Integrate Custom Azure AI Agents with Copilot Studio and M365 Copilot
Integrating Custom Agents with Copilot Studio and M365 Copilot In today's fast-paced digital world, integrating custom agents with Copilot Studio and M365 Copilot can significantly enhance your company's digital presence and extend your CoPilot platform to your enterprise applications and data. This blog will guide you through the integration steps of bringing your custom Azure AI Agent Service within an Azure Function App, into a Copilot Studio solution and publishing it to M365 and Teams Applications. When Might This Be Necessary: Integrating custom agents with Copilot Studio and M365 Copilot is necessary when you want to extend customization to automate tasks, streamline processes, and provide better user experience for your end-users. This integration is particularly useful for organizations looking to streamline their AI Platform, extend out-of-the-box functionality, and leverage existing enterprise data and applications to optimize their operations. Custom agents built on Azure allow you to achieve greater customization and flexibility than using Copilot Studio agents alone. What You Will Need: To get started, you will need the following: Azure AI Foundry Azure OpenAI Service Copilot Studio Developer License Microsoft Teams Enterprise License M365 Copilot License Steps to Integrate Custom Agents: Create a Project in Azure AI Foundry: Navigate to Azure AI Foundry and create a project. Select 'Agents' from the 'Build and Customize' menu pane on the left side of the screen and click the blue button to create a new agent. Customize Your Agent: Your agent will automatically be assigned an Agent ID. Give your agent a name and assign the model your agent will use. Customize your agent with instructions: Add your knowledge source: You can connect to Azure AI Search, load files directly to your agent, link to Microsoft Fabric, or connect to third-party sources like Tripadvisor. In our example, we are only testing the CoPilot integration steps of the AI Agent, so we did not build out additional options of providing grounding knowledge or function calling here. Test Your Agent: Once you have created your agent, test it in the playground. If you are happy with it, you are ready to call the agent in an Azure Function. Create and Publish an Azure Function: Use the sample function code from the GitHub repository to call the Azure AI Project and Agent. Publish your Azure Function to make it available for integration. azure-ai-foundry-agent/function_app.py at main · azure-data-ai-hub/azure-ai-foundry-agent Connect your AI Agent to your Function: update the "AIProjectConnString" value to include your Project connection string from the project overview page of in the AI Foundry. Role Based Access Controls: We have to add a role for the function app on OpenAI service. Role-based access control for Azure OpenAI - Azure AI services | Microsoft Learn Enable Managed Identity on the Function App Grant "Cognitive Services OpenAI Contributor" role to the System-assigned managed identity to the Function App in the Azure OpenAI resource Grant "Azure AI Developer" role to the System-assigned managed identity for your Function App in the Azure AI Project resource from the AI Foundry Build a Flow in Power Platform: Before you begin, make sure you are working in the same environment you will use to create your Copilot Studio agent. To get started, navigate to the Power Platform (https://make.powerapps.com) to build out a flow that connects your Copilot Studio solution to your Azure Function App. When creating a new flow, select 'Build an instant cloud flow' and trigger the flow using 'Run a flow from Copilot'. Add an HTTP action to call the Function using the URL and pass the message prompt from the end user with your URL. The output of your function is plain text, so you can pass the response from your Azure AI Agent directly to your Copilot Studio solution. Create Your Copilot Studio Agent: Navigate to Microsoft Copilot Studio and select 'Agents', then 'New Agent'. Make sure you are in the same environment you used to create your cloud flow. Now select ‘Create’ button at the top of the screen From the top menu, navigate to ‘Topics’ and ‘System’. We will open up the ‘Conversation boosting’ topic. When you first open the Conversation boosting topic, you will see a template of connected nodes. Delete all but the initial ‘Trigger’ node. Now we will rebuild the conversation boosting agent to call the Flow you built in the previous step. Select 'Add an Action' and then select the option for existing Power Automate flow. Pass the response from your Custom Agent to the end user and end the current topic. My existing Cloud Flow: Add action to connect to existing Cloud Flow: When this menu pops up, you should see the option to Run the flow you created. Here, mine does not have a very unique name, but you see my flow 'Run a flow from Copilot' as a Basic action menu item. If you do not see your cloud flow here add the flow to the default solution in the environment. Go to Solutions > select the All pill > Default Solution > then add the Cloud Flow you created to the solution. Then go back to Copilot Studio, refresh and the flow will be listed there. Now complete building out the conversation boosting topic: Make Agent Available in M365 Copilot: Navigate to the 'Channels' menu and select 'Teams + Microsoft 365'. Be sure to select the box to 'Make agent available in M365 Copilot'. Save and re-publish your Copilot Agent. It may take up to 24 hours for the Copilot Agent to appear in M365 Teams agents list. Once it has loaded, select the 'Get Agents' option from the side menu of Copilot and pin your Copilot Studio Agent to your featured agent list Now, you can chat with your custom Azure AI Agent, directly from M365 Copilot! Conclusion: By following these steps, you can successfully integrate custom Azure AI Agents with Copilot Studio and M365 Copilot, enhancing you’re the utility of your existing platform and improving operational efficiency. This integration allows you to automate tasks, streamline processes, and provide better user experience for your end-users. Give it a try! Curious of how to bring custom models from your AI Foundry to your Copilot Studio solutions? Check out this blog
AI Hub --> Project Structure In Microsoft Foundry
The AI Hub → Project structure works great for a single team. But when you've got a large org with multiple departments, each running their own hub with several projects. I found it doesn't quite fit the deployment model we needed. Here's the scenario: I create a hub per department, and they can share resources and apply governance across their projects. But I also need org-level policies that apply across all department hubs. And visibility into programs that span multiple departments. With the current two-level structure, I don't have a structural layer for that. Current options both have tradeoffs: Single org-wide hub with departments as projects = lose department-level resource isolation and independent governance Separate hubs per department = manually replicate org-level policies, no rollup reporting across departments For my scenario, it would help if: there was an intermediate level , either nested hubs or an explicit "portfolio/program" grouping, so governance can work at both org and department levels, with rollup visibility. Curious: are others running into this? How are you structuring org-level governance across multiple department hubs? Looking forward for suggestions on this, how others are doing this.Context-Aware RAG System with Azure AI Search to Cut Token Costs and Boost Accuracy
🚀 Introduction As AI copilots and assistants become integral to enterprises, one question dominates architecture discussions: “How can we make large language models (LLMs) provide accurate, source-grounded answers — without blowing up token costs?” Retrieval-Augmented Generation (RAG) is the industry’s go-to strategy for this challenge. But traditional RAG pipelines often use static document chunking, which breaks semantic context and drives inefficiencies. To address this, we built a context-aware, cost-optimized RAG pipeline using Azure AI Search and Azure OpenAI, leveraging AI-driven semantic chunking and intelligent retrieval. The result: accurate answers with up to 85% lower token consumption. Majorly in this blog we are considering: Tokenization Chunking The Problem with Naive Chunking Most RAG systems split documents by token or character count (e.g., every 1,000 tokens). This is easy to implement but introduces real-world problems: 🧩 Loss of context — sentences or concepts get split mid-idea. ⚙️ Retrieval noise — irrelevant fragments appear in top results. 💸 Higher cost — you often send 5× more text than necessary. These issues degrade both accuracy and cost efficiency. 🧠 Context-Aware Chunking: Smarter Document Segmentation Instead of breaking text arbitrarily, our system uses an LLM-powered preprocessor to identify semantic boundaries — meaning each chunk represents a complete and coherent concept. Example Naive chunking: “Azure OpenAI Service offers… [cut] …integrates with Azure AI Search for intelligent retrieval.” Context-aware chunking: “Azure OpenAI Service provides access to models like GPT-4o, enabling developers to integrate advanced natural language understanding and generation into their applications. It can be paired with Azure AI Search for efficient, context-aware information retrieval.” ✅ The chunk is self-contained and semantically meaningful. This allows the retriever to match queries with conceptually complete information rather than partial sentences — leading to precision and fewer chunks needed per query. Architecture Diagram Chunking Service: Purpose: Transforms messy enterprise data (wikis, PDFs, transcripts, repos, images) into structured, model-friendly chunks for Retrieval-Augmented Generation (RAG). ChallengeChunking FixLLM context limitsBreaks docs into smaller piecesEmbedding sizeKeeps within token boundsRetrieval accuracyGranular, relevant sections onlyNoiseRemoves irrelevant blocksTraceabilityChunk IDs for auditabilityCost/latencyRe-embed only changed chunks The Chunking Flow (End-to-End) The Chunking Service sits in the ingestion pipeline and follows this sequence: Ingestion: Raw text arrives from sources (wiki, repo, transcript, PDF, image description). Token-aware splitting: Large text is cut into manageable pre-chunks with a 100-token overlap, ensuring no semantic drift across boundaries. Semantic segmentation: Each pre-chunk is passed to an Azure OpenAI Chat model with a structured prompt. Output = JSON array of semantic chunks (sectiontitle, speaker, content). Optional overlap injection: Character-level overlap can be applied across chunks for discourse-heavy text like meeting transcripts. Embedding generation: Each chunk is passed to Azure OpenAI Embeddings API (text-embedding-3-small), producing a 1536-dimension vector. Indexing: Chunks (text + vectors) are uploaded to Azure AI Search. Retrieval: During question answering or document generation, the system pulls top-k chunks, concatenates them, and enriches the prompt for the LLM. Resilience & Traceability The service is built to handle real-world pipeline issues. It retries once on rate limits, validates JSON outputs, and fails fast on malformed data instead of silently dropping chunks. Each chunk is assigned a unique ID (chunk_<sequence>_<sourceTag>), making retrieval auditable and enabling selective re-embedding when only parts of a document change. ☁️ Why Azure AI Search Matters Here Azure AI Search (formerly Cognitive Search) is the heart of the retrieval pipeline. Key Roles: Vector Search Engine: Stores embeddings of chunks and performs semantic similarity search. Hybrid Search (Keyword + Vector): Combines lexical and semantic matching for high precision and recall. Scalability: Supports millions of chunks with blazing-fast search latency. Metadata Filtering: Enables fine-grained retrieval (e.g., by document type, author, section). Native Integration with Azure OpenAI: Allows a seamless, end-to-end RAG pipeline without third-party dependencies. In short, Azure AI Search provides the speed, scalability, and semantic intelligence to make your RAG pipeline enterprise-grade. 💡 Importance of Azure OpenAI Azure OpenAI complements Azure AI Search by providing: High-quality embeddings (text-embedding-3-large) for accurate vector search. Powerful generative reasoning (GPT-4o or GPT-4.1) to craft contextually relevant answers. Security and compliance within your organization’s Azure boundary — critical for regulated environments. Together, these two services form the retrieval (Azure AI Search) and generation (Azure OpenAI) halves of your RAG system. 💰 Token Efficiency By limiting the model’s input to only the most relevant, semantically meaningful chunks, you drastically reduce prompt size and cost. Approach Tokens per Query Typical Cost Accuracy Full-document prompt ~15,000–20,000 Very high Medium Fixed-size RAG chunks ~5,000–8,000 Moderate Medium-high Context-aware RAG (this approach) ~2,000–3,000 Low High 💰 Token Cost Reduction Analysis Let’s quantify it: Step Naive Approach (no RAG) Your Approach (Context-Aware RAG) Prompt context size Entire document (e.g., 15,000 tokens) Top 3 chunks (e.g., 2,000 tokens) Tokens per query ~16,000 (incl. user + system) ~2,500 Cost reduction — ~84% reduction in token usage Accuracy Often low (hallucinations) Higher (targeted retrieval) That’s roughly an 80–85% reduction in token usage while improving both accuracy and response speed. 🧱 Tech Stack Overview Component Service Purpose Chunking Engine Azure OpenAI (GPT models) Generate context-aware chunks Embedding Model Azure OpenAI Embedding API Create high-dimensional vectors Retriever Azure AI Search Perform hybrid and vector search Generator Azure OpenAI GPT-4o Produce final answer Orchestration Layer Python / FastAPI / .NET c# Handle RAG pipeline 🔍 The Bottom Line By adopting context-aware chunking and Azure AI Search-powered RAG, you achieve: ✅ Higher accuracy (contextually complete retrievals) 💸 Lower cost (token-efficient prompts) ⚡ Faster latency (smaller context per call) 🧩 Scalable and secure architecture (fully Azure-native) This is the same design philosophy powering Microsoft Copilot and other enterprise AI assistants today. 🧪 Real-Life Example: Context-Aware RAG in Action To bring this architecture to life, let’s walk through a simple example of how documents can be chunked, embedded, stored in Azure AI Search, and then queried to generate accurate, cost-efficient answers. Imagine you want to build an internal knowledge assistant that answers developer questions from your company’s Azure documentation. ⚙️ Step 1: Intelligent Document Chunking We’ll use a small LLM call to segment text into context-aware chunks — rather than fixed token counts //Context Aware Chunking //text can be your retrieved text from any page/ document private async Task<List<SemanticChunk>> AzureOpenAIChunk(string text) { try { string prompt = $@" Divide the following text into logical, meaningful chunks. Each chunk should represent a coherent section, topic, or idea. Return the result as a JSON array, where each object contains: - sectiontitle - speaker (if applicable, otherwise leave empty) - content Do not add any extra commentary or explanation. Only output the JSON array. Do not give content an array, try to keep all in string. TEXT: {text}" var client = GetAzureOpenAIClient(); var chatCompletionsOptions = new ChatCompletionOptions { Temperature = 0, FrequencyPenalty = 0, PresencePenalty = 0 }; var Messages = new List<OpenAI.Chat.ChatMessage> { new SystemChatMessage("You are a text processing assistant."), new UserChatMessage(prompt) }; var chatClient = client.GetChatClient( deploymentName: _appSettings.Agent.Model); var response = await chatClient.CompleteChatAsync(Messages, chatCompletionsOptions); string responseText = response.Value.Content[0].Text.ToString(); string cleaned = Regex.Replace(responseText, @"```[\s\S]*?```", match => { var match1 = match.Value.Replace("```json", "").Trim(); return match1.Replace("```", "").Trim(); }); // Try to parse the response as JSON array of chunks return CreateChunkArray(cleaned); } catch (JsonException ex) { _logger.LogError("Failed to parse GPT response: " + ex.Message); throw; } catch (Exception ex) { _logger.LogError("Error in AzureOpenAIChunk: " + ex.Message); throw; } } 🧠 Step 2: Adding Overlaps for better result We are adding overlapping between chunks for better and accurate answers. Overlapping window can be modified based on the documents. public List<SemanticChunk> AddOverlap(List<SemanticChunk> chunks, string IDText, int overlapChars = 0) { var overlappedChunks = new List<SemanticChunk>(); for (int i = 0; i < chunks.Count; i++) { var current = chunks[i]; string previousOverlap = i > 0 ? chunks[i - 1].Content[^Math.Min(overlapChars, chunks[i - 1].Content.Length)..] : ""; string combinedText = previousOverlap + "\n" + current.Content; var Id = $"chunk_{i + '_' + IDText}"; overlappedChunks.Add(new SemanticChunk { Id = Regex.Replace(Id, @"[^A-Za-z0-9_\-=]", "_"), Content = combinedText, SectionTitle = current.SectionTitle }); } return overlappedChunks; } 🧠 Step 3: Generate and Store Embeddings in Azure AI Search We convert each chunk into an embedding vector and push it to an Azure AI Search index. public async Task<List<SemanticChunk>> AddEmbeddings(List<SemanticChunk> chunks) { var client = GetAzureOpenAIClient(); var embeddingClient = client.GetEmbeddingClient("text-embedding-3-small"); foreach (var chunk in chunks) { // Generate embedding using the EmbeddingClient var embeddingResult = await embeddingClient.GenerateEmbeddingAsync(chunk.Content).ConfigureAwait(false); chunk.Embedding = embeddingResult.Value.ToFloats(); } return chunks; } public async Task UploadDocsAsync(List<SemanticChunk> chunks) { try { var indexClient = GetSearchindexClient(); var searchClient = indexClient.GetSearchClient(_indexName); var result = await searchClient.UploadDocumentsAsync(chunks); } catch (Exception ex) { _logger.LogError("Failed to upload documents: " + ex); throw; } } 🤖 Step 4: Generate the Final Answer with Azure OpenAI Now we combine the top chunks with the user query to create a cost-efficient, context-rich prompt. P.S. : Here in this example we have used semantic kernel agent , in real time any agent can be used and any prompt can be updated. var context = await _aiSearchService.GetSemanticSearchresultsAsync(UserQuery); // Gets chunks from Azure AI Search //here UserQuery is query asked by user/any question prompt which need to be answered. string questionWithContext = $@"Answer the question briefly in short relevant words based on the context provided. Context : {context}. \n\n Question : {UserQuery}?"; var _agentModel = new AgentModel() { Model = _appSettings.Agent.Model, AgentName = "Answering_Agent", Temperature = _appSettings.Agent.Temperature, TopP = _appSettings.Agent.TopP, AgentInstructions = $@"You are a cloud Migration Architect. " + "Analyze all the details from top to bottom in context based on the details provided for the Migration of APP app using Azure Services. Do not assume anything." + "There can be conflicting details for a question , please verify all details of the context. If there are any conflict please start your answer with word - **Conflict**." + "There might not be answers for all the questions, please verify all details of the context. If there are no answer for question just mention - **No Information**" }; _agentModel = await _agentService.CreateAgentAsync(_agentModel); _agentModel.QuestionWithContext = questionWithContext; var modelWithResponse = await _agentService.GetAnswerAsync(_agentModel); 🧠 Final Thoughts Context-aware RAG isn’t just a performance optimization — it’s an architectural evolution. It shifts the focus from feeding LLMs more data to feeding them the right data. By letting Azure AI Search handle intelligent retrieval and Azure OpenAI handle reasoning, you create an efficient, explainable, and scalable AI assistant. The outcome: Smarter answers, lower costs, and a pipeline that scales with your enterprise. Wiki Link: Tokenization and Chunking IP Link: AI Migration Accelerator
Import error: Cannot import name "PromptAgentDefinition" from "azure.ai.projects.models"
Hello, I am trying to build the agentic retrieval using Azure Ai search. During the creation of agent i am getting "ImportError: cannot import name 'PromptAgentDefinition' from 'azure.ai.projects.models'". Looked into possible ways of building without it but I need the mcp connection. This is the documentation i am following: https://learn.microsoft.com/en-us/azure/search/agentic-retrieval-how-to-create-pipeline?tabs=search-perms%2Csearch-development%2Cfoundry-setup Note: There is no Promptagentdefinition in the directory of azure.ai.projects.models. ['ApiKeyCredentials', 'AzureAISearchIndex', 'BaseCredentials', 'BlobReference', 'BlobReferenceSasCredential', 'Connection', 'ConnectionType', 'CosmosDBIndex', 'CredentialType', 'CustomCredential', 'DatasetCredential', 'DatasetType', 'DatasetVersion', 'Deployment', 'DeploymentType', 'EmbeddingConfiguration', 'EntraIDCredentials', 'EvaluatorIds', 'FieldMapping', 'FileDatasetVersion', 'FolderDatasetVersion', 'Index', 'IndexType', 'ManagedAzureAISearchIndex', 'ModelDeployment', 'ModelDeploymentSku', 'NoAuthenticationCredentials', 'PendingUploadRequest', 'PendingUploadResponse', 'PendingUploadType', 'SASCredentials', 'TYPE_CHECKING', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '_enums', '_models', '_patch', '_patch_all', '_patch_evaluations', '_patch_sdk'] Traceback (most recent call last): Please let me know what i should do and if there is any other alternative. Thanks in advance.Get to know the core Foundry solutions
Foundry includes specialized services for vision, language, documents, and search, plus Microsoft Foundry for orchestration and governance. Here’s what each does and why it matters: Azure Vision With Azure Vision, you can detect common objects in images, generate captions, descriptions, and tags based on image contents, and read text in images. Example: Automate visual inspections or extract text from scanned documents. Azure Language Azure Language helps organizations understand and work with text at scale. It can identify key information, gauge sentiment, and create summaries from large volumes of content. It also supports building conversational experiences and question-answering tools, making it easier to deliver fast, accurate responses to customers and employees. Example: Understand customer feedback or translate text into multiple languages. Azure Document IntelligenceWith Azure Document Intelligence, you can use pre-built or custom models to extract fields from complex documents such as invoices, receipts, and forms. Example: Automate invoice processing or contract review. Azure SearchAzure Search helps you find the right information quickly by turning your content into a searchable index. It uses AI to understand and organize data, making it easier to retrieve relevant insights. This capability is often used to connect enterprise data with generative AI, ensuring responses are accurate and grounded in trusted information. Example: Help employees retrieve policies or product details without digging through files. Microsoft FoundryActs as the orchestration and governance layer for generative AI and AI agents. It provides tools for model selection, safety, observability, and lifecycle management. Example: Coordinate workflows that combine multiple AI capabilities with compliance and monitoring. Business leaders often ask: Which Foundry tool should I use? The answer depends on your workflow. For example: Are you trying to automate document-heavy processes like invoice handling or contract review? Do you need to improve customer engagement with multilingual support or sentiment analysis? Or are you looking to orchestrate generative AI across multiple processes for marketing or operations? Connecting these needs to the right Foundry solution ensures you invest in technology that delivers measurable results.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.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.
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!
Issue with: Connected Agent Tool Forcing from an Orchestrator Agent
Hi Team, I am trying to force tool selection for my Connected Agents from an Orchestrator Agent for my Multi-Agent Model. Not sure if that is possible Apologies in advance for too much detail as I really need this to work! Please let me know if there is a flaw in my approach! The main intention behind going towards Tool forcing was because with current set of instructions provided to my Orchestrator Agent, It was providing hallucinated responses from my Child Agents for each query. I have an Orchestrator Agent which is connected to the following Child Agents (Each with detailed instructions) Child Agent 1 - Connects to SQL DB in Fabric to fetch information from Log tables. Child Agent 2 - Invokes OpenAPI Action tool for Azure Functions to run pipelines in Fabric. I have provided details on 3 approaches. Approach 1: I have checked the MS docs "CONNECTED_AGENT" is a valid property for ToolChoiceType "https://learn.microsoft.com/en-us/python/api/azure-ai-agents/azure.ai.agents.models.agentsnamedtoolchoicetype?view=azure-python" Installed the latest Python AI Agents SDK Beta version as it also supports "Connected Agents": https://pypi.org/project/azure-ai-agents/1.2.0b6/#create-an-agent-using-another-agents The following code is integrated into a streamlit UI code. Python Code: agents_client = AgentsClient( endpoint=PROJECT_ENDPOINT, credential=DefaultAzureCredential( exclude_environment_credential=True, exclude_managed_identity_credential=True ) ) # ------------------------------------------------------------------- # UPDATE ORCHESTRATOR TOOLS (executed once) # ------------------------------------------------------------------- fabric_tool = ConnectedAgentTool( id=FABRIC_AGENT_ID, name="Fabric_Agent", description="Handles Fabric pipeline questions" ) openapi_tool = ConnectedAgentTool( id=OPENAPI_AGENT_ID, name="Fabric_Pipeline_Trigger", description="Handles OpenAPI pipeline triggers" ) # Update orchestrator agent to include child agent tools agents_client.update_agent( agent_id=ORCH_AGENT_ID, tools=[ fabric_tool.definitions[0], openapi_tool.definitions[0] ], instructions=""" You are the Master Orchestrator Agent. Use: - "Fabric_Agent" when the user's question includes: "Ingestion", "Trigger", "source", "Connection" - "Fabric_Pipeline_Trigger" when the question mentions: "OpenAPI", "Trigger", "API call", "Pipeline start" Only call tools when needed. Respond clearly and concisely. """ ) # ------------------------- TOOL ROUTING LOGIC ------------------------- def choose_tool(user_input: str): text = user_input.lower() if any(k in text for k in ["log", "trigger","pipeline","connection"]): return fabric_tool if any(k in text for k in ["openapi", "api call", "pipeline start"]): return openapi_tool # No forced routing → let orchestrator decide return None forced_tool = choose_tool(user_query) run = agents_client.runs.create_and_process( thread_id=st.session_state.thread.id, agent_id=ORCH_AGENT_ID, tool_choice={ "type": "connected_agent", "function": forced_tool.definitions[0] } Error: Azure.core.exceptions.HttpResponseError: (invalid_value) Invalid value: 'connected_agent'. Supported values are: 'code_interpreter', 'function', 'file_search', 'openapi', 'azure_function', 'azure_ai_search', 'bing_grounding', 'bing_custom_search', 'deep_research', 'sharepoint_grounding', 'fabric_dataagent', 'computer_use_preview', and 'image_generation'. Code: invalid_value Message: Invalid value: 'connected_agent'. Supported values are: 'code_interpreter', 'function', 'file_search', 'openapi', 'azure_function', 'azure_ai_search', 'bing_grounding', 'bing_custom_search', 'deep_research', 'sharepoint_grounding', 'fabric_dataagent', 'computer_use_preview', and 'image_generation'." Approach 2: Create ConnectedAgentTool as you do, and pass its definitions to update_agent(...). Force a tool by name using tool_choice={"type": "function", "function": {"name": "<tool-name>"}}. Do not set type: "connected_agent" anywhere—there is no such tool_choice.type. Code: from azure.identity import DefaultAzureCredential from azure.ai.agents import AgentsClient # Adjust imports to your SDK layout: # e.g., from azure.ai.agents.tool import ConnectedAgentTool agents_client = AgentsClient( endpoint=PROJECT_ENDPOINT, credential=DefaultAzureCredential( exclude_environment_credential=True, exclude_managed_identity_credential=True # keep your current credential choices ) ) # ------------------------------------------------------------------- # CREATE CONNECTED AGENT TOOLS (child agents exposed as function tools) # ------------------------------------------------------------------- fabric_tool = ConnectedAgentTool( id=FABRIC_AGENT_ID, # the **child agent ID** you created elsewhere name="Fabric_Agent", # **tool name** visible to the orchestrator description="Handles Fabric pipeline questions" ) openapi_tool = ConnectedAgentTool( id=OPENAPI_AGENT_ID, # another child agent ID name="Fabric_Pipeline_Trigger", # tool name visible to the orchestrator description="Handles OpenAPI pipeline triggers" ) # ------------------------------------------------------------------- # UPDATE ORCHESTRATOR: attach child tools # ------------------------------------------------------------------- # NOTE: definitions is usually a list of ToolDefinition objects produced by the helper agents_client.update_agent( agent_id=ORCH_AGENT_ID, tools=[ fabric_tool.definitions[0], openapi_tool.definitions[0] ], instructions=""" You are the Master Orchestrator Agent. Use: - "Fabric_Agent" when the user's question includes: "Ingestion", "Trigger", "source", "Connection" - "Fabric_Pipeline_Trigger" when the question mentions: "OpenAPI", "Trigger", "API call", "Pipeline start" Only call tools when needed. Respond clearly and concisely. """ ) # ------------------------- TOOL ROUTING LOGIC ------------------------- def choose_tool(user_input: str): text = user_input.lower() if any(k in text for k in ["log", "trigger", "pipeline", "connection"]): return "Fabric_Agent" # return the **tool name** if any(k in text for k in ["openapi", "api call", "pipeline start"]): return "Fabric_Pipeline_Trigger" # return the **tool name** return None forced_tool_name = choose_tool(user_query) # ------------------------- RUN INVOCATION ------------------------- if forced_tool_name: # FORCE a specific connected agent by **function name** run = agents_client.runs.create_and_process( thread_id=st.session_state.thread.id, agent_id=ORCH_AGENT_ID, tool_choice={ "type": "function", # <-- REQUIRED "function": { "name": forced_tool_name # <-- must match the tool's name as registered } } ) else: # Let the orchestrator auto-select (no tool_choice → "auto") run = agents_client.runs.create_and_process( thread_id=st.session_state.thread.id, agent_id=ORCH_AGENT_ID ) Error: azure.core.exceptions.HttpResponseError: (None) Invalid tool_choice: Fabric_Agent. You must also pass this tool in the 'tools' list on the Run. Code: None Message: Invalid tool_choice: Fabric_Agent. You must also pass this tool in the 'tools' list on the Run. Approach 3: Modified version of the 2nd Approach with Took Definitions call: # ------------------------- TOOL ROUTING LOGIC ------------------------- def choose_tool(user_input: str): text = user_input.lower() if any(k in text for k in ["log", "trigger","pipeline","connection"]): # return "Fabric_Agent" return ( "Fabric_Agent", fabric_tool.definitions[0] ) if any(k in text for k in ["openapi", "api call", "pipeline start"]): # return "Fabric_Pipeline_Trigger" return ( "Fabric_Pipeline_Trigger", openapi_tool.definitions[0] ) # No forced routing → let orchestrator decide # return None return (None, None) # forced_tool = choose_tool(user_query) forced_tool_name, forced_tool_def = choose_tool(user_query) # ------------------------- ORCHESTRATOR CALL ------------------------- if forced_tool_name: tool_choice = { "type": "function", "function": { "name": forced_tool_name } } run = agents_client.runs.create_and_process( thread_id=st.session_state.thread.id, agent_id=ORCH_AGENT_ID, tool_choice=tool_choice, tools=[ forced_tool_def ] # << only the specific tool ) else: # no forced tool, orchestrator decides run = agents_client.runs.create_and_process( thread_id=st.session_state.thread.id, agent_id=ORCH_AGENT_ID ) Error: TypeError: azure.ai.agents.operations._patch.RunsOperations.create() got multiple values for keyword argument 'tools'Foundry 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.
