Why Data Platforms Must Become Intelligence Platforms for AI Agents to Work
The promise and the gap Your organization has invested in an AI agent. You ask it: "Prepare a summary of Q3 revenue by region, including year-over-year trends and top product lines." The agent finds revenue numbers in a SQL warehouse, product metadata in Dataverse, regional mappings in SharePoint, historical data in Azure Blob Storage, and organizational context in Microsoft Graph. Five data sources. Five schemas. No shared definitions. The result? The agent hallucinates, returns incomplete data, or asks a dozen clarifying questions that defeat its purpose. This isn't a model limitation — modern AI models are highly capable. The real constraint is that enterprise data is not structured for reasoning. Traditional data platforms were built for humans to query. Intelligence platforms must be built for agents to _reason_ over. That distinction is the subject of this post. What you'll understand Why fragmented enterprise data blocks effective AI agents What distinguishes a storage platform from an intelligence platform How Microsoft Fabric and Azure AI Foundry work together to enable trustworthy, agent-ready data access The enterprise pain: Fragmented data breaks AI agents Enterprise data is spread across relational databases, data lakes, business applications, collaboration platforms, third-party APIs, and Microsoft Graph — each with its own schema and security model. Humans navigate this fragmentation through institutional knowledge and years of muscle memory. A seasoned analyst knows that "revenue" in the data warehouse means net revenue after returns, while "revenue" in the CRM means gross bookings. An AI agent does not. The cost of this fragmentation isn't hypothetical. Each new AI agent deployment can trigger another round of bespoke data preparation — custom integrations and transformation pipelines just to make data usable, let alone agent-ready. This approach doesn't scale. Why agents struggle without a semantic layer To produce a trustworthy answer, an AI agent needs: (1) **data access** to reach relevant sources, (2) **semantic context** to understand what the data _means_ (business definitions, relationships, hierarchies), and (3) **trust signals** like lineage, permissions, and freshness metadata. Traditional platforms provide the first but rarely the second or third — leaving agents to infer meaning from column names and table structures. This is fragile at best and misleading at worst. Figure 1: Without a shared semantic layer, AI agents must interpret raw, disconnected data across multiple systems — often leading to inconsistent or incomplete results. From storage to intelligence: What must change The fix isn't another ETL pipeline or another data integration tool. The fix is a fundamental shift in what we expect from a data platform. A storage platform asks: "Where is the data, and how do I access it?" An intelligence platform asks: "What does the data mean, who can use it, and how can an agent reason over it?" This shift requires four foundational pillars: Pillar 1: Unified data access OneLake, the data lake built into Microsoft Fabric, provides a single logical namespace across an organization. Whether data originates in a Fabric lakehouse, a warehouse, or an external storage account, OneLake makes it accessible through one interface — using shortcuts and mirroring rather than requiring data migration. This respects existing investments while reducing fragmentation. Pillar 2: Shared semantic layer Semantic models in Microsoft Fabric define business measures, table relationships, human-readable field descriptions, and row-level security. When an agent queries a semantic model instead of raw tables, it gets _answers_ — like `Total Revenue = $42.3M for North America in Q3` — not raw result sets requiring interpretation and aggregation. Before vs After: What changes for an agent? Without semantic layer: Queries raw tables Infers business meaning Risk of incorrect aggregation With semantic layer: Queries `[Total Revenue]` Uses business-defined logic Gets consistent, governed results Pillar 3: Context enrichment Microsoft Graph adds organizational signals — people and roles, activity patterns, and permissions — helping agents produce responses that are not just accurate, but _relevant_ and _appropriately scoped_ to the person asking. Pillar 4: Agent-ready APIs Data Agents in Microsoft Fabric (currently in preview) provide a natural-language interface to semantic models and lakehouses. Instead of generating SQL, an AI agent can ask: "What was Q3 revenue by region?" and receive a structured, sourced response. This is the critical difference: the platform provides structured context and business logic, helping reduce the reasoning burden on the agent. Figure 2: An intelligence platform adds semantic context, trust signals, and agent-ready APIs on top of unified data access — enabling AI agents to combine structured data, business definitions, and relationships to produce more consistent responses. Microsoft Fabric as the intelligence layer Microsoft Fabric is often described as a unified analytics platform. That description is accurate but incomplete. In the context of AI agents, Fabric's role is better understood as an **intelligence layer** — a platform that doesn't just store and process data, but _makes data understandable_ to autonomous systems. Let's look at each capability through the lens of agent readiness. OneLake: One namespace, many sources OneLake provides a single logical namespace backed by Azure Data Lake Storage Gen2. For AI agents, this means one authentication context, one discovery mechanism, and one governance surface. Key capabilities: **shortcuts** (reference external data without copying), **mirroring** (replicate from Azure SQL, Cosmos DB, or Snowflake), and a **unified security model**. For more on OneLake architecture, see [OneLake documentation on Microsoft Learn](https://learn.microsoft.com/fabric/onelake/onelake-overview). Semantic models: Business logic that agents can understand Semantic models (built on the Analysis Services engine) transform raw tables into business concepts: Raw Table Column Semantic Model Measure `fact_sales.amount` `[Total Revenue]` — Sum of net sales after returns `fact_sales.amount / dim_product.cost` `[Gross Margin %]` — Revenue minus COGS as a percentage `fact_sales.qty` YoY comparison `[YoY Growth %]` — Year-over-year quantity growth Code Snippet 1 — Querying a Fabric Semantic Model with Semantic Link (Python) import sempy.fabric as fabric # Query business-defined measures — no need to know underlying table schemas dax_query = """ EVALUATE SUMMARIZECOLUMNS( 'Geography'[Region], 'Calendar'[FiscalQuarter], "Total Revenue", [Total Revenue], "YoY Growth %", [YoY Growth %] ) """ result_df = fabric.evaluate_dax( dataset="Contoso Sales Analytics", workspace="Contoso Analytics Workspace", dax_string=dax_query ) print(result_df.head()) # NOTE: Output shown is illustrative and based on the semantic model definition # Output (illustrative): # Region FiscalQuarter Total Revenue YoY Growth % # North America Q3 FY2026 42300000 8.2 # Europe Q3 FY2026 31500000 5.7 Key takeaway: The agent doesn’t need to know that revenue is in `fact_sales.amount` or that fiscal quarters don’t align with calendar quarters. The semantic model handles all of this. Code Snippet 2 — Discovering Available Models and Measures (Python) Before an agent can query, it needs to _discover_ what data is available. Semantic Link provides programmatic access to model metadata — enabling agents to find relevant measures without hardcoded knowledge. import sempy.fabric as fabric # Discover available semantic models in the workspace datasets = fabric.list_datasets(workspace="Contoso Analytics Workspace") print(datasets[["Dataset Name", "Description"]]) # NOTE: Output shown is illustrative and based on the semantic model definition # Output (illustrative): # Dataset Name Description # Contoso Sales Analytics Revenue, margins, and growth metrics # Contoso HR Analytics Headcount, attrition, and hiring pipeline # Contoso Supply Chain Inventory, logistics, and supplier data # Inspect available measures — these are the business-defined metrics an agent can query measures = fabric.list_measures( dataset="Contoso Sales Analytics", workspace="Contoso Analytics Workspace" ) print(measures[["Table Name", "Measure Name", "Description"]]) # Output (illustrative): # Table Name Measure Name Description # Sales Total Revenue Sum of net sales after returns # Sales Gross Margin % Revenue minus COGS as a percentage # Sales YoY Growth % Year-over-year quantity growth Key takeaway: An agent can programmatically discover which semantic models exist and what measures they expose — turning the platform into a self-describing data catalog that agents can navigate autonomously. For more on Semantic Link, see the Semantic Link documentation on Microsoft Learn. Data Agents: Natural-language access for AI (preview) Note: Fabric Data Agents are currently in preview. See [Microsoft preview terms](https://learn.microsoft.com/legal/microsoft-fabric-preview) for details. A Data Agent wraps a semantic model and exposes it as a natural-language-queryable endpoint. An AI Foundry agent can register a Fabric Data Agent as a tool — when it needs data, it calls the Data Agent like any other tool. Important: In production scenarios, use managed identities or Microsoft Entra ID authentication. Always follow the [principle of least privilege](https://learn.microsoft.com/entra/identity-platform/secure-least-privileged-access) when configuring agent access. Microsoft Graph: Organizational context Microsoft Graph adds the final layer: who is asking (role-appropriate detail), what’s relevant (trending datasets), and who should review (data stewards). Fabric’s integration with Graph brings these signals into the data platform so agents produce contextually appropriate responses. Tying it together: Azure AI Foundry + Microsoft Fabric The real power of the intelligence platform concept emerges when you see how Azure AI Foundry and Microsoft Fabric are designed to work together. The integration pattern Azure AI Foundry provides the orchestration layer (conversations, tool selection, safety, response generation). Microsoft Fabric provides the data intelligence layer (data access, semantic context, structured query resolution). The integration follows a tool-calling pattern: 1.User prompt → End user asks a question through an AI Foundry-powered application. 2.Tool call → The agent selects the appropriate Fabric Data Agent and sends a natural-language query. 3.Semantic resolution → The Data Agent translates the query into DAX against the semantic model and executes it via OneLake. 4.Structured response → Results flow back through the stack, with each layer adding context (business definitions, permissions verification, data lineage). 5.User response → The AI Foundry agent presents a grounded, sourced answer to the user. Why these matters No custom ETL for agents — Agents query the intelligence platform directly No prompt-stuffing — The semantic model provides business context at query time No trust gap — Governed semantic models enforce row-level security and lineage No one-off integrations — Multiple agents reuse the same Data Agents Code Snippet 3 — Azure AI Foundry Agent with Fabric Data Agent Tool (Python) The following example shows how an Azure AI Foundry agent registers a Fabric Data Agent as a tool and uses it to answer a business question. The agent handles tool selection, query routing, and response grounding automatically. from azure.ai.projects import AIProjectClient from azure.ai.projects.models import FabricTool from azure.identity import DefaultAzureCredential # Connect to Azure AI Foundry project project_client = AIProjectClient.from_connection_string( credential=DefaultAzureCredential(), conn_str="<your-ai-foundry-connection-string>" ) # Register a Fabric Data Agent as a grounding tool # The connection references a Fabric workspace with semantic models fabric_tool = FabricTool(connection_id="<fabric-connection-id>") # Create an agent that uses the Fabric Data Agent for data queries agent = project_client.agents.create_agent( model="gpt-4o", name="Contoso Revenue Analyst", instructions="""You are a business analytics assistant for Contoso. Use the Fabric Data Agent tool to answer questions about revenue, margins, and growth. Always cite the source semantic model.""", tools=fabric_tool.definitions ) # Start a conversation thread = project_client.agents.create_thread() message = project_client.agents.create_message( thread_id=thread.id, role="user", content="What was Q3 revenue by region, and which region grew fastest?" ) # The agent automatically calls the Fabric Data Agent tool, # queries the semantic model, and returns a grounded response run = project_client.agents.create_and_process_run( thread_id=thread.id, agent_id=agent.id ) # Retrieve the agent's response messages = project_client.agents.list_messages(thread_id=thread.id) print(messages.data[0].content[0].text.value) # NOTE: Output shown is illustrative and based on the semantic model definition # Output (illustrative): # "Based on the Contoso Sales Analytics model, Q3 FY2026 revenue by region: # - North America: $42.3M (+8.2% YoY) # - Europe: $31.5M (+5.7% YoY) # - Asia Pacific: $18.9M (+12.1% YoY) — fastest growing # Source: Contoso Sales Analytics semantic model, OneLake" Key takeaway: The AI Foundry agent never writes SQL or DAX. It calls the Fabric Data Agent as a tool, which resolves the query against the semantic model. The response comes back grounded with source attribution — matching the five-step integration pattern described above. Figure 3: Each layer adds context — semantic models provide business definitions, Graph adds permissions awareness, and Data Agents provide the natural-language interface. Getting started: Practical next steps You don't need to redesign your entire data platform to begin this shift. Start with one high-value domain and expand incrementally. Step 1: Consolidate data access through OneLake Create OneLake shortcuts to your most critical data sources — core business metrics, customer data, financial records. No migration needed. [Create OneLake shortcuts](https://learn.microsoft.com/fabric/onelake/create-onelake-shortcut) Step 2: Build semantic models with business definitions For each major domain (sales, finance, operations), create a semantic model with key measures, table relationships, human-readable descriptions, and row-level security. [Create semantic models in Microsoft Fabric](https://learn.microsoft.com/fabric/data-warehouse/semantic-models) Step 3: Enable Data Agents (preview) Expose your semantic models as natural-language endpoints. Start with a single domain to validate the pattern. Note: Review the [preview terms](https://learn.microsoft.com/legal/microsoft-fabric-preview) and plan for API changes. [Fabric Data Agents overview](https://learn.microsoft.com/fabric/data-science/concept-data-agent) Step 4: Connect Azure AI Foundry agents Register Data Agents as tools in your AI Foundry agent configuration. Azure AI Foundry documentation Conclusion: The bottleneck isn't the model — it's the platform Models can reason, plan, and hold multi-turn conversations. But in the enterprise, the bottleneck for effective AI agents is the data platform underneath. Agents can’t reason over data they can’t find, apply business logic that isn’t encoded, respect permissions that aren’t enforced, or cite sources without lineage. The shift from storage to intelligence requires unified data access, a shared semantic layer, organizational context, and agent-ready APIs. Microsoft Fabric provides these capabilities, and its integration with Azure AI Foundry makes this intelligence layer accessible to AI agents. Disclaimer: Some features described in this post, including Fabric Data Agents, are currently in preview. Preview features may change before general availability, and their availability, functionality, and pricing may differ from the final release. See [Microsoft preview terms](https://learn.microsoft.com/legal/microsoft-fabric-preview) for details.Building Your First Local RAG Application with Foundry Local
A developer's guide to building an offline, mobile-responsive AI support agent using Retrieval-Augmented Generation, the Foundry Local SDK, and JavaScript. Imagine you are a gas field engineer standing beside a pipeline in a remote location. There is no Wi-Fi, no mobile signal, and you need a safety procedure right now. What do you do? This is the exact problem that inspired this project: a fully offline RAG-powered support agent that runs entirely on your machine. No cloud. No API keys. No outbound network calls. Just a local language model, a local vector store, and your own documents, all accessible from a browser on any device. In this post, you will learn how it works, how to build your own, and the key architectural decisions behind it. If you have ever wanted to build an AI application that runs locally and answers questions grounded in your own data, this is the place to start. The finished application: a browser-based AI support agent that runs entirely on your machine. What Is Retrieval-Augmented Generation? Retrieval-Augmented Generation (RAG) is a pattern that makes AI models genuinely useful for domain-specific tasks. Rather than hoping the model "knows" the answer from its training data, you: Retrieve relevant chunks from your own documents using a vector store Augment the model's prompt with those chunks as context Generate a response grounded in your actual data The result is fewer hallucinations, traceable answers with source attribution, and an AI that works with your content rather than relying on general knowledge. If you are building internal tools, customer support bots, field manuals, or knowledge bases, RAG is the pattern you want. RAG vs CAG: Understanding the Trade-offs If you have explored AI application patterns before, you have likely encountered Context-Augmented Generation (CAG). Both RAG and CAG solve the same core problem: grounding an AI model's answers in your own content. They take different approaches, and each has genuine strengths and limitations. RAG (Retrieval-Augmented Generation) How it works: Documents are split into chunks, vectorised, and stored in a database. At query time, the most relevant chunks are retrieved and injected into the prompt. Strengths: Scales to thousands or millions of documents Fine-grained retrieval at chunk level with source attribution Documents can be added or updated dynamically without restarting Token-efficient: only relevant chunks are sent to the model Supports runtime document upload via the web UI Limitations: More complex architecture: requires a vector store and chunking strategy Retrieval quality depends on chunking parameters and scoring method May miss relevant content if the retrieval step does not surface it CAG (Context-Augmented Generation) How it works: All documents are loaded at startup. The most relevant ones are selected per query using keyword scoring and injected into the prompt. Strengths: Drastically simpler architecture with no vector database or embeddings All information is always available to the model Minimal dependencies and easy to set up Near-instant document selection Limitations: Constrained by the model's context window size Best suited to small, curated document sets (tens of documents) Adding documents requires an application restart Want to compare these patterns hands-on? There is a CAG-based implementation of the same gas field scenario using whole-document context injection. Clone both repositories, run them side by side, and see how the architectures differ in practice. When Should You Choose Which? Consideration Choose RAG Choose CAG Document count Hundreds or thousands Tens of documents Document updates Frequent or dynamic (runtime upload) Infrequent (restart to reload) Source attribution Per-chunk with relevance scores Per-document Setup complexity Moderate (ingestion step required) Minimal Query precision Better for large or diverse collections Good for keyword-matchable content Infrastructure SQLite vector store (single file) None beyond the runtime For the sample application in this post (20 gas engineering procedure documents with runtime upload), RAG is the clear winner. If your document set is small and static, CAG may be simpler. Both patterns run fully offline using Foundry Local. Foundry Local: Your On-Device AI Runtime Foundry Local is a lightweight runtime from Microsoft that downloads, manages, and serves language models entirely on your device. No cloud account, no API keys, no outbound network calls (after the initial model download). What makes it particularly useful for developers: No GPU required: runs on CPU or NPU, making it accessible on standard laptops and desktops Native SDK bindings: in-process inference via the foundry-local-sdk npm package, with no HTTP round-trips to a local server Automatic model management: downloads, caches, and loads models automatically Hardware-optimised variant selection: the SDK picks the best variant for your hardware (GPU, NPU, or CPU) Real-time progress callbacks: ideal for building loading UIs that show download and initialisation progress The integration code is refreshingly minimal: import { FoundryLocalManager } from "foundry-local-sdk"; // Create a manager and discover models via the catalogue const manager = FoundryLocalManager.create({ appName: "gas-field-local-rag" }); const model = await manager.catalog.getModel("phi-3.5-mini"); // Download if not cached, then load into memory if (!model.isCached) { await model.download((progress) => { console.log(`Download: ${Math.round(progress * 100)}%`); }); } await model.load(); // Create a chat client for direct in-process inference const chatClient = model.createChatClient(); const response = await chatClient.completeChat([ { role: "system", content: "You are a helpful assistant." }, { role: "user", content: "How do I detect a gas leak?" } ]); That is it. No server configuration, no authentication tokens, no cloud provisioning. The model runs in the same process as your application. The Technology Stack The sample application is deliberately simple. No frameworks, no build steps, no Docker: Layer Technology Purpose AI Model Foundry Local + Phi-3.5 Mini Runs locally via native SDK bindings, no GPU required Back end Node.js + Express Lightweight HTTP server, everyone knows it Vector Store SQLite (via better-sqlite3 ) Zero infrastructure, single file on disc Retrieval TF-IDF + cosine similarity No embedding model required, fully offline Front end Single HTML file with inline CSS No build step, mobile-responsive, field-ready The total dependency footprint is three npm packages: express , foundry-local-sdk , and better-sqlite3 . Architecture Overview The five-layer architecture, all running on a single machine. The system has five layers, all running on a single machine: Client layer: a single HTML file served by Express, with quick-action buttons and a responsive chat interface Server layer: Express.js starts immediately and serves the UI plus SSE status and chat endpoints RAG pipeline: the chat engine orchestrates retrieval and generation; the chunker handles TF-IDF vectorisation; the prompts module provides safety-first system instructions Data layer: SQLite stores document chunks and their TF-IDF vectors; documents live as .md files in the docs/ folder AI layer: Foundry Local runs Phi-3.5 Mini on CPU or NPU via native SDK bindings Building the Solution Step by Step Prerequisites You need two things installed on your machine: Node.js 20 or later: download from nodejs.org Foundry Local: Microsoft's on-device AI runtime: winget install Microsoft.FoundryLocal The SDK will automatically download the Phi-3.5 Mini model (approximately 2 GB) the first time you run the application. Getting the Code Running # Clone the repository git clone https://github.com/leestott/local-rag.git cd local-rag # Install dependencies npm install # Ingest the 20 gas engineering documents into the vector store npm run ingest # Start the server npm start Open http://127.0.0.1:3000 in your browser. You will see the status indicator whilst the model loads. Once the model is ready, the status changes to "Offline Ready" and you can start chatting. Desktop view Mobile view How the RAG Pipeline Works Let us trace what happens when a user asks: "How do I detect a gas leak?" The query flow from browser to model and back. 1 Documents are ingested and indexed When you run npm run ingest , every .md file in the docs/ folder is read, parsed (with optional YAML front-matter for title, category, and ID), split into overlapping chunks of approximately 200 tokens, and stored in SQLite with TF-IDF vectors. 2 Model is loaded via the SDK The Foundry Local SDK discovers the model in the local catalogue and loads it into memory. If the model is not already cached, it downloads it first (with progress streamed to the browser via SSE). 3 User sends a question The question arrives at the Express server. The chat engine converts it into a TF-IDF vector, uses an inverted index to find candidate chunks, and scores them using cosine similarity. The top 3 chunks are returned in under 1 ms. 4 Prompt is constructed The engine builds a messages array containing: the system prompt (with safety-first instructions), the retrieved chunks as context, the conversation history, and the user's question. 5 Model generates a grounded response The prompt is sent to the locally loaded model via the Foundry Local SDK's native chat client. The response streams back token by token through Server-Sent Events to the browser. Source references with relevance scores are included. A response with safety warnings and step-by-step guidance The sources panel shows which chunks were used and their relevance Key Code Walkthrough The Vector Store (TF-IDF + SQLite) The vector store uses SQLite to persist document chunks alongside their TF-IDF vectors. At query time, an inverted index finds candidate chunks that share terms with the query, then cosine similarity ranks them: // src/vectorStore.js search(query, topK = 5) { const queryTf = termFrequency(query); this._ensureCache(); // Build in-memory cache on first access // Use inverted index to find candidates sharing at least one term const candidateIndices = new Set(); for (const term of queryTf.keys()) { const indices = this._invertedIndex.get(term); if (indices) { for (const idx of indices) candidateIndices.add(idx); } } // Score only candidates, not all rows const scored = []; for (const idx of candidateIndices) { const row = this._rowCache[idx]; const score = cosineSimilarity(queryTf, row.tf); if (score > 0) scored.push({ ...row, score }); } scored.sort((a, b) => b.score - a.score); return scored.slice(0, topK); } The inverted index, in-memory row cache, and prepared SQL statements bring retrieval time to sub-millisecond for typical query loads. Why TF-IDF Instead of Embeddings? Most RAG tutorials use embedding models for retrieval. This project uses TF-IDF because: Fully offline: no embedding model to download or run Zero latency: vectorisation is instantaneous (it is just maths on word frequencies) Good enough: for 20 domain-specific documents, TF-IDF retrieves the right chunks reliably Transparent: you can inspect the vocabulary and weights, unlike neural embeddings For larger collections or when semantic similarity matters more than keyword overlap, you would swap in an embedding model. For this use case, TF-IDF keeps the stack simple and dependency-free. The System Prompt For safety-critical domains, the system prompt is engineered to prioritise safety, prevent hallucination, and enforce structured responses: // src/prompts.js export const SYSTEM_PROMPT = `You are a local, offline support agent for gas field inspection and maintenance engineers. Behaviour Rules: - Always prioritise safety. If a procedure involves risk, explicitly call it out. - Do not hallucinate procedures, measurements, or tolerances. - If the answer is not in the provided context, say: "This information is not available in the local knowledge base." Response Format: - Summary (1-2 lines) - Safety Warnings (if applicable) - Step-by-step Guidance - Reference (document name + section)`; This pattern is transferable to any safety-critical domain: medical devices, electrical work, aviation maintenance, or chemical handling. Runtime Document Upload Unlike the CAG approach, RAG supports adding documents without restarting the server. Click the upload button to add new .md or .txt files. They are chunked, vectorised, and indexed immediately. The upload modal with the complete list of indexed documents. Adapting This for Your Own Domain The sample project is designed to be forked and adapted. Here is how to make it yours in four steps: 1. Replace the documents Delete the gas engineering documents in docs/ and add your own markdown files. The ingestion pipeline handles any markdown content with optional YAML front-matter: --- title: Troubleshooting Widget Errors category: Support id: KB-001 --- # Troubleshooting Widget Errors ...your content here... 2. Edit the system prompt Open src/prompts.js and rewrite the system prompt for your domain. Keep the structure (summary, safety, steps, reference) and update the language to match your users' expectations. 3. Tune the retrieval In src/config.js : chunkSize: 200 : smaller chunks give more precise retrieval, less context per chunk chunkOverlap: 25 : prevents information falling between chunks topK: 3 : how many chunks to retrieve per query (more gives more context but slower generation) 4. Swap the model Change config.model in src/config.js to any model available in the Foundry Local catalogue. Smaller models give faster responses on constrained devices; larger models give better quality. Building a Field-Ready UI The front end is a single HTML file with inline CSS. No React, no build tooling, no bundler. This keeps the project accessible to beginners and easy to deploy. Design decisions that matter for field use: Dark, high-contrast theme with 18px base font size for readability in bright sunlight Large touch targets (minimum 44px) for operation with gloves or PPE Quick-action buttons that wrap on mobile so all options are visible without scrolling Responsive layout that works from 320px to 1920px+ screen widths Streaming responses via SSE, so the user sees tokens arriving in real time The mobile chat experience, optimised for field use. Testing The project includes unit tests using the built-in Node.js test runner, with no extra test framework needed: # Run all tests npm test Tests cover the chunker, vector store, configuration, and server endpoints. Use them as a starting point when you adapt the project for your own domain. Ideas for Extending the Project Once you have the basics running, there are plenty of directions to explore: Embedding-based retrieval: use a local embedding model for better semantic matching on diverse queries Conversation memory: persist chat history across sessions using local storage or a lightweight database Multi-modal support: add image-based queries (photographing a fault code, for example) PWA packaging: make it installable as a standalone offline application on mobile devices Hybrid retrieval: combine TF-IDF keyword search with semantic embeddings for best results Try the CAG approach: compare with the local-cag sample to see which pattern suits your use case Ready to Build Your Own? Clone the RAG sample, swap in your own documents, and have an offline AI agent running in minutes. Or compare it with the CAG approach to see which pattern suits your use case best. Get the RAG Sample Get the CAG Sample Summary Building a local RAG application does not require a PhD in machine learning or a cloud budget. With Foundry Local, Node.js, and SQLite, you can create a fully offline, mobile-responsive AI agent that answers questions grounded in your own documents. The key takeaways: RAG is ideal for scalable, dynamic document sets where you need fine-grained retrieval with source attribution. Documents can be added at runtime without restarting. CAG is simpler when you have a small, stable set of documents that fit in the context window. See the local-cag sample to compare. Foundry Local makes on-device AI accessible: native SDK bindings, in-process inference, automatic model selection, and no GPU required. TF-IDF + SQLite is a viable vector store for small-to-medium collections, with sub-millisecond retrieval thanks to inverted indexing and in-memory caching. Start simple, iterate outwards. Begin with RAG and a handful of documents. If your needs are simpler, try CAG. Both patterns run entirely offline. Clone the repository, swap in your own documents, and start building. The best way to learn is to get your hands on the code. This project is open source under the MIT licence. It is a scenario sample for learning and experimentation, not production medical or safety advice. local-rag on GitHub · local-cag on GitHub · Foundry LocalBuilding an Offline AI Interview Coach with Foundry Local, RAG, and SQLite
How to build a 100% offline, AI-powered interview preparation tool using Microsoft Foundry Local, Retrieval-Augmented Generation, and nothing but JavaScript. Foundry Local 100% Offline RAG + TF-IDF JavaScript / Node.js Contents Introduction What is RAG and Why Offline? Architecture Overview Setting Up Foundry Local Building the RAG Pipeline The Chat Engine Dual Interfaces: Web & CLI Testing Adapting for Your Own Use Case What I Learned Getting Started Introduction Imagine preparing for a job interview with an AI assistant that knows your CV inside and out, understands the job you're applying for, and generates tailored questions, all without ever sending your data to the cloud. That's exactly what Interview Doctor does. Interview Doctor's web UI, a polished, dark-themed interface running entirely on your local machine. In this post, I'll walk you through how I built an interview prep tool as a fully offline JavaScript application using: Foundry Local — Microsoft's on-device AI runtime SQLite — for storing document chunks and TF-IDF vectors RAG (Retrieval-Augmented Generation) — to ground the AI in your actual documents Express.js — for the web server Node.js built-in test runner — for testing with zero extra dependencies No cloud. No API keys. No internet required. Everything runs on your machine. What is RAG and Why Does It Matter? Retrieval-Augmented Generation (RAG) is a pattern that makes AI models dramatically more useful for domain-specific tasks. Instead of relying solely on what a model learned during training (which can be outdated or generic), RAG: Retrieves relevant chunks from your own documents Augments the model's prompt with those chunks as context Generates a response grounded in your actual data For Interview Doctor, this means the AI doesn't just ask generic interview questions, it asks questions specific to your CV, your experience, and the specific job you're applying for. Why Offline RAG? Privacy is the obvious benefit, your CV and job applications never leave your device. But there's more: No API costs — run as many queries as you want No rate limits — iterate rapidly during your prep Works anywhere — on a plane, in a café with bad Wi-Fi, anywhere Consistent performance — no cold starts, no API latency Architecture Overview Complete architecture showing all components and data flow. The application has two interfaces (CLI and Web) that share the same core engine: Document Ingestion — PDFs and markdown files are chunked and indexed Vector Store — SQLite stores chunks with TF-IDF vectors Retrieval — queries are matched against stored chunks using cosine similarity Generation — relevant chunks are injected into the prompt sent to the local LLM Step 1: Setting Up Foundry Local First, install Foundry Local: # Windows winget install Microsoft.FoundryLocal # macOS brew install microsoft/foundrylocal/foundrylocal The JavaScript SDK handles everything else — starting the service, downloading the model, and connecting: import { FoundryLocalManager } from "foundry-local-sdk"; import { OpenAI } from "openai"; const manager = new FoundryLocalManager(); const modelInfo = await manager.init("phi-3.5-mini"); // Foundry Local exposes an OpenAI-compatible API const openai = new OpenAI({ baseURL: manager.endpoint, // Dynamic port, discovered by SDK apiKey: manager.apiKey, }); ⚠️ Key Insight Foundry Local uses a dynamic port never hardcode localhost:5272 . Always use manager.endpoint which is discovered by the SDK at runtime. Step 2: Building the RAG Pipeline Document Chunking Documents are split into overlapping chunks of ~200 tokens. The overlap ensures important context isn't lost at chunk boundaries: export function chunkText(text, maxTokens = 200, overlapTokens = 25) { const words = text.split(/\s+/).filter(Boolean); if (words.length <= maxTokens) return [text.trim()]; const chunks = []; let start = 0; while (start < words.length) { const end = Math.min(start + maxTokens, words.length); chunks.push(words.slice(start, end).join(" ")); if (end >= words.length) break; start = end - overlapTokens; } return chunks; } Why 200 tokens with 25-token overlap? Small chunks keep retrieved context compact for the model's limited context window. Overlap prevents information loss at boundaries. And it's all pure string operations, no dependencies needed. TF-IDF Vectors Instead of using a separate embedding model (which would consume precious memory alongside the LLM), we use TF-IDF, a classic information retrieval technique: export function termFrequency(text) { const tf = new Map(); const tokens = text .toLowerCase() .replace(/[^a-z0-9\-']/g, " ") .split(/\s+/) .filter((t) => t.length > 1); for (const t of tokens) { tf.set(t, (tf.get(t) || 0) + 1); } return tf; } export function cosineSimilarity(a, b) { let dot = 0, normA = 0, normB = 0; for (const [term, freq] of a) { normA += freq * freq; if (b.has(term)) dot += freq * b.get(term); } for (const [, freq] of b) normB += freq * freq; if (normA === 0 || normB === 0) return 0; return dot / (Math.sqrt(normA) * Math.sqrt(normB)); } Each document chunk becomes a sparse vector of word frequencies. At query time, we compute cosine similarity between the query vector and all stored chunk vectors to find the most relevant matches. SQLite as a Vector Store Chunks and their TF-IDF vectors are stored in SQLite using sql.js (pure JavaScript — no native compilation needed): export class VectorStore { // Created via: const store = await VectorStore.create(dbPath) insert(docId, title, category, chunkIndex, content) { const tf = termFrequency(content); const tfJson = JSON.stringify([...tf]); this.db.run( "INSERT INTO chunks (...) VALUES (?, ?, ?, ?, ?, ?)", [docId, title, category, chunkIndex, content, tfJson] ); this.save(); } search(query, topK = 5) { const queryTf = termFrequency(query); // Score each chunk by cosine similarity, return top-K } } 💡 Why SQLite for Vectors? For a CV plus a few job descriptions (dozens of chunks), brute-force cosine similarity over SQLite rows is near-instant (~1ms). No need for Pinecone, Qdrant, or Chroma — just a single .db file on disk. Step 3: The RAG Chat Engine The chat engine ties retrieval and generation together: async *queryStream(userMessage, history = []) { // 1. Retrieve relevant CV/JD chunks const chunks = this.retrieve(userMessage); const context = this._buildContext(chunks); // 2. Build the prompt with retrieved context const messages = [ { role: "system", content: SYSTEM_PROMPT }, { role: "system", content: `Retrieved context:\n\n${context}` }, ...history, { role: "user", content: userMessage }, ]; // 3. Stream from the local model const stream = await this.openai.chat.completions.create({ model: this.modelId, messages, temperature: 0.3, stream: true, }); // 4. Yield chunks as they arrive for await (const chunk of stream) { const content = chunk.choices[0]?.delta?.content; if (content) yield { type: "text", data: content }; } } The flow is straightforward: vectorize the query, retrieve with cosine similarity, build a prompt with context, and stream from the local LLM. The temperature: 0.3 keeps responses focused — important for interview preparation where consistency matters. Step 4: Dual Interfaces — Web & CLI Web UI The web frontend is a single HTML file with inline CSS and JavaScript — no build step, no framework, no React or Vue. It communicates with the Express backend via REST and SSE: File upload via multipart/form-data Streaming chat via Server-Sent Events (SSE) Quick-action buttons for common follow-up queries (coaching tips, gap analysis, mock interview) The setup form with job title, seniority level, and a pasted job description — ready to generate tailored interview questions. CLI The CLI provides the same experience in the terminal with ANSI-coloured output: npm run cli It walks you through uploading your CV, entering the job details, and then generates streaming questions. Follow-up questions work interactively. Both interfaces share the same ChatEngine class, they're thin layers over identical logic. Edge Mode For constrained devices, toggle Edge mode to use a compact system prompt that fits within smaller context windows: Edge mode activated, uses a minimal prompt for devices with limited resources. Step 5: Testing Tests use the Node.js built-in test runner, no Jest, no Mocha, no extra dependencies: import { describe, it } from "node:test"; import assert from "node:assert/strict"; describe("chunkText", () => { it("returns single chunk for short text", () => { const chunks = chunkText("short text", 200, 25); assert.equal(chunks.length, 1); }); it("maintains overlap between chunks", () => { // Verifies overlapping tokens between consecutive chunks }); }); npm test Tests cover the chunker, vector store, config, prompts, and server API contract, all without needing Foundry Local running. Adapting for Your Own Use Case Interview Doctor is a pattern, not just a product. You can adapt it for any domain: What to Change How Domain documents Replace files in docs/ with your content System prompt Edit src/prompts.js Chunk sizes Adjust config.chunkSize and config.chunkOverlap Model Change config.model — run foundry model list UI Modify public/index.html — it's a single file Ideas for Adaptation Customer support bot — ingest your product docs and FAQs Code review assistant — ingest coding standards and best practices Study guide — ingest textbooks and lecture notes Compliance checker — ingest regulatory documents Onboarding assistant — ingest company handbooks and processes What I Learned Offline AI is production-ready. Foundry Local + small models like Phi-3.5 Mini are genuinely useful for focused tasks. You don't need vector databases for small collections. SQLite + TF-IDF is fast, simple, and has zero infrastructure overhead. RAG quality depends on chunking. Getting chunk sizes right for your use case is more impactful than the retrieval algorithm. The OpenAI-compatible API is a game-changer. Switching from cloud to local was mostly just changing the baseURL . Dual interfaces are easy when you share the engine. The CLI and Web UI are thin layers over the same ChatEngine class. ⚡ Performance Notes On a typical laptop (no GPU): ingestion takes under 1 second for ~20 documents, retrieval is ~1ms, and the first LLM token arrives in 2-5 seconds. Foundry Local automatically selects the best model variant for your hardware (CUDA GPU, NPU, or CPU). Getting Started git clone https://github.com/leestott/interview-doctor-js.git cd interview-doctor-js npm install npm run ingest npm start # Web UI at http://127.0.0.1:3000 # or npm run cli # Interactive terminal The full source code is on GitHub. Star it, fork it, adapt it — and good luck with your interviews! Resources Foundry Local — Microsoft's on-device AI runtime Foundry Local SDK (npm) — JavaScript SDK Foundry Local GitHub — Source, samples, and documentation Local RAG Reference — Reference RAG implementation Interview Doctor (JavaScript) — This project's source codeVectorless Reasoning-Based RAG: A New Approach to Retrieval-Augmented Generation
Introduction Retrieval-Augmented Generation (RAG) has become a widely adopted architecture for building AI applications that combine Large Language Models (LLMs) with external knowledge sources. Traditional RAG pipelines rely heavily on vector embeddings and similarity search to retrieve relevant documents. While this works well for many scenarios, it introduces challenges such as: Requires chunking documents into small segments Important context can be split across chunks Embedding generation and vector databases add infrastructure complexity A new paradigm called Vectorless Reasoning-Based RAG is emerging to address these challenges. One framework enabling this approach is PageIndex, an open-source document indexing system that organizes documents into a hierarchical tree structure and allows Large Language Models (LLMs) to perform reasoning-based retrieval over that structure. Vectorless Reasoning-Based RAG Instead of vectors, this approach uses structured document navigation. User Query ->Document Tree Structure ->LLM Reasoning ->Relevant Nodes Retrieved ->LLM Generates Answer This mimics how humans read documents: Look at the table of contents Identify relevant sections Read the relevant content Answer the question Core features No Vector Database: It relies on document structure and LLM reasoning for retrieval. It does not depend on vector similarity search. No Chunking: Documents are not split into artificial chunks. Instead, they are organized using their natural structure, such as pages and sections. Human-like Retrieval: The system mimics how human experts read documents. It navigates through sections and extracts information from relevant parts. Better Explainability and Traceability: Retrieval is based on reasoning. The results can be traced back to specific pages and sections. This makes the process easier to interpret. It avoids opaque and approximate vector search, often called “vibe retrieval.” When to Use Vectorless RAG Vectorless RAG works best when: Data is structured or semi-structured Documents have clear metadata Knowledge sources are well organized Queries require reasoning rather than semantic similarity Examples: enterprise knowledge bases internal documentation systems compliance and policy search healthcare documentation financial reporting Implementing Vectorless RAG with Azure AI Foundry Step 1 : Install Pageindex using pip command, from pageindex import PageIndexClient import pageindex.utils as utils # Get your PageIndex API key from https://dash.pageindex.ai/api-keys PAGEINDEX_API_KEY = "YOUR_PAGEINDEX_API_KEY" pi_client = PageIndexClient(api_key=PAGEINDEX_API_KEY) Step 2 : Set up your LLM Example using Azure OpenAI: from openai import AsyncAzureOpenAI client = AsyncAzureOpenAI( api_key=AZURE_OPENAI_API_KEY, azure_endpoint=AZURE_OPENAI_ENDPOINT, api_version=AZURE_OPENAI_API_VERSION ) async def call_llm(prompt, temperature=0): response = await client.chat.completions.create( model=AZURE_DEPLOYMENT_NAME, messages=[{"role": "user", "content": prompt}], temperature=temperature ) return response.choices[0].message.content.strip() Step 3: Page Tree Generation import os, requests pdf_url = "https://arxiv.org/pdf/2501.12948.pdf" //give the pdf url for tree generation, here given one for example pdf_path = os.path.join("../data", pdf_url.split('/')[-1]) os.makedirs(os.path.dirname(pdf_path), exist_ok=True) response = requests.get(pdf_url) with open(pdf_path, "wb") as f: f.write(response.content) print(f"Downloaded {pdf_url}") doc_id = pi_client.submit_document(pdf_path)["doc_id"] print('Document Submitted:', doc_id) Step 4 : Print the generated pageindex tree structure if pi_client.is_retrieval_ready(doc_id): tree = pi_client.get_tree(doc_id, node_summary=True)['result'] print('Simplified Tree Structure of the Document:') utils.print_tree(tree) else: print("Processing document, please try again later...") Step 5 : Use LLM for tree search and identify nodes that might contain relevant context import json query = "What are the conclusions in this document?" tree_without_text = utils.remove_fields(tree.copy(), fields=['text']) search_prompt = f""" You are given a question and a tree structure of a document. Each node contains a node id, node title, and a corresponding summary. Your task is to find all nodes that are likely to contain the answer to the question. Question: {query} Document tree structure: {json.dumps(tree_without_text, indent=2)} Please reply in the following JSON format: {{ "thinking": "<Your thinking process on which nodes are relevant to the question>", "node_list": ["node_id_1", "node_id_2", ..., "node_id_n"] }} Directly return the final JSON structure. Do not output anything else. """ tree_search_result = await call_llm(search_prompt) Step 6 : Print retrieved nodes and reasoning process node_map = utils.create_node_mapping(tree) tree_search_result_json = json.loads(tree_search_result) print('Reasoning Process:') utils.print_wrapped(tree_search_result_json['thinking']) print('\nRetrieved Nodes:') for node_id in tree_search_result_json["node_list"]: node = node_map[node_id] print(f"Node ID: {node['node_id']}\t Page: {node['page_index']}\t Title: {node['title']}") Step 7: Answer generation node_list = json.loads(tree_search_result)["node_list"] relevant_content = "\n\n".join(node_map[node_id]["text"] for node_id in node_list) print('Retrieved Context:\n') utils.print_wrapped(relevant_content[:1000] + '...') answer_prompt = f""" Answer the question based on the context: Question: {query} Context: {relevant_content} Provide a clear, concise answer based only on the context provided. """ print('Generated Answer:\n') answer = await call_llm(answer_prompt) utils.print_wrapped(answer) When to Use Each Approach Both vector-based RAG and vectorless RAG have their strengths. Choosing the right approach depends on the nature of the documents and the type of retrieval required. When to Use Vector Database–Based RAG Vector-based retrieval works best when dealing with large collections of unrelated or loosely structured documents. In such cases, semantic similarity is often sufficient to identify relevant information quickly. Use vector RAG when: Searching across many independent documents Semantic similarity is sufficient to locate relevant content Real-time retrieval is required over very large datasets Common use cases include: Customer support knowledge bases Conversational chatbots Product and content search systems When to Use Vectorless RAG Vectorless approaches such as PageIndex are better suited for long, structured documents where understanding the logical organization of the content is important. Use vectorless RAG when: Documents contain clear hierarchical structure Logical reasoning across sections is required High retrieval accuracy is critical Typical examples include: Financial filings and regulatory reports Legal documents and contracts Technical manuals and documentation Academic and research papers In these scenarios, navigating the document structure allows the system to identify the exact section that logically contains the answer, rather than relying only on semantic similarity. Conclusion Vector databases significantly advanced RAG architectures by enabling scalable semantic search across large datasets. However, they are not the optimal solution for every type of document. Vectorless approaches such as PageIndex introduce a different philosophy: instead of retrieving text that is merely semantically similar, they retrieve text that is logically relevant by reasoning over the structure of the document. As RAG architectures continue to evolve, the future will likely combine the strengths of both approaches. Hybrid systems that integrate vector search for broad retrieval and reasoning-based navigation for precision may offer the best balance of scalability and accuracy for enterprise AI applications.Announcing the IQ Series: Foundry IQ
AI agents are rapidly becoming a new way to build applications. But for agents to be truly useful, they need access to the knowledge and context that helps them reason about the world they operate in. That’s where Foundry IQ comes in. Today we’re announcing the IQ Series: Foundry IQ, a new set of developer-focused episodes exploring how to build knowledge-centric AI systems using Foundry IQ. The series focuses on the core ideas behind how modern AI systems work with knowledge, how they retrieve information, reason across sources, synthesize answers, and orchestrate multi-step interactions. Instead of treating retrieval as a single step in a pipeline, Foundry IQ approaches knowledge as something that AI systems actively work with throughout the reasoning process. The IQ Series breaks down these concepts and shows how they come together when building real AI applications. You can explore the series and all the accompanying samples here: 👉 https://aka.ms/iq-series What is Foundry IQ? Foundry IQ helps AI systems work with knowledge in a more structured and intentional way. Rather than wiring retrieval logic directly into every application, developers can define knowledge bases that connect to documents, data sources, and other information systems. AI agents can then query these knowledge bases to gather the context they need to generate responses, make decisions, or complete tasks. This model allows knowledge to be organized, reused, and combined across applications, instead of being rebuilt for each new scenario. What's covered in the IQ Series? The Foundry IQ episodes in the IQ Series explore the key building blocks behind knowledge-driven AI systems from how knowledge enters the system to how agents ultimately query and use it. The series is released as three weekly episodes: Foundry IQ: Unlocking Knowledge for Your Agents — March 18, 2026: Introduces Foundry IQ and the core ideas behind it. The episode explains how AI agents work with knowledge and walks through the main components of the Foundry IQ that support knowledge-driven applications. Foundry IQ: Building the Data Pipeline with Knowledge Sources — March 25, 2026: Focuses on Knowledge Sources and how different types of content flow into Foundry IQ. It explores how systems such as SharePoint, Fabric, OneLake, Azure Blob Storage, Azure AI Search, and the web contribute information that AI systems can later retrieve and use. Foundry IQ: Querying the Multi-Source AI Knowledge Bases — April 1, 2026: Dives into the Knowledge Bases and how multiple knowledge sources can be organized behind a single endpoint. The episode demonstrates how AI systems query across these sources and synthesize information to answer complex questions. Each episode includes a short executive introduction, a tech talk exploring the topic in depth, and a visual recap with doodle summaries of the key ideas. Alongside the episodes, the GitHub repository provides cookbooks with sample code, summary of the episodes, and additinal learning resources, so developers can explore the concepts and apply them in their own projects. Explore the Repo All episodes and supporting materials live in the IQ Series repository: 👉 https://aka.ms/iq-series Inside the repository you’ll find: The Foundry IQ episode links Cookbooks for each episode Links to documentation and additional resources If you're building AI agents or exploring how AI systems can work with knowledge, the IQ Series is a great place to start. Watch the episodes and explore the cookbooks! We’re excited to see what you build and welcome your feedback & ideas as the series evolves.
Building High-Performance Agentic Systems
Most enterprise chatbots fail in the same quiet way. They answer questions. They impress in demos. And then they stall in production. Knowledge goes stale. Answers cannot be audited. The system cannot act beyond generating text. When workflows require coordination, execution, or accountability, the chatbot stops being useful. Agentic systems exist because that model is insufficient. Instead of treating the LLM as the product, agentic architecture embeds it inside a bounded control loop: plan → act (tools) → observe → refine The model becomes one component in a runtime system with explicit state management, safety policies, identity enforcement, and operational telemetry. This shift is not speculative. A late-2025 MIT Sloan Management Review / BCG study reports that 35% of organizations have already adopted AI agents, with another 44% planning deployment. Microsoft is advancing open protocols for what it calls the “agentic web,” including Agent-to-Agent (A2A) interoperability and Model Context Protocol (MCP), with integration paths emerging across Copilot Studio and Azure AI Foundry. The real question is no longer whether agents are coming. It is whether enterprise architecture is ready for them. This article translates “agentic” into engineering reality: the runtime layers, latency and cost levers, orchestration patterns, and governance controls required for production deployment. The Core Capabilities of Agentic AI What makes an AI “agentic” is not a single feature—it’s the interaction of different capabilities. Together, they form the minimum set needed to move from “answering” to “operating”. Autonomy – Goal-Driven Task Completion Traditional bots are reactive: they wait for a prompt and produce output. Autonomy introduces a goal state and a control loop. The agent is given an objective (or a trigger) and it can decide the next step without being micromanaged. The critical engineering distinction is that autonomy must be bounded: in production, you implement it with explicit budgets and stop conditions—maximum tool calls, maximum retries, timeouts, and confidence thresholds. The typical execution shape is a loop: plan → act → observe → refine. A project-management agent, for example, doesn’t just answer “what’s the status?” It monitors signals (work items, commits, build health), detects a risk pattern (slippage, dependency blockage), and then either surfaces an alert or prepares a remediation action (re-plan milestones, notify owners). In high-stakes environments, autonomy is usually human-in-the-loop by design: the agent can draft changes, propose next actions, and only execute after approval. Over time, teams expand the autonomy envelope for low-risk actions while keeping approvals for irreversible or financially sensitive operations. Tool Integration – Taking Action and Staying Current A standalone LLM cannot fetch live enterprise state and cannot change it. Tool integration is how an agent becomes operational: it can query systems of record, call APIs, trigger workflows, and produce outputs that reflect the current world rather than the model’s pretraining snapshot. There are two classes of tools that matter in enterprise agents: Retrieval tools (grounding / RAG)When the agent needs facts, it retrieves them. This is the backbone of reducing hallucination: instead of guessing, the agent pulls authoritative content (SharePoint, Confluence, policy repositories, CRM records, Fabric datasets) and uses it as evidence. In practice, retrieval works best when it is engineered as a pipeline: query rewrite (optional) → hybrid search (keyword + vector) → filtering (metadata/ACL) → reranking → compact context injection. The point is not “stuff the prompt with documents,” but “inject only the minimum evidence required to answer accurately.” Action tools (function calling / connectors) These are the hands of the agent: update a CRM record, create a ticket, send an email, schedule a meeting, generate a report, run a pipeline. Tool integration shifts value from “advice” to “execution,” but also introduces risk—so action tools need guardrails: least-privilege permissions, input validation, idempotency keys, and post-condition checks (confirm the update actually happened). In Microsoft ecosystems, this tool plane often maps to Graph actions + business connectors (via Logic Apps/Power Automate) + custom APIs, with Copilot Studio (low code) or Foundry-style runtimes (pro code) orchestrating the calls. Memory (Context & Learning) – Context Awareness and Adaptation “Memory” is not just a long prompt. In agentic systems, memory is an explicit state strategy: Working memory: what the agent has learned during the current run (intermediate tool results, constraints, partial plans). Session memory: what should persist across turns (user preferences, ongoing tasks, summarized history). Long-term memory: enterprise knowledge the agent can retrieve (indexed documents, structured facts, embeddings + metadata). Short-term memory enables multi-step workflows without repeating questions. An HR onboarding agent can carry a new hire’s details from intake through provisioning without re-asking, because the workflow state is persisted and referenced. Long-term “learning” is typically implemented through feedback loops rather than real-time model weight updates: capturing corrections, storing validated outcomes, and periodically improving prompts, routing logic, retrieval configuration, or (where appropriate) fine-tuning. The key design rule is that memory must be policy-aware: retention rules, PII handling, and permission trimming apply to stored state as much as they apply to retrieved documents. Orchestration – Coordinating Multi-Agent Teams Complex enterprise work is rarely single-skill. Orchestration is how agentic systems scale capability without turning one agent into an unmaintainable monolith. The pattern is “manager + specialists”: an orchestrator decomposes the goal into subtasks, routes each to the best tool or sub-agent, and then composes a final response. This can be done sequentially or in parallel. Employee onboarding is a classic: HR intake, IT account creation, equipment provisioning, and training scheduling can run in parallel where dependencies allow. The engineering challenge is making orchestration reliable: defining strict input/output contracts between agents (often structured JSON), handling failures (timeouts, partial completion), and ensuring only one component has authority to send the final user-facing message to avoid conflicting outputs. In Microsoft terms, orchestration can be implemented as agentic flows in Copilot Studio, connected-agent patterns in Foundry, or explicit orchestrators in code using structured tool schemas and shared state. Strategic Impact – How Agentic AI Changes Knowledge Work Agentic AI is no longer an experimental overlay to enterprise systems. It is becoming an embedded operational layer inside core workflows. Unlike earlier chatbot deployments that answered isolated questions, modern enterprise agents execute end-to-end processes, interact with structured systems, maintain context, and operate within governed boundaries. The shift is not about conversational intelligence alone; it is about workflow execution at scale. The transformation becomes clearer when examining real implementations across industries. In legal services, agentic systems have moved beyond document summarization into operational case automation. Assembly Software’s NeosAI, built on Azure AI infrastructure, integrates directly into legal case management systems and automates document analysis, structured data extraction, and first-draft generation of legal correspondence. What makes this deployment impactful is not merely the generative drafting capability, but the integration architecture. NeosAI is not an isolated chatbot; it operates within the same document management systems, billing systems, and communication platforms lawyers already use. Firms report time savings of up to 25 hours per case, with document drafting cycles reduced from days to minutes for first-pass outputs. Importantly, the system runs within secure Azure environments with zero data retention policies, addressing one of the most sensitive concerns in legal AI adoption: client confidentiality. JPMorgan’s COiN platform represents another dimension of legal and financial automation. Instead of conversational assistance, COiN performs structured contract intelligence at production scale. It analyzes more than 12,000 commercial loan agreements annually, extracting over 150 clause attributes per document. Work that previously required approximately 360,000 human hours now executes in seconds. The architecture emphasizes structured NLP pipelines, taxonomy-based clause classification, and private cloud deployment for regulatory compliance. Rather than replacing legal professionals, the system flags unusual clauses for human review, maintaining oversight while dramatically accelerating analysis. Over time, COiN has also served as a knowledge retention mechanism, preserving institutional contract intelligence that would otherwise be lost with employee turnover. In financial services, the impact is similarly structural. Morgan Stanley’s internal AI Assistant allows wealth advisors to query over 100,000 proprietary research documents using natural language. Adoption has reached nearly universal usage across advisor teams, not because it replaces expertise, but because it compresses research time and surfaces insights instantly. Building on this foundation, the firm introduced an AI meeting debrief agent that transcribes client conversations using speech-to-text models and generates CRM notes and follow-up drafts through GPT-based reasoning. Advisors review outputs before finalization, preserving human judgment. The result is faster client engagement and measurable productivity improvements. What differentiates Morgan Stanley’s approach is not only deployment scale, but disciplined evaluation before release. The firm established rigorous benchmarking frameworks to test model outputs against expert standards for accuracy, compliance, and clarity. Only after meeting defined thresholds were systems expanded firmwide. This pattern—evaluation before scale—is becoming a defining trait of successful enterprise agent deployment. Human Resources provides a different perspective on agentic AI. Johnson Controls deployed an AI HR assistant inside Slack to manage policy questions, payroll inquiries, and onboarding support across a global workforce exceeding 100,000 employees. By embedding the agent in a channel employees already use, adoption barriers were reduced significantly. The result was a 30–40% reduction in live HR call volume, allowing HR teams to redirect focus toward strategic workforce initiatives. Similarly, Ciena integrated an AI assistant directly into Microsoft Teams, unifying HR and IT support through a single conversational interface. Employees no longer navigate separate portals; the agent orchestrates requests across backend systems such as Workday and ServiceNow. The technical lesson here is clear: integration breadth drives usability, and usability drives adoption. Engineering and IT operations reveal perhaps the most technically sophisticated application of agentic AI: multi-agent orchestration. In a proof-of-concept developed through collaboration between Microsoft and ServiceNow, an AI-driven incident response system coordinates multiple agents during high-priority outages. Microsoft 365 Copilot transcribes live war-room discussions and extracts action items, while ServiceNow’s Now Assist executes operational updates within IT service management systems. A Semantic Kernel–based manager agent maintains shared context and synchronizes activity across platforms. This eliminates the longstanding gap between real-time discussion and structured documentation, automatically generating incident reports while freeing engineers to focus on remediation rather than clerical tasks. The system demonstrates that orchestration is not conceptual—it is operational. Across these examples, the pattern is consistent. Agentic AI changes knowledge work by absorbing structured cognitive labor: document parsing, compliance classification, research synthesis, workflow routing, transcription, and task coordination. Humans remain essential for judgment, ethics, and accountability, but the operational layer increasingly runs through AI-mediated execution. The result is not incremental productivity improvement; it is structural acceleration of knowledge processes. Design and Governance Challenges – Managing the Risks As agentic AI shifts from answering questions to executing workflows, governance must mature accordingly. These systems retrieve enterprise data, invoke APIs, update records, and coordinate across platforms. That makes them operational actors inside your architecture—not just assistants. The primary shift is this: autonomy increases responsibility. Agents must be observable. Every retrieval, reasoning step, and tool invocation should be traceable. Without structured telemetry and audit trails, enterprises lose visibility into why an agent acted the way it did. Agents must also operate within scoped authority. Least-privilege access, role-based identity, and bounded credentials are essential. An HR agent should not access finance systems. A finance agent should not modify compliance data without policy constraints. Autonomy only works when it is deliberately constrained. Execution boundaries are equally critical. High-risk actions—financial approvals, legal submissions, production changes—should include embedded thresholds or human approval gates. Autonomy should be progressive, not absolute. Cost and performance must be governed just like cloud infrastructure. Agentic systems can trigger recursive calls and model loops. Without usage monitoring, rate limits, and model-tier routing, compute consumption can escalate unpredictably. Finally, agentic systems require continuous evaluation. Real-world testing, live monitoring, and drift detection ensure the system remains aligned with business rules and compliance requirements. These are not “set and forget” deployments. In short, agentic AI becomes sustainable only when autonomy is paired with observability, scoped authority, embedded guardrails, cost control, and structured oversight. Conclusion – Towards the Agentic Enterprise The organizations achieving meaningful returns from agentic AI share a common pattern. They do not treat AI agents as experimental tools. They design them as production systems with defined roles, scoped authority, measurable KPIs, embedded observability, and formal governance layers. When autonomy is paired with integration, memory, orchestration, and governance discipline, agentic AI becomes more than automation—it becomes an operational architecture. Enterprises that master this architecture are not merely reducing costs; they are redefining how knowledge work is executed. In this emerging model, human professionals focus on strategic judgment and innovation, while AI agents manage structured cognitive execution at scale. The competitive advantage will not belong to those who deploy the most AI, but to those who deploy it with architectural rigor and governance maturity. Before we rush to deploy more agents, a few questions are worth asking: If an AI agent executes a workflow in your enterprise today, can you trace every reasoning step and tool invocation behind that decision? Does your architecture treat AI as a conversational layer - or as an operational actor with scoped identity, cost controls, and policy enforcement? Where should autonomy stop in your organization - and who defines that boundary? Agentic AI is not just a capability shift. It is an architectural decision. Curious to hear how others are designing their control planes and orchestration layers. References MIT Sloan – “Agentic AI, Explained” by Beth Stackpole: A foundational overview of agentic AI, its distinction from traditional generative AI, and its implications for enterprise workflows, governance, and strategy. Microsoft TechCommunity – “Introducing Multi-Agent Orchestration in Foundry Agent Service”: Details Microsoft’s multi-agent orchestration capabilities, including Connected Agents, Multi-Agent Workflows, and integration with A2A and MCP protocols. Microsoft Learn – “Extend the Capabilities of Your Agent – Copilot Studio”: Explains how to build and extend custom agents in Microsoft Copilot Studio using tools, connectors, and enterprise data sources. Assembly Software’s NeosAI case – Microsoft Customer Stories JPMorgan COiN platform – GreenData Case Study HR support AI (Johnson Controls, Ciena, Databricks) – Moveworks case studies ServiceNow & Semantic Kernel multi-agent P1 Incident – Microsoft Semantic Kernel BlogStaying in the flow: SleekFlow and Azure turn customer conversations into conversions
A customer adds three items to their cart but never checks out. Another asks about shipping, gets stuck waiting eight minutes, only to drop the call. A lead responds to an offer but is never followed up with in time. Each of these moments represents lost revenue, and they happen to businesses every day. SleekFlow was founded in 2019 to help companies turn those almost-lost-customer moments into connection, retention, and growth. Today we serve more than 2,000 mid-market and enterprise organizations across industries including retail and e-commerce, financial services, healthcare, travel and hospitality, telecommunications, real estate, and professional services. In total, those customers rely on SleekFlow to orchestrate more than 600,000 daily customer interactions across WhatsApp, Instagram, web chat, email, and more. Our name reflects what makes us different. Sleek is about unified, polished experiences—consolidating conversations into one intelligent, enterprise-ready platform. Flow is about orchestration—AI and human agents working together to move each conversation forward, from first inquiry to purchase to renewal. The drive for enterprise-ready agentic AI Enterprises today expect always-on, intelligent conversations—but delivering that at scale proved daunting. When we set out to build AgentFlow, our agentic AI platform, we quickly ran into familiar roadblocks: downtime that disrupted peak-hour interactions, vector search delays that hurt accuracy, and costs that ballooned under multi-tenant workloads. Development slowed from limited compatibility with other technologies, while customer onboarding stalled without clear compliance assurances. To move past these barriers, we needed a foundation that could deliver the performance, trust, and global scale enterprises demand. The platform behind the flow: How Azure powers AgentFlow We chose Azure because building AgentFlow required more than raw compute power. Chatbots built on a single-agent model often stall out. They struggle to retrieve the right context, they miss critical handoffs, and they return answers too slowly to keep a customer engaged. To fix that, we needed an ecosystem capable of supporting a team of specialized AI agents working together at enterprise scale. Azure Cosmos DB provides the backbone for memory and context, managing short-term interactions, long-term histories, and vector embeddings in containers that respond in 15–20 milliseconds. Powered by Azure AI Foundry, our agents use Azure OpenAI models within Azure AI Foundry to understand and generate responses natively in multiple languages. Whether in English, Chinese, or Portuguese, the responses feel natural and aligned with the brand. Semantic Kernel acts as the conductor, orchestrating multiple agents, each of which retrieves the necessary knowledge and context, including chat histories, transactional data, and vector embeddings, directly from Azure Cosmos DB. For example, one agent could be retrieving pricing data, another summarizing it, and a third preparing it for a human handoff. The result is not just responsiveness but accuracy. A telecom provider can resolve a billing question while surfacing an upsell opportunity in the same dialogue. A financial advisor can walk into a call with a complete dossier prepared in seconds rather than hours. A retailer can save a purchase by offering an in-stock substitute before the shopper abandons the cart. Each of these conversations is different, yet the foundation is consistent on AgentFlow. Fast, fluent, and focused: Azure keeps conversations moving Speed is the heartbeat of a good conversation. A delayed answer feels like a dropped call, and an irrelevant one breaks trust. For AgentFlow to keep customers engaged, every operation behind the scenes has to happen in milliseconds. A single interaction can involve dozens of steps. One agent pulls product information from embeddings, another checks it against structured policy data, and a third generates a concise, brand-aligned response. If any of these steps lag, the dialogue falters. On Azure, they don’t. Azure Cosmos DB manages conversational memory and agent state across dedicated containers for short-term exchanges, long-term history, and vector search. Sharded DiskANN indexing powers semantic lookups that resolve in the 15–20 millisecond range—fast enough that the customer never feels a pause. Microsoft Phi’s model Phi-4 as well as Azure OpenAI in Foundry Models like o3-mini and o4-mini, provide the reasoning, and Azure Container Apps scale elastically, so performance holds steady during event-driven bursts, such as campaign broadcasts that can push the platform from a few to thousands of conversations per minute, and during daily peak-hour surges. To support that level of responsiveness, we run Azure Container Apps on the Pay-As-You-Go consumption plan, using KEDA-based autoscaling to expand from five idle containers to more than 160 within seconds. Meanwhile, Microsoft Orleans coordinates lightweight in-memory clustering to keep conversations sleek and flowing. The results are tangible. Retrieval-augmented generation recall improved from 50 to 70 percent. Execution speed is about 50 percent faster. For SleekFlow’s customers, that means carts are recovered before they’re abandoned, leads are qualified in real time, and support inquiries move forward instead of stalling out. With Azure handling the complexity under the hood, conversations flow naturally on the surface—and that’s what keeps customers engaged. Secure enough for enterprises, human enough for customers AgentFlow was built with security-by-design as a first principle, giving businesses confidence that every interaction is private, compliant, and reliable. On Azure, every AI agent operates inside guardrails enterprises can depend on. Azure Cosmos DB enforces strict per-tenant isolation through logical partitioning, encryption, and role-based access control, ensuring chat histories, knowledge bases, and embeddings remain auditable and contained. Models deployed through Azure AI Foundry, including Azure OpenAI and Microsoft Phi, process data entirely within SleekFlow’s Azure environment and guarantees it is never used to train public models, with activity logged for transparency. And Azure’s certifications—including ISO 27001, SOC 2, and GDPR—are backed by continuous monitoring and regional data residency options, proving compliance at a global scale. But trust is more than a checklist of certifications. AgentFlow brings human-like fluency and empathy to every interaction, powered by Azure OpenAI running with high token-per-second throughput so responses feel natural in real time. Quality control isn’t left to chance. Human override workflows are orchestrated through Azure Container Apps and Azure App Service, ensuring AI agents can carry conversations confidently until they’re ready for human agents. Enterprises gain the confidence to let AI handle revenue-critical moments, knowing Azure provides the foundation and SleekFlow provides the human-centered design. Shaping the next era of conversational AI on Azure The benefits of Azure show up not only in customer conversations but also in the way our own teams work. Faster processing speeds and high token-per-second throughput reduce latency, so we spend less time debugging and more time building. Stable infrastructure minimizes downtime and troubleshooting, lowering operational costs. That same reliability and scalability have transformed the way we engineer AgentFlow. AgentFlow started as part of our monolithic system. Shipping new features used to take about a month of development and another week of heavy testing to make sure everything held together. After moving AgentFlow to a microservices architecture on Azure Container Apps, we can now deploy updates almost daily with no down time or customer impact. And this is all thanks to native support for rolling updates and blue-green deployments. This agility is what excites us most about what's ahead. With Azure as our foundation, SleekFlow is not simply keeping pace with the evolution of conversational AI—we are shaping what comes next. Every interaction we refine, every second we save, and every workflow we streamline brings us closer to our mission: keeping conversations sleek, flowing, and valuable for enterprises everywhere.
🎉 Announcing General Availability of AI & RAG Connectors in Logic Apps (Standard)
We’re excited to share that a comprehensive set of AI and Retrieval-Augmented Generation (RAG) capabilities is now Generally Available in Azure Logic Apps (Standard). This release brings native support for document processing, semantic retrieval, embeddings, and grounded reasoning directly into the Logic Apps workflow engine. 🔌 Available AI Connectors in Logic Apps Standard Logic Apps (Standard) had previously previewed four AI-focused connectors that open the door for a new generation of intelligent automation across the enterprise. Whether you're processing large volumes of documents, enriching operational data with intelligence, or enabling employees to interact with systems using natural language, these connectors provide the foundation for building solutions that are smarter, faster, and more adaptable to business needs. These are now in GA. They allow teams to move from routine workflow automation to AI-assisted decisioning, contextual responses, and multi-step orchestration that reflects real business intent. Below is the full set of built-in connectors and their actions as they appear in the designer. 1. Azure OpenAI Actions Get an embedding Get chat completions Get chat completions using Prompt Template Get completion Get multiple chat completions Get multiple embeddings What this unlocks Bring natural language reasoning and structured AI responses directly into workflows. Common scenarios include guided decisioning, user-facing assistants, classification and routing, or preparing embeddings for semantic search and RAG workflows. 2. Azure AI Search Actions Delete a document Delete multiple documents Get agentic retrieval output (Preview) Index a document Index multiple documents Merge document Search vectors Search vectors with natural language What this unlocks Add vector, hybrid semantic, and natural language search directly to workflow logic. Ideal for retrieving relevant content from enterprise data, powering search-driven workflows, and grounding AI responses with context from your own documents. 3. Azure AI Document Intelligence Action Analyze document What this unlocks Document Intelligence serves as the entry point for document-heavy scenarios. It extracts structured information from PDFs, images, and forms, allowing workflows to validate documents, trigger downstream processes, or feed high-quality data into search and embeddings pipelines. 4. AI Operations Actions Chunk text with metadata Parse document with metadata What this unlocks Transform unstructured files into enriched, structured content. Enables token-aware chunking, page-level metadata, and clean preparation of content for embeddings and semantic search at scale. 🤖 Advanced AI & Agentic Workflows with AgentLoop Logic Apps (Standard) also supports AgentLoop (also Generally Available), allowing AI models to use workflow actions as tools and iterate until the task is complete. Combined with chunking, embeddings, and natural language search, this opens the door to advanced agentic scenarios such as document intelligence agents, RAG-based assistants, and iterative evaluators. Conclusion With these capabilities now built into Logic Apps Standard, teams can bring AI directly into their integration workflows without additional infrastructure or complexity. Whether you’re streamlining document-heavy processes, enabling richer search experiences, or exploring more advanced agentic patterns, these capabilities provide a strong foundation to start building today.📢 Agent Loop Ignite Update - New Set of AI Features Arrive in Public Preview
Today at Ignite, we announced the General Availability of Agent Loop in Logic Apps Standard—bringing production-ready agentic automation to every customer. But GA is just the beginning. We’re also releasing a broad set of new and powerful AI-first capabilities in Public Preview that dramatically expand what developers can build: run agents in the Consumption SKU ,bring your own models through APIM AI Gateway, call any tool through MCP, deploy agents directly into Teams, secure RAG with document-level permissions, onboard with Okta, and build in a completely redesigned workflow designer. With these preview features layered on top of GA, customers can build AI applications that bring together secure tool calling, user identity, governance, observability, and integration with their existing systems—whether they’re running in Standard, Consumption, or the Microsoft 365 ecosystem. Here’s a closer look at the new capabilities now available in Public Preview. Public Preview of Agentic Workflows in Consumption SKU Agent Loop is now available in Azure Logic Apps Consumption, bringing autonomous and conversational AI agents to everyone through a fully serverless, pay-as-you-go experience. You can now turn any workflow into an intelligent workflow using the agent loop action—without provisioning infrastructure or managing AI models. This release provides instant onboarding, simple authentication, and a frictionless entry point for building agentic automation. Customers can also tap into Logic Apps’ ecosystem of 1,400+ connectors for tool calling and system integrations. This update makes AI-powered automation accessible for rapid prototyping while still offering a clear path to scale and production-ready deployments in Logic Apps Standard, including BYOM, VNET integration, and enterprise-grade controls. Preview limitations include limited regions, no VS Code local development, and no nested agents or MCP tools yet. Read more about this in our announcement blog! Bring your Own Model We’re excited to introduce Bring Your Own Model (BYOM) support in Agent Loop for Logic Apps Standard - making it possible to use any AI model in your agentic workflows from Foundry, and even on-prem or private cloud models. The key highlight of this feature is the deep integration with the Azure API Management (APIM) AI Gateway, which now serves as the control plane for how Agent Loop connects to models. Instead of wiring agents directly to individual endpoints, AI Gateway creates a single, governed interface that manages authentication, keys, rate limits, and quotas in one place. It provides built-in monitoring, logging, and observability, giving you full visibility into every request. It also ensures a consistent API shape for model interactions, so your workflows remain stable even as backends evolve. With AI Gateway in front, you can test, upgrade, and refine your model configuration without changing your Logic Apps, making model management safer, more predictable, and easier to operate at scale. Beyond AI Gateway, Agent Loop also supports: Direct external model integration when you want lightweight, point-to-point access to a third-party model API. Local/VNET model integration for on-prem, private cloud, or custom fine-tuned models that require strict data residency and private networking. Together, these capabilities let you treat the model as a pluggable component - start with the model you have today, bring in specialized or cost-optimized models as needed, and maintain enterprise-grade governance, security, and observability throughout. This makes Logic Apps one of the most flexible platforms for building model-agnostic, production-ready AI agent workflows. Ready to try this out? Go to http://aka.ms/agentloop/byom to learn more and get started. MCP support for Agent Loop in Logic Apps Standard Agent Loop in Azure Logic Apps Standard now supports the Model Context Protocol (MCP), enabling agents to discover and call external tools through an open, standardized interface. This brings powerful, flexible tool extensibility to both conversational and autonomous agents. Agent Loop offers three ways to bring MCP tools into your workflows: Bring Your Own MCP connector – Point to any external MCP server using its URL and credentials, instantly surfacing its published tools in your agent. Managed MCP connector – Access Azure-hosted MCP servers through the familiar managed connector experience, with shared connections and Azure-managed catalogs. Custom MCP connector – Build and publish your own OpenAPI-based MCP connector to expose private or tenant-scoped MCP servers. Idea for reusability of MCPs across organization. Managed and Custom MCP connectors support on-behalf-of (OBO) authentication, allowing agents to call MCP tools using the end user’s identity. This provides user-context-aware, permission-sensitive tool access across your intelligent workflows. Want to learn more – check out our announcement blog and how-to documents. Deploy Conversational Agents to Teams/M365 Workflows with conversational agents in Logic Apps can now be deployed directly into Microsoft Teams, so your agentic workflows show up where your users already live all day. Instead of going to a separate app or portal, employees can ask the agent questions, kick off approvals, check order or incident status, or look up internal policies right from a Teams chat or channel. The agent becomes just another teammate in the conversation—joining stand-ups, project chats, and support rooms as a first-class participant. Because the same Logic Apps agent can also be wired into other Microsoft 365 experiences that speak to Bots and web endpoints, this opens the door to a consistent and personalized “organization copilot” that follows users across the M365 ecosystem: Teams for chat, meetings, and channels today, and additional surfaces over time. Azure Bot Service and your proxy handle identity, tokens, and routing, while Logic Apps takes care of reasoning, tools, and back-end systems. The result is an agent that feels native to Teams and Microsoft 365—secure, governed, and always just one @mention away. Ready to bring your agentic workflows into Teams? Here’s how to get started. Secure Knowledge Retrieval for AI Agents in Logic Apps We’ve added native document-level authorization to Agent Loop by integrating Azure AI Search ACLs. This ensures AI agents only retrieve information the requesting user is permitted to access—making RAG workflows secure, compliant, and permission-aware by default. Documents are indexed with user or group permissions, and Agent Loop automatically applies those permissions during search using the caller’s principal ID or group memberships. Only authorized documents reach the LLM, preventing accidental exposure of sensitive data. This simplifies development, removes custom security code, and allows a single agent to safely serve users with different access levels—whether for HR, IT, or internal knowledge assistants. Here is our blogpost to learn more about this feature. Okta Agent Loop now supports Okta as an identity provider for conversational agents, alongside Microsoft Entra ID. This makes it easy for organizations using Okta for workforce identity to pass authenticated user context—including user attributes, group membership, and permissions—directly into the agent at runtime. Agents can now make user-aware decisions, enforce access rules, personalize responses, and execute tools with proper user context. This update helps enterprises adopt Agent Loop without changing their existing identity architecture and enables secure, policy-aligned AI interactions across both Okta and Entra environments. Setting up Okta as the identity provider requires a few steps and they are all explained in details here at Logic Apps Labs. Designer makeover! We’ve introduced a major redesign of the Azure Logic Apps designer, now in Public Preview for Standard workflows. This release marks the beginning of a broader modernization effort to make building, testing, and operating workflows faster, cleaner, and more intuitive. The new designer focuses on reducing friction and streamlining the development loop. You now land directly in the designer when creating a workflow, with plans to remove early decisions like stateful/stateless or agentic setup. The interface has been simplified into a single unified view, bringing together the visual canvas, code view, settings, and run history so you no longer switch between blades. A major addition is Draft Mode with auto-save, which preserves your work every few seconds without impacting production. Drafts can be tested safely and only go live when you choose to publish—without restarting the app during editing. Search has also been completely rebuilt for speed and accuracy, powered by backend indexing instead of loading thousands of connectors upfront. The designer now supports sticky notes and markdown, making it easy to document workflows directly on the canvas. Monitoring is integrated into the same page, letting you switch between runs instantly and compare draft and published results. A new hierarchical timeline view improves debugging by showing every action executed in order. This release is just the start—many more improvements and a unified designer experience across Logic Apps are on the way as we continue to iterate based on your feedback. Learn more about the designer updates in our announcement blog ! What's Next We’d love your feedback. Which capabilities should we prioritize, and what would create the biggest impact for your organization?🎉Announcing General Availability of Agent Loop in Azure Logic Apps
Transforming Business Automation with Intelligent, Collaborative Multi-Agentic workflows! Agent Loop is now Generally Available in Azure Logic Apps Standard, turning Logic Apps platform into a complete multi-agentic automation system. Build AI agents that work alongside workflows and humans, secured with enterprise-grade identity and access controls, deployed using your existing CI/CD pipelines. Thousands of customers have already built tens of thousands of agents—now you can take them to production with confidence. Get Started | Workshop | Demo Videos | Ignite 2025 Session | After an incredible journey since we introduced Agent Loop at Build earlier this year, we're thrilled to announce that Agent Loop is now generally available in Azure Logic Apps. This milestone represents more than just a feature release—it's the culmination of learnings from thousands of customers who have been pushing the boundaries of what's possible with agentic workflows. Agent Loop transforms Azure Logic Apps into a complete multi-agentic business process automation platform, where AI agents, automated workflows, and human expertise collaborate seamlessly to solve complex business challenges. With GA, we're delivering enterprise-grade capabilities that organizations need to confidently deploy intelligent automation at scale. The Journey to GA: Proven by Customers, Built for Production Since our preview launch at Build, the response has been extraordinary. Thousands of customers—from innovative startups to Fortune 500 enterprises—have embraced Agent Loop, building thousands of active agents that have collectively processed billions of tokens every month for the past six months. The growth of agents, executions, and token usage has accelerated significantly, doubling month over month. Since the launch of Conversational Agents in September, they already account for nearly 30% of all agentic workflows. Across the platform, agentic workflows now consume billions of tokens, with overall token usage increasing at nearly 3× month over month. Cyderes: 5X Faster Security Investigation Cycles Cyderes leveraged Agent Loop to automate triage and handling of security alerts, leading to faster investigation cycles and significant cost savings. "We were drowning in data—processing over 10,000 alerts daily while analysts spent more time chasing noise than connecting narratives. Agent Loop changed everything. By empowering our team to design and deploy their own AI agents through low-code orchestration, we've achieved 5X faster investigation cycles and significant cost savings, all while keeping pace with increasingly sophisticated cyber threats that now leverage AI to operate 25X faster than traditional attacks." – Eric Summers, Engineering Manager - AI & SOAR Vertex Pharmaceuticals: Hours Condensed to Minutes Vertex Pharmaceuticals unlocked knowledge trapped across dozens of systems via a team of agents. VAIDA, built with Logic Apps and Agent Loop, orchestrates multiple AI agents and helps employees find information faster, while maintaining compliance and supporting multiple languages. "We had knowledge trapped across dozens of systems—ServiceNow, documentation, training materials—and teams were spending valuable time hunting for answers. Logic Apps Agent Loop changed that. VAIDA now orchestrates multiple AI agents to summarize, search, and analyze this knowledge, then routes approvals right in Teams and Outlook. We've condensed hours into minutes while maintaining compliance and delivering content in multiple languages." – Pratik Shinde, Director, Digital Infrastructure & GenAI Platforms Where Customers Are Deploying Agent Loop Customers across industries are using Agent Loop to build AI applications that power both everyday tasks and mission-critical business processes across Healthcare, Retail, Energy, Financial Services, and beyond. These applications drive impact across a wide range of scenarios: Developer Productivity: Write code, generate unit tests, create workflows, map data between systems, automate source control, deployment and release pipelines IT Operations: Incident management, ticket and issue handling, policy review and enforcement, triage, resource management, cost optimization, issue remediation Business Process Automation: Empower sales specialists, retail assistants, order processing/approval flows, and healthcare assistants for intake and scheduling Customer & Stakeholder Support: Project planning and estimation, content generation, automated communication, and streamlined customer service workflows Proven Internally at Microsoft Agent Loop is also powering Microsoft and Logic Apps team's own operations, demonstrating its versatility and real-world impact: IcM Automation Team: Transforming Microsoft's internal incident automation platform into an agent studio that leverages Logic Apps' Agent Loop, enabling teams across Microsoft to build agentic live site incident automations Logic Apps Team Use Cases: Release & Deployment Agent: Streamlines deployment and release management for the Logic Apps platform Incident Management Agent: An extension of our SRE Agent, leveraging Agent Loop to accelerate incident response and remediation Analyst Agent: Assists teams in exploring product usage and health data, generating insights directly from analytics What's Generally Available Today Core Agent Loop Capabilities (GA) Agent Loop in Logic Apps Standard SKU - Support for both Autonomous and Conversational workflows Autonomous workflows run agents automatically based on triggers and conditions Conversational workflows use A2A to enable interactive chat experiences with agents On-Behalf-Of Authentication - Per-user authentication for 1st-party and 3rd-party connectors Agent Hand-Off - Enable seamless collaboration in multi-agent workflows Python Code Interpreter - Execute Python code dynamically for data analysis and computation Nested Agent Action - Use agents as tools within other agents for sophisticated orchestration User ACLs Support - Fine-grained document access control for knowledge Exciting New Agent Loop Features in Public Preview We've also released several groundbreaking features in Public Preview: New Designer Experience - Redesigned interface optimized for building agentic workflows Agent Loop in Consumption SKU - Deploy agents in the serverless Consumption tier MCP Support - Integrate Model Context Protocol servers as tools, enabling agents to access standardized tool ecosystems AI Gateway Integration - Use Azure AI Gateway as a model source for unified governance and monitoring Teams/M365 Deployment - Deploy conversational agents directly in Microsoft Teams and Microsoft 365 Okta Identity Provider - Use Okta as the identity provider for conversational agents Here’s our Announcement Blog for these new capabilities Built on a Platform You Already Trust Azure Logic Apps is already a proven iPaaS platform with thousands of customers using it for automation – ranging from startups to 100% of Fortune 500 companies. Agent Loop doesn't create a separate "agentic workflow automation platform" you have to learn and operate. Instead, it makes Azure Logic Apps itself your agentic platform: Workflows orchestrate triggers, approvals, retries, and branching Agent Loop, powered by LLMs, handle reasoning, planning, and tool selection Humans stay in control through approvals, exceptions, and guided hand-offs Agent Loop runs inside your Logic Apps Standard environment, so you get the same benefits you already know: enterprise SLAs, VNET integration, data residency controls, hybrid hosting options, and integration with your existing deployment pipelines and governance model. Enterprise Ready - Secure, User-Aware Agents by Design Bringing agents into the enterprise only works if security and compliance are first-class. With Agent Loop in Azure Logic Apps, security is built into every layer of the stack. Per-User Actions with On-Behalf-Of (OBO) and Delegated Permissions Many agent scenarios require tools to act in the context of the signed-in user. Agent Loop supports the OAuth 2.0 On-Behalf-Of (OBO) flow so that supported connector actions can run with delegated, per-user connections rather than a broad app-only identity. That means when an agent sends mail, reads SharePoint, or updates a service desk system, it does so as the user (where supported), respecting that user's licenses, permissions, and data boundaries. This is critical for scenarios like IT operations, HR requests, and finance approvals where "who did what" must be auditable. Document-Level Security with Microsoft Entra-Based Access Control Agents should only see the content a user is entitled to see. With Azure AI Search's Entra-based document-level security, your retrieval-augmented workflows can enforce ACLs and RBAC directly in the index so that queries are automatically trimmed to documents the user has access to. Secured Chat Entry Point with Easy Auth and Entra ID The built-in chat client and your custom clients can be protected using App Service Authentication (Easy Auth) and Microsoft Entra ID, so only authorized users and apps can invoke your conversational endpoints. Together, OBO, document-level security, and Easy Auth give you end-to-end identity and access control—from the chat surface, through the agent, down to your data and systems. An Open Toolbox: Connectors, Workflows, MCP Servers, and External Agents Agent Loop inherits the full power of the Logic Apps ecosystem and more - 1,400+ connectors for SaaS, on-premises, and custom APIs Workflows and agents as tools - compose sophisticated multi-step capabilities MCP server support - integrate with the Model Context Protocol for standardized tool access (Preview) A2A protocol support - enable agent-to-agent communication across platforms Multi-model flexibility - use Azure OpenAI, Azure AI Foundry hosted models, or bring your own model on any endpoint via AI gateway You're not locked into a single vendor or model provider. Agent Loop gives you an open, extensible framework that works with your existing investments and lets you choose the right tools for each job. Run Agents Wherever You Run Logic Apps Agent Loop is native to Logic Apps Standard, so your agentic workflows run consistently across cloud, on-premises, or hybrid environments. They inherit the same deployment, scaling, and networking capabilities as your workflows, bringing adaptive, AI-driven automation to wherever your systems and data live. Getting Started with Agent Loop We're in very exciting times, and we can't wait to see our customers go to production and realize the benefits of these capabilities for their business outcomes and success. Here are some useful links to get started on your AI journey with Logic Apps! Logic Apps Labs - https://aka.ms/LALabs Workshop - https://aka.ms/la-agent-in-a-day Demos - https://aka.ms/agentloopdemos
