Expanded Models Available in Microsoft Foundry Agent Service
Announcement Summary Foundry Agent Service now supports an expanded ecosystem of frontier and specialist models. Access models from Anthropic, DeepSeek AI, Meta, Microsoft, xAI, and more. Avoid model lock-in and choose the best model for each scenario. Build complex, multimodal, multi-agent workflows at enterprise scale. From document intelligence to operational automation, Microsoft Foundry makes AI agents ready for mission-critical workloads.Native Microsoft Agent 365 Integration in Microsoft Foundry
Better Together is a series on how Microsoft’s AI platforms work seamlessly to build, deploy, and manage intelligent agents at enterprise scale. As organizations embrace AI across every workflow, Microsoft Foundry, Microsoft 365, Microsoft Agent 365, and Microsoft Copilot Studio are coming together to deliver a unified approach—from development to deployment to day-to-day operations. This three-part series explores how these technologies connect to help enterprises build AI agents that are secure, governed, and deeply integrated with Microsoft’s product ecosystem. This blog focuses on Part 2: Microsoft Foundry + A365Microsoft Agent 365 native Integration, showing how organizations can build, deploy, and customize Microsoft Agent 365 agents directly from Foundry. What Is Microsoft Agent 365? Microsoft Agent 365 is the control plane for enterprise AI agents, allowing IT to register, secure, and scale agents across Microsoft 365 and third-party environments. AI agents act more like people than code—they bring skills, learn from context, and leverage enterprise data to complete tasks. Like with people in the enterprise, they need to be protected from digital threats, governed with the right IT controls, and managed following enterprise policies. Our philosophy is simple: treat agents like users. Extend your existing identity, security, compliance, and productivity infrastructure to agents using familiar tools adapted for their unique needs. Each agent receives its own identity, policies, and access controls, ensuring it operates effectively while staying compliant. With Agent 365, organizations can: Manage AI agents at scale with unified identity and lifecycle controls Enforce least-privilege access and compliance with Defender, Entra, and Purview Boost productivity through native integration with Microsoft 365 apps and Work IQ Monitor activity and apply policies from a single, secure registry Learn more about Microsoft Agent 365 Foundry: The Ideal Place for Developers to Build AI Agents Microsoft Foundry is the ideal platform for building, testing, and deploying Agent 365 agents. It provides a unified environment where developers can create enterprise-ready AI agents that are secure, governed, and fully integrated with Microsoft 365. At Ignite, Foundry introduces support for Agent 365 hosted (containerized) agents, giving developers a consistent, scalable runtime managed entirely within the Microsoft cloud. This initial release focuses on hosted agents to provide a fully managed and secure environment from development to deployment. With Foundry, developers can: Author agents quickly using low-code or pro-code workflows Test and iterate in a secure, hosted environment Integrate frontier AI models from Microsoft, OpenAI, Meta, DeepSeek, and xAI Package and deploy agents with Microsoft identity, security, and governance built in Through its native integration with Microsoft Agent 365, Foundry also provides: Foundry-hosted runtime for seamless agent execution Azure Bot Service and Microsoft 365 app integration (Teams, Outlook, M365 Copilot) MCP-connected tools from Microsoft Agent 365 Simplified preparation flow for publishing to M365 Copilot, Teams and BizChat Apps Together, Foundry and Microsoft Agent 365 let organizations build, host, and manage AI agents natively, making them enterprise-ready from day one. What Can Employees Do with Agent 365? With Agent 365, employees can: Automate email triage and meeting preparation Summarize and generate content Locate organizational knowledge instantly Orchestrate cross-system workflows and approvals Advanced teams can also: Integrate internal knowledge bases Create business-specific workflows Extend actions using Foundry APIs and connectors Why It Matters This integration makes Agent 365 agents enterprise-ready out of the box—combining the authoring power of Microsoft Foundry with the security and manageability of the Microsoft 365 ecosystem. IT retains control over policy, compliance, and lifecycle management, while business users gain intelligent agents that work across the tools they already use. Get Started Early access to Microsoft Agent 365 is available through the Frontier preview program, offering hands-on experience with Microsoft’s latest AI innovations. 🔗 [Quickstart — Publish an Agent to A365 GitHub Sample]Publishing Agents from Microsoft Foundry to Microsoft 365 Copilot & Teams
Better Together is a series on how Microsoft’s AI platforms work seamlessly to build, deploy, and manage intelligent agents at enterprise scale. As organizations embrace AI across every workflow, Microsoft Foundry, Microsoft 365, Agent 365, and Microsoft Copilot Studio are coming together to deliver a unified approach—from development to deployment to day-to-day operations. This three-part series explores how these technologies connect to help enterprises build AI agents that are secure, governed, and deeply integrated with Microsoft’s product ecosystem. Series Overview Part 1: Publishing from Foundry to Microsoft 365 Copilot and Microsoft Teams Part 2: Foundry + Agent 365 — Native Integration for Enterprise AI Part 3: Microsoft Copilot Studio Integration with Foundry Agents This blog focuses on Part 1: Publishing from Foundry to Microsoft 365 Copilot—how developers can now publish agents built in Foundry directly to Microsoft 365 Copilot and Teams in just a few clicks. Build once. Publish everywhere. Developers can now take an AI agent built in Microsoft Foundry and publish it directly to Microsoft 365 Copilot and Microsoft Teams in just a few clicks. The new streamlined publishing flow eliminates manual setup across Entra ID, Azure Bot Service, and manifest files, turning hours of configuration into a seamless, guided flow in the Foundry Playground. Simplifying Agent Publishing for Microsoft 365 Copilot & Microsoft Teams Previously, deploying a Foundry AI agent into Microsoft 365 Copilot and Microsoft Teams required multiple steps: app registration, bot provisioning, manifest editing, and admin approval. With the new Foundry → M365 integration, the process is straightforward and intuitive. Key capabilities No-code publishing — Prepare, package, and publish agents directly from Foundry Playground. Unified build — A single agent package powers multiple Microsoft 365 channels, including Teams Chat, Microsoft 365 Copilot Chat, and BizChat. Agent-type agnostic — Works seamlessly whether you have a prompt agent, hosted agent, or workflow agent. Built-in Governance — Every agent published to your organization is automatically routed through Microsoft 365 Admin Center (MAC) for review, approval, and monitoring. Downloadable package — Developers can download a .zip for local testing or submission to the Microsoft Marketplace. For pro-code developers, the experience is also simplified. A C# code-first sample in the Agent Toolkit for Visual Studio is searchable, featured, and ready to use. Why It Matters This integration isn’t just about convenience; it’s about scale, control, and trust. Faster time to value — Deliver intelligent agents where people already work, without infrastructure overhead. Enterprise control — Admins retain full oversight via Microsoft 365 Admin Center, with built-in approval, review and governance flows. Developer flexibility — Both low-code creators and pro-code developers benefit from the unified publishing experience. Better Together — This capability lays the groundwork for Agent 365 publishing and deeper M365 integrations. Real-world scenarios YoungWilliams built Priya, an AI agent that helps handle government service inquiries faster and more efficiently. Using the one-click publishing flow, Priya was quickly deployed to Microsoft Teams and M365 Copilot without manual setup. This allowed Young Williams’ customers to provide faster, more accurate responses while keeping governance and compliance intact. “Integrating Microsoft Foundry with Microsoft 365 Copilot fundamentally changed how we deliver AI solutions to our government partners,” said John Tidwell, CTO of YoungWilliams. “With Foundry’s one-click publishing to Teams and Copilot, we can take an idea from prototype to production in days instead of weeks—while maintaining the enterprise-grade security and governance our clients expect. It’s a game changer for how public services can adopt AI responsibly and at scale.” Availability Publishing from Foundry to M365 is in Public Preview within the Foundry Playground. Developers can explore the preview in Microsoft Foundry and test the Teams / M365 publishing flow today. SDK and CLI extensions for code-first publishing are generally available. What’s Next in the Better Together Series This blog is part of the broader Better Together series connecting Microsoft Foundry, Microsoft 365, Agent 365, and Microsoft Copilot Studio. Continue the journey: Foundry + Agent 365 — Native Integration for Enterprise AI (Link) Start building today [Quickstart — Publish an Agent to Microsoft 365 ] Try it now in the new Foundry PlaygroundFoundry Agent Service at Ignite 2025: Simple to Build. Powerful to Deploy. Trusted to Operate.
The upgraded Foundry Agent Service delivers a unified, simplified platform with managed hosting, built-in memory, tool catalogs, and seamless integration with Microsoft Agent Framework. Developers can now deploy agents faster and more securely, leveraging one-click publishing to Microsoft 365 and advanced governance features for streamlined enterprise AI operations.Introducing Microsoft Agent Factory
Microsoft Agent Factory is a new program designed for organizations that want to move from experimentation to execution faster. With a single plan, organizations can build agents with Work IQ, Fabric IQ, and Foundry IQ using Microsoft Foundry and Copilot Studio. They can also deploy their agents anywhere, including Microsoft 365 Copilot, with no upfront licensing and provisioning required. Eligible organizations can also tap into hands-on engagement from top AI Forward Deployed Engineers (FDEs) and access tailored role-based training to boost AI fluency across teams.Hybrid AI Using Foundry Local, Microsoft Foundry and the Agent Framework - Part 1
Hybrid AI is quickly becoming one of the most practical architectures for real-world applications—especially when privacy, compliance, or sensitive data handling matter. Today, it’s increasingly common for users to have capable GPUs in their laptops or desktops, and the ecosystem of small, efficient open-source language models has grown dramatically. That makes local inference not only possible, but easy. In this guide, we explore how a locally run agent built with the Agent Framework can combine the strengths of cloud models in Azure AI Foundry with a local LLM running on your own GPU through Foundry Local. This pattern allows you to use powerful cloud reasoning without ever sending raw sensitive data—like medical labs, legal documents, or financial statements—off the device. Part 1 focuses on the foundations of this architecture, using a simple illustrative example to show how local and cloud inference can work together seamlessly under a single agent. Disclaimer: The diagnostic results, symptom checker, and any medical guidance provided in this article are for illustrative and informational purposes only. They are not intended to provide medical advice, diagnosis, or treatment. Demonstrating the concept Problem Statement We’ve all done it: something feels off, we get a strange symptom, or a lab report pops into our inbox—and before thinking twice, we copy-paste way too much personal information into whatever website or chatbot seems helpful at the moment. Names, dates of birth, addresses, lab values, clinic details… all shared out of habit, usually because we just want answers quickly. This guide uses a simple, illustrative scenario—a symptom checker with lab report summarization—to show how hybrid AI can help reduce that oversharing. It’s not a medical product or a clinical solution, but it’s a great way to understand the pattern. With Microsoft Foundry, Foundry Local, and the Agent Framework, we can build workflows where sensitive data stays on the user’s machine and is processed locally, while the cloud handles the heavier reasoning. Only a safe, structured summary ever leaves the device. The Agent Framework handles when to use the local model vs. the cloud model, giving us a seamless and privacy-preserving hybrid experience. Demo scenario This demo uses a simple, illustrative symptom-checker to show how hybrid AI keeps sensitive data private while still benefiting from powerful cloud reasoning. It’s not a medical product—just an easy way to demonstrate the pattern: Here’s what happens: A Python agent (Agent Framework) runs locally and can call both cloud models and local tools. Azure AI Foundry (GPT-4o) handles reasoning and triage logic but never sees raw PHI. Foundry Local runs a small LLM (phi-4-mini) on your GPU and processes the raw lab report entirely on-device. A tool function (@ai_function) lets the agent call the local model automatically when it detects lab-like text. The flow is simple: user_message = symptoms + raw lab text agent → calls local tool → local LLM returns JSON cloud LLM → uses JSON to produce guidance Environment setup Foundry Local Service On the local machine with GPU, let's install Foundry local using: PS C: \Windows\system32> winget install Microsoft.FoundryLocal Then let's download our local model, in this case phi-4-mini and test it: PS C:\Windows\system32> foundry model download phi-4-mini Downloading Phi-4-mini-instruct-cuda-gpu:5... [################### ] 53.59 % [Time remaining: about 4m] 5.9 MB/s/s PS C:\Windows\system32> foundry model load phi-4-mini 🕗 Loading model... 🟢 Model phi-4-mini loaded successfully PS C:\Windows\system32> foundry model run phi-4-mini Model Phi-4-mini-instruct-cuda-gpu:5 was found in the local cache. Interactive Chat. Enter /? or /help for help. Press Ctrl+C to cancel generation. Type /exit to leave the chat. Interactive mode, please enter your prompt > Hello can you let me know who you are and which model you are using 🧠 Thinking... 🤖 Hello! I'm Phi, an AI developed by Microsoft. I'm here to help you with any questions or tasks you have. How can I assist you today? > PS C:\Windows\system32> foundry service status 🟢 Model management service is running on http://127.0.0.1:52403/openai/status Now we see the model is accessible with API on the localhost with port 52403. Foundry Local models don’t always use simple names like "phi-4-mini". Each installed model has a specific Model ID that Foundry Local assigns (for example: Phi-4-mini-instruct-cuda-gpu:5 in this case). We now can use the Model ID for a quick test: from openai import OpenAI client = OpenAI(base_url="http://127.0.0.1:52403/v1", api_key="ignored") resp = client.chat.completions.create( model="Phi-4-mini-instruct-cuda-gpu:5", messages=[{"role": "user", "content": "Say hello"}]) Returned 200 OK. Microsoft Foundry To handle the cloud part of the hybrid workflow, we start by creating a Microsoft AI Foundry project. This gives us an easy, managed way to use models like GPT-4o-mini —no deployment steps, no servers to configure. You simply point the Agent Framework at your project, authenticate, and you’re ready to call the model. A nice benefit is that Microsoft Foundry and Foundry Local share the same style of API. Whether you call a model in the cloud or on your own machine, the request looks almost identical. This consistency makes hybrid development much easier: the agent doesn’t need different logic for local vs. cloud models—it just switches between them when needed. Under the Hood of Our Hybrid AI Workflow Agent Framework For the agent code, I am using the Agent Framework libraries, and I am giving specific instructions to the agent as per below: from agent_framework import ChatAgent, ai_function from agent_framework.azure import AzureAIAgentClient from azure.identity.aio import AzureCliCredential # ========= Cloud Symptom Checker Instructions ========= SYMPTOM_CHECKER_INSTRUCTIONS = """ You are a careful symptom-checker assistant for non-emergency triage. General behavior: - You are NOT a clinician. Do NOT provide medical diagnosis or prescribe treatment. - First, check for red-flag symptoms (e.g., chest pain, trouble breathing, severe bleeding, stroke signs, one-sided weakness, confusion, fainting). If any are present, advise urgent/emergency care and STOP. - If no red-flags, summarize key factors (age group, duration, severity), then provide: 1) sensible next steps a layperson could take, 2) clear guidance on when to contact a clinician, 3) simple self-care advice if appropriate. - Use plain language, under 8 bullets total. - Always end with: "This is not medical advice." Tool usage: - When the user provides raw lab report text, or mentions “labs below” or “see labs”, you MUST call the `summarize_lab_report` tool to convert the labs into structured data before giving your triage guidance. - Use the tool result as context, but do NOT expose the raw JSON directly. Instead, summarize the key abnormal findings in plain language. """.strip() Referencing the local model Now I am providing a system prompt for the locally inferred model to transform the lab result text into a JSON object with lab results only: # ========= Local Lab Summarizer (Foundry Local + Phi-4-mini) ========= FOUNDRY_LOCAL_BASE = "http://127.0.0.1:52403" # from `foundry service status` FOUNDRY_LOCAL_CHAT_URL = FOUNDRY_LOCAL_BASE + "/v1/chat/completions" # This is the model id you confirmed works: FOUNDRY_LOCAL_MODEL_ID = "Phi-4-mini-instruct-cuda-gpu:5" LOCAL_LAB_SYSTEM_PROMPT = """ You are a medical lab report summarizer running locally on the user's machine. You MUST respond with ONLY one valid JSON object. Do not include any explanation, backticks, markdown, or text outside the JSON. The JSON must have this shape: { "overall_assessment": "<short plain English summary>", "notable_abnormal_results": [ { "test": "string", "value": "string", "unit": "string or null", "reference_range": "string or null", "severity": "mild|moderate|severe" } ] } If you are unsure about a field, use null. Do NOT invent values. """.strip() Agent Framework tool In this next step, we wrap the local Foundry inference inside an Agent Framework tool using the AI_function decorator. This abstraction is more than styler—it is the recommended best practice for hybrid architectures. By exposing local GPU inference as a tool, the cloud-hosted agent can decide when to call it, pass structured arguments, and consume the returned JSON seamlessly. It also ensures that the raw lab text (which may contain PII) stays strictly within the local function boundary, never entering the cloud conversation. Using a tool in this way provides a consistent, declarative interface, enables automatic reasoning and tool-routing by frontier models, and keeps the entire hybrid workflow maintainable, testable, and secure: @ai_function( name="summarize_lab_report", description=( "Summarize a raw lab report into structured abnormalities using a local model " "running on the user's GPU. Use this whenever the user provides lab results as text." ), ) def summarize_lab_report( lab_text: Annotated[str, Field(description="The raw text of the lab report to summarize.")], ) -> Dict[str, Any]: """ Tool: summarize a lab report using Foundry Local (Phi-4-mini) on the user's GPU. Returns a JSON-compatible dict with: - overall_assessment: short text summary - notable_abnormal_results: list of abnormal test objects """ payload = { "model": FOUNDRY_LOCAL_MODEL_ID, "messages": [ {"role": "system", "content": LOCAL_LAB_SYSTEM_PROMPT}, {"role": "user", "content": lab_text}, ], "max_tokens": 256, "temperature": 0.2, } headers = { "Content-Type": "application/json", } print(f"[LOCAL TOOL] POST {FOUNDRY_LOCAL_CHAT_URL}") resp = requests.post( FOUNDRY_LOCAL_CHAT_URL, headers=headers, data=json.dumps(payload), timeout=120, ) resp.raise_for_status() data = resp.json() # OpenAI-compatible shape: choices[0].message.content content = data["choices"][0]["message"]["content"] # Handle string vs list-of-parts if isinstance(content, list): content_text = "".join( part.get("text", "") for part in content if isinstance(part, dict) ) else: content_text = content print("[LOCAL TOOL] Raw content from model:") print(content_text) # Strip ```json fences if present, then parse JSON cleaned = _strip_code_fences(content_text) lab_summary = json.loads(cleaned) print("[LOCAL TOOL] Parsed lab summary JSON:") print(json.dumps(lab_summary, indent=2)) # Return dict – Agent Framework will serialize this as the tool result return lab_summary The case, labs and prompt All patient and provider information in below example is entirely fictitious and used for illustrative purposes only. To illustrate the pattern, this sample prepares the “case” in code: it combines a symptom description with a lab report string and then submits that prompt to the agent. In production, these inputs would be captured from a UI or API. # Example free-text case + raw lab text that the agent can decide to send to the tool case = ( "Teenager with bad headache and throwing up. Fever of 40C and no other symptoms." ) lab_report_text = """ ------------------------------------------- AI Land FAMILY LABORATORY SERVICES 4420 Camino Del Foundry, Suite 210 Gpuville, CA 92108 Phone: (123) 555-4821 | Fax: (123) 555-4822 ------------------------------------------- PATIENT INFORMATION Name: Frontier Model DOB: 04/12/2007 (17 yrs) Sex: Male Patient ID: AXT-442871 Address: 1921 MCP Court, CA 01100 ORDERING PROVIDER Dr. Bot, MD NPI: 1780952216 Clinic: Phi Pediatrics Group REPORT DETAILS Accession #: 24-SDFLS-118392 Collected: 11/14/2025 14:32 Received: 11/14/2025 16:06 Reported: 11/14/2025 20:54 Specimen: Whole Blood (EDTA), Serum Separator Tube ------------------------------------------------------ COMPLETE BLOOD COUNT (CBC) ------------------------------------------------------ WBC ................. 14.5 x10^3/µL (4.0 – 10.0) HIGH RBC ................. 4.61 x10^6/µL (4.50 – 5.90) Hemoglobin .......... 13.2 g/dL (13.0 – 17.5) LOW-NORMAL Hematocrit .......... 39.8 % (40.0 – 52.0) LOW MCV ................. 86.4 fL (80 – 100) Platelets ........... 210 x10^3/µL (150 – 400) ------------------------------------------------------ INFLAMMATORY MARKERS ------------------------------------------------------ C-Reactive Protein (CRP) ......... 60 mg/L (< 5 mg/L) HIGH Erythrocyte Sedimentation Rate ... 32 mm/hr (0 – 15 mm/hr) HIGH ------------------------------------------------------ BASIC METABOLIC PANEL (BMP) ------------------------------------------------------ Sodium (Na) .............. 138 mmol/L (135 – 145) Potassium (K) ............ 3.9 mmol/L (3.5 – 5.1) Chloride (Cl) ............ 102 mmol/L (98 – 107) CO2 (Bicarbonate) ........ 23 mmol/L (22 – 29) Blood Urea Nitrogen (BUN) 11 mg/dL (7 – 20) Creatinine ................ 0.74 mg/dL (0.50 – 1.00) Glucose (fasting) ......... 109 mg/dL (70 – 99) HIGH ------------------------------------------------------ LIVER FUNCTION TESTS ------------------------------------------------------ AST ....................... 28 U/L (0 – 40) ALT ....................... 22 U/L (0 – 44) Alkaline Phosphatase ...... 144 U/L (65 – 260) Total Bilirubin ........... 0.6 mg/dL (0.1 – 1.2) ------------------------------------------------------ NOTES ------------------------------------------------------ Mild leukocytosis and elevated inflammatory markers (CRP, ESR) may indicate an acute infectious or inflammatory process. Glucose slightly elevated; could be non-fasting. ------------------------------------------------------ END OF REPORT SDFLS-CLIA ID: 05D5554973 This report is for informational purposes only and not a diagnosis. ------------------------------------------------------ """ # Single user message that gives both the case and labs. # The agent will see that there are labs and call summarize_lab_report() as a tool. user_message = ( "Patient case:\n" f"{case}\n\n" "Here are the lab results as raw text. If helpful, you can summarize them first:\n" f"{lab_report_text}\n\n" "Please provide non-emergency triage guidance." ) The Hybrid Agent code Here’s where the hybrid behavior actually comes together. By this point, we’ve defined a local tool that talks to Foundry Local and configured access to a cloud model in Azure AI Foundry. In the main() function, the Agent Framework ties these pieces into a single workflow. The agent runs locally, receives a message containing both symptoms and a raw lab report, and decides when to call the local tool. The lab report is summarized on your GPU, and only the structured JSON is passed to the cloud model for reasoning. The snippet below shows how we attach the tool to the agent and trigger both local inference and cloud guidance within one natural-language prompt # ========= Hybrid Main (Agent uses the local tool) ========= async def main(): ... async with ( AzureCliCredential() as credential, ChatAgent( chat_client=AzureAIAgentClient(async_credential=credential), instructions=SYMPTOM_CHECKER_INSTRUCTIONS, # 👇 Tool is now attached to the agent tools=[summarize_lab_report], name="hybrid-symptom-checker", ) as agent, ): result = await agent.run(user_message) print("\n=== Symptom Checker (Hybrid: Local Tool + Cloud Agent) ===\n") print(result.text) if __name__ == "__main__": asyncio.run(main()) Testing the Hybrid Agent Now I am running the agent code from VSCode and can see the local inference happening when lab was submitted. Then results are formatted, PII omitted and the GPT-40 model can process the symptom along the results What's next In this example, the agent runs locally and pulls in both cloud and local inference. In Part 2, we’ll explore the opposite architecture: a cloud-hosted agent that can safely call back into a local LLM through a secure gateway. This opens the door to more advanced hybrid patterns where tools running on edge devices, desktops, or on-prem systems can participate in cloud-driven workflows without exposing sensitive data. References Agent Framework: https://github.com/microsoft/agent-framework Repo for the code available here:Foundry IQ: Unlocking ubiquitous knowledge for agents
Introducing Foundry IQ by Azure AI Search in Microsoft Foundry. Foundry IQ is a centralized knowledge layer that connects agents to data with the next generation of retrieval-augmented generation (RAG). Foundry IQ includes the following features: Knowledge bases: Available directly in the new Foundry portal, knowledge bases are reusable, topic-centric collections that ground multiple agents and applications through a single API. Automated indexed and federated knowledge sources – Expand what data an agent can reach by connecting to both indexed and remote knowledge sources. For indexed sources, Foundry IQ delivers automatic indexing, vectorization, and enrichment for text, images, and complex documents. Agentic retrieval engine in knowledge bases – A self-reflective query engine that uses AI to plan, select sources, search, rank and synthesize answers across sources with configurable “retrieval reasoning effort.” Enterprise-grade security and governance – Support for document-level access control, alignment with existing permissions models, and options for both indexed and remote data. Foundry IQ is available in public preview through the new Foundry portal and Azure portal with Azure AI Search. Foundry IQ is part of Microsoft's intelligence layer with Fabric IQ and Work IQ.
Less models in ai foundry that supports agentic use
Hi, I have seen that nearly 11,000 models are available in Azure ai foundry, but when I try to deploy models that support Agents, only 18 models are available for selection. Is there any reason behind this ? Are we planning to support many models from external providers or rely on gpt models as first priority
Announcing Elastic MCP Server in Microsoft Foundry Tool Catalog
Introduction The future of enterprise AI is agentic - driven by intelligent, context-aware agents that deliver real business value. Microsoft Foundry is committed to enabling developers with the tools and integrations they need to build, deploy, and govern these advanced AI solutions. Today, we are excited to announce that Elastic MCP Server is now discoverable in the Microsoft Foundry Tool Catalog, unlocking seamless access to Elastic’s industry-leading vector search capabilities for Retrieval-Augmented Generation (RAG) scenarios. Seamless Integration: Elastic Meets Microsoft Foundry This integration is a major milestone in our ongoing effort to foster an open, extensible AI ecosystem. With Elastic MCP Server now available in the Azure MCP registry, developers can easily connect their agents to Elastic’s powerful search and analytics engine using the Model Context Protocol (MCP). This ensures that agents built on Microsoft Foundry are grounded in trusted, enterprise-grade data - delivering accurate, relevant, and verifiable responses. Create Elastic cloud hosted deployments or Serverless Search Projects through the Microsoft Marketplace or the Azure Portal Discoverability: Elastic MCP Server is listed as a remote MCP server in the Azure MCP Registry and the Foundry Tool Catalog. Multi-Agent Workflows: Enable collaborative agent scenarios via the A2A protocol. Unlocking Vector Search for RAG Elastic’s advanced vector search capabilities are now natively accessible to Foundry agents, enabling powerful Retrieval-Augmented Generation (RAG) workflows: Semantic Search: Agents can perform hybrid and vector-based searches over enterprise data, retrieving the most relevant context for grounding LLM responses. Customizable Retrieval: With Elastic’s Agent Builder, you can define your custom tools specific to your indices and datasets and expose them to Foundry Agents via MCP. Enterprise Grounding: Ensure agent outputs are always based on proprietary, up-to-date data, reducing hallucinations and improving trust. Deployment: Getting Started Follow these steps to integrate Elastic MCP Server with your Foundry agents: Within your Foundry project, you can either: Go to Build in the top menu, then select Tools. Click on Connect a Tool. Select the Catalog tab, search for Elasticsearch, and click Create. Once prompted, configure the Elasticsearch details by providing a name, your Kibana endpoint, and your Elasticsearch API key. Click on Use in an agent and select an existing Agent to integrate Elastic MCP Server. Alternatively, within your Agent: Click on Tools. Click Add, then select Custom. Search for Elasticsearch, add it, and configure the tool as described above. The tool will now appear in your Agent’s configuration. You are all set to now interact with your Elasticsearch projects and deployments! Conclusion & Next Steps The addition of Elastic MCP Server to the Foundry Tool Catalog empowers developers to build the next generation of intelligent, grounded AI agents - combining Microsoft’s agentic platform with Elastic’s cutting-edge vector search. Whether you’re building RAG-powered copilots, automating workflows, or orchestrating multi-agent systems, this integration accelerates your journey from prototype to production. Ready to get started? Get started with Elastic via the Azure Marketplace or Azure portal. New users get a 7-day free trial! Explore agent creation in Microsoft Foundryportal and try the Foundry Tool Catalog. Deep dive into Elastic MCP and Agent Builder Join us at Microsoft Ignite 2025 for live demos, deep dives, and more on building agentic AI with Elastic and Microsoft Foundry!Foundry IQ: boost response relevance by 36% with agentic retrieval
The latest RAG performance evaluations and results for knowledge bases and built-in agentic retrieval engine. Foundry IQ by Azure AI Search is a unified knowledge layer for agents, designed to improve response performance, automate RAG workflows and enable enterprise-ready grounding. These evaluations tested RAG performance for knowledge bases and new features including retrieval reasoning effort and federated sources like web and SharePoint for M365. Foundry IQ and Azure AI Search are part of Microsoft Foundry.
