunking content appropriately, summarizing, and generating embeddings.
• Storage: provisioning a vector database, defining indexing policies, and tuning for cost and performance.
• Retrieval: rewriting queries, vectorizing them, executing semantic search, and returning the most relevant chunks to the model.
• Operations: monitoring upload status, handling failures, managing credentials, and maintaining security.

Each component represents a meaningful engineering investment. Together, they constitute a platform — one that diverts engineering capacity away from the business problems teams set out to solve.

Introducing built-in Knowledge capability 

Built-in Knowledge in Logic Apps is a managed knowledge layer built into Azure Logic Apps that turns your documents into a ready-to-use knowledge base, without requiring you to build or operate a RAG pipeline.

You provide the documents, and the platform manages the remainder of the process, both ingestion and retrieval, end to end. Built directly into Logic Apps, KBaaS provides an abstraction over the underlying vector store and AI models, enabling your workflows to consume structured, semantically searchable knowledge through a single connection.

A knowledge base is a logical container that organizes related sources for a given domain. For example, an "HR Policies" knowledge base might hold all relevant HR documents. You create the knowledge base, upload your files, and attach it as a tool that your agent can call.

How it works 

Managed Knowledge experience is built around two managed pipelines.

Ingestion pipeline. When you upload a knowledge source, the service automatically parses, chunks, summarizes, and vectorizes the content, then stores the results, with no manual preprocessing required. The current preview supports a broad range of formats out of the box: DOC, DOCX, HTML, MD, PDF, PPT, PPTX, TXT, XLS, and XLSX. Each upload provides a progress status and a clear Completed or Failed result.

Retrieval pipeline. When your agent queries the knowledge base, the service rewrites the query where beneficial, generates a vector representation, executes a semantic search, and returns the most relevant chunks to the language model for response generation. Query planning, vector search, and ranking are all handled by the service.

The outcome is that your agents receive accurate, context-rich answers grounded in your own content, without requiring you to author retrieval logic.

Built for agentic workflows 

Knowledge is available in Azure Logic Apps Standard, where it integrates directly with agentic workflows. Once a knowledge base has been created, it appears as a capability that can be attached to an agent loop. From there, the agent automatically queries the knowledge base to retrieve semantically relevant information from your uploaded documents at the point it is needed, as part of completing a task.

Getting started involves three steps:

1. Create the knowledge base connection - associate your vector store and your completions and embeddings models.
2. Add knowledge sources - upload files into a knowledge base, optionally organized into groups.
3. Add the knowledge base as a context - select it from the agent node so your agent can begin retrieving.

The platform provisions and manages the required databases, containers, and indexing policies on your behalf, removing the burden of operating the underlying storage and search infrastructure.

Two SKUs to consume it - Standard or Automation

This feature is available across Logic Apps SKUs, with some differences in how you setup and manage them.

Logic Apps Standard — bring your own resources. On Standard SKU, the model operates on your own Cosmos DB vector store and AI models, KBaaS integrates with them directly. You connect your existing resources, and the platform manages the complete ingestion and retrieval pipeline on top of them. This approach retains full control over your data and models while removing the need to build and maintain the RAG pipeline.

Logic Apps Automation SKU — bring only your documents. On the Automation SKU, this feature operates on a hosted-on-behalf-of model, in which the platform provisions and manages both the underlying vector store and the AI models. There is no Cosmos DB to provision, no embeddings or completions model to deploy, and no connections to configure. You upload your documents and attach the knowledge base to your agent, and the entire knowledge layer, including the supporting infrastructure is fully managed for you.

This delivers the same managed knowledge experience with the maximum degree of abstraction, providing the most direct path from source documents to a working, agent-ready knowledge base.

Secure by design 

KBaaS supports authentication through Microsoft Entra ID using either a managed identity or an API key. We recommend managed identity wherever possible. It is the most secure option and eliminates the need to manually provision and rotate credentials, secrets, or access keys.

Available today in Public Preview 

This initial release focuses on the most common starting point: uploading unstructured documents. Additional capabilities are planned, including support for more knowledge sources, richer ingestion (such as image parsing, semantic chunking, and multimodal embeddings), configurable retrieval settings, access checks during retrieval, and more.

Built-in Knowledge in Logic Apps is available now in Public Preview. Provide your documents and give your agents a knowledge base that is ready to use, without building or operating a RAG pipeline.

• Read the MS Learn docs to get started
• Check out the demo below