Azure AI Connect - March 2 to March 6 2026
The Future of AI is Connected. The Future is on Azure. Join us for a 5-day virtual event dedicated to mastering the Microsoft Azure AI platform. Azure AI Connect isn't just another virtual conference. It's a 5-day deep-dive immersion into the *connective tissue* of artificial intelligence on the cloud. We're bringing together developers, data scientists, and enterprise leaders to explore the full spectrum of Azure AI services—from Cognitive Services and Machine Learning to the latest breakthroughs in Generative AI. Explore the Ecosystem: Understand how services work *together* to create powerful, end-to-end solutions. Learn from Experts: Get direct insights from Microsoft MVPs, product teams, and industry pioneers. Gain Practical Skills: Move beyond theory with code-driven sessions, practical workshops, and live Q&As. Connect with Peers: Network with a global community in our virtual lounge. Event Details
Missing equivalent for Python MemorySearchTool and AgentMemorySettings in C# SDK
Hi Team, I am currently working with the Azure AI Foundry Agent Service (preview). I’ve been reviewing the documentation for managed long-term memory, specifically the "Automatic User Memory" features demonstrated in the Python SDK here: https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/memory-usage?view=foundry&tabs=python. In Python, it is very straightforward to attach a MemorySearchTool to an agent and use AgentMemorySettings(scope="user_123") during a run. This allows the service to automatically extract, consolidate, and retrieve memories without manual intervention. However, in the https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Projects#memory-store-operations, I only see the low-level MemoryStoreClient which appears to require manual CRUD operations on memory items. My Questions: Is there an equivalent high-level AgentMemorySearchTool or similar abstraction in the current C# NuGet package (Azure.AI.Projects) that handles automatic extraction and retrieval? If not currently available, is this feature on the immediate roadmap for the .NET SDK? Are there any samples showing how to achieve "automatic" memory (where the Agent extracts facts itself) using the C# SDK without having to build a custom orchestration layer or call REST APIs directly? Any guidance on the timeline for feature parity between the Python and .NET SDKs regarding Agent Memory would be greatly appreciated. SDK Version: Azure.AI.Projects 1.2.0-beta.5How We Built an AI Operations Agent Using MCP Servers and Dynamic Tool Routing
Modern operations teams are turning to AI Agents to resolve shipping delays faster and more accurately. In this article, we build a “two‑brain” AI Agent on Azure—one MCP server that reads policies from Blob Storage and another that updates order data in Azure SQL—to automate decisions like hazardous‑material handling and delivery prioritization. You’ll see how these coordinated capabilities transform a simple user query into a fully automated operational workflowHow to Set Up Claude Code with Microsoft Foundry Models on macOS
Introduction Building with AI isn't just about picking a smart model. It is about where that model lives. I chose to route my Claude Code setup through Microsoft Foundry because I needed more than just a raw API. I wanted the reliability, compliance, and structured management that comes with Microsoft's ecosystem. When you are moving from a prototype to something real, having that level of infrastructure backing your calls makes a significant difference. The challenge is that Foundry is designed for enterprise cloud environments, while my daily development work happens locally on a MacBook. Getting the two to communicate seamlessly involved navigating a maze of shell configurations and environment variables that weren't immediately obvious. I wrote this guide to document the exact steps for bridging that gap. Here is how you can set up Claude Code to run locally on macOS while leveraging the stability of models deployed on Microsoft Foundry. Requirements Before we open the terminal, let's make sure you have the necessary accounts and environments ready. Since we are bridging a local CLI with an enterprise cloud setup, having these credentials handy now will save you time later. Azure Subscription with Microsoft Foundry Setup - This is the most critical piece. You need an active Azure subscription where the Microsoft Foundry environment is initialized. Ensure that you have deployed the Claude model you intend to use and that the deployment status is active. You will need the specific endpoint URL and the associated API keys from this deployment to configure the connection. An Anthropic User Account - Even though the compute is happening on Azure, the interface requires an Anthropic account. You will need this to authenticate your session and manage your user profile settings within the Claude Code ecosystem. Claude Code Client on macOS - We will be running the commands locally, so you need the Claude Code CLI installed on your MacBook. Step 1: Install Claude Code on macOS The recommended installation method is via Homebrew or Curl, which sets it up for terminal access ("OS level"). Option A: Homebrew (Recommended) brew install --cask claude-code Option B: Curl curl -fsSL https://claude.ai/install.sh | bash Verify Installation: Run claude --version. Step 2: Set Up Microsoft Foundry to deploy Claude model Navigate to your Microsoft Foundry portal, and find the Claude model catalog, and deploy the selected Claude model. [Microsoft Foundry > My Assets > Models + endpoints > + Deploy Model > Deploy Base model > Search for "Claude"] In your Model Deployment dashboard, go to the deployed Claude Models and get the "Endpoints and keys". Store it somewhere safe, because we will need them to configure Claude Code later on. Configure Environment Variables in MacOS terminal: Now we need to tell your local Claude Code client to route requests through Microsoft Foundry instead of the default Anthropic endpoints. This is handled by setting specific environment variables that act as a bridge between your local machine and your Azure resources. You could run these commands manually every time you open a terminal, but it is much more efficient to save them permanently in your shell profile. For most modern macOS users, this file is .zshrc. Open your terminal and add the following lines to your profile, making sure to replace the placeholder text with your actual Azure credentials: export CLAUDE_CODE_USE_FOUNDRY=1 export ANTHROPIC_FOUNDRY_API_KEY="your-azure-api-key" export ANTHROPIC_FOUNDRY_RESOURCE="your-resource-name" # Specify the deployment name for Opus export CLAUDE_CODE_MODEL="your-opus-deployment-name" Once you have added these variables, you need to reload your shell configuration for the changes to take effect. Run the source command below to update your current session, and then verify the setup by launching Claude: source ~/.zshrc claude If everything is configured correctly, the Claude CLI will initialize using your Microsoft Foundry deployment as the backend. Once you execute the claude command, the CLI will prompt you to choose an authentication method. Select Option 2 (Antrophic Console account) to proceed. This action triggers your default web browser and redirects you to the Claude Console. Simply sign in using your standard Anthropic account credentials. After you have successfully signed in, you will be presented with a permissions screen. Click the Authorize button to link your web session back to your local terminal. Return to your terminal window, and you should see a notification confirming that the login process is complete. Press Enter to finalize the setup. You are now fully connected. You can start using Claude Code locally, powered entirely by the model deployment running in your Microsoft Foundry environment. Conclusion Setting up this environment might seem like a heavy lift just to run a CLI tool, but the payoff is significant. You now have a workflow that combines the immediate feedback of local development with the security and infrastructure benefits of Microsoft Foundry. One of the most practical upgrades is the removal of standard usage caps. You are no longer limited to the 5-hour API call limits, which gives you the freedom to iterate, test, and debug for as long as your project requires without hitting a wall. By bridging your local macOS terminal to Azure, you are no longer just hitting an API endpoint. You are leveraging a managed, compliance-ready environment that scales with your needs. The best part is that now the configuration is locked in, you don't need to think about the plumbing again. You can focus entirely on coding, knowing that the reliability of an enterprise platform is running quietly in the background supporting every command.
Cannot connect Azure OpenAI Embeddings model to SQL Server 2025
On SQL Server 2025, I am trying to vectorize a table. To set up the ability for SQL Server 2025 to communicate with Azure OpenAI embeddings model, I first created a master key for encryption. CREATE MASTER KEY ENCRYPTION BY PASSWORD = 'Secret'; GO Then I set up a database scoped credential. CREATE DATABASE SCOPED CREDENTIAL [MyAzureOpenAICredential] WITH IDENTITY = 'HTTPEndpointHeaders', SECRET = '{"api-key":"secret"}'; Then I created an external model. CREATE EXTERNAL MODEL AzureOpenAIEmbeddingsModel WITH ( LOCATION = 'https://{secret}-eastus2.cognitiveservices.azure.com/openai/deployments/text-embedding-3-small/embeddings?api-version=2023-05-15', API_FORMAT = 'Azure OpenAI', MODEL_TYPE = EMBEDDINGS, MODEL = 'text-embedding-3-small', CREDENTIAL = [MyAzureOpenAICredential] ); However, when I run this simple script: DECLARE @text NVARCHAR(MAX) = N'SQL Server 2025 enables AI-powered applications'; DECLARE @embedding VECTOR(1536) = AI_GENERATE_EMBEDDINGS(@text USE MODEL AzureOpenAIEmbeddingsModel); I get this error. The database scoped credential 'MyAzureOpenAICredential' cannot be used to invoke an external rest endpoint. I have read through https://learn.microsoft.com/en-us/training/modules/build-ai-solutions-sql-server/4-integrate-ai-models pertaining to this task. As well as SQL Server 2025 docs for creating a model. I have also read SQL Server 2025 docs for creating https://learn.microsoft.com/en-us/sql/t-sql/statements/create-database-scoped-credential-transact-sql?view=sql-server-ver17. I have not found any answers.SubgenAI makes AI practical, scalable, and sustainable with Azure Database for PostgreSQL
Authors: Abe Omorogbe, Senior Program Manager at Microsoft and Julia Schröder Langhaeuser, VP of Product Serenity Star at SubgenAI AI agents are thriving in pilots and prototypes. However, scaling them across organizations is more difficult. A recent MIT report shows that 95 percent of projects fail to reach production. Long development cycles, lack of observability, and compliance hurdles leave enterprises struggling to deliver production-ready agents. SubgenAI, a European generative AI company that focuses on democratizing AI for businesses and governments, saw an opportunity to change this. Its flagship platform, Serenity Star, transforms AI agent development from a code-heavy, fragmented process into a streamlined, no-code experience. Built on Microsoft Azure Database for PostgreSQL, Semantic Kernel, and Microsoft Foundry, Serenity Star empowers organizations to deploy production-grade AI agents in minutes, not months. SubgenAI’s mission is to make generative AI accessible, scalable, and secure for every organization. Whether you're a startup or a multinational, Serenity Star offers the tools to build intelligent agents tailored to your business logic, with full control over data and deployment. “Many things must happen around it in the coming years. Serenity Star is designed to solve problems like data control, compliance, and decision ethics—so companies can unleash the full potential of generative AI without compromising trust or profitability” - Lorenzo Serratosa Simplifying complex AI agent development Technical and operational challenges are inherent in enterprise-wide AI agent deployments. Examples include time-consuming iteration cycles, lack of observability and cost control, security concerns, and data sovereignty requirements. Serenity Star addresses these pain points by handling the entire AI agent lifecycle while providing enterprise-grade security and compliance features. Users can focus on defining their agent's purpose and behavior rather than wrestling with technical implementation details. Its framework focuses on four essentials for AI agents: the brain (underlying model), knowledge (accessible information), behavior (programmed responses), and tools (external system integrations). This framework directly influenced the technology stack choices for Serenity Star, with Azure Database for PostgreSQL powering the knowledge retrieval and Semantic Kernel enabling flexible model orchestration. Real-world architecture in action When a user query comes in, Serenity Star uses the vector capabilities of Azure Database for PostgreSQL to retrieve the most relevant knowledge. That context, combined with the user’s input, forms a complete prompt. Semantic Kernel then routes the request to the right large language model, ensuring the agent delivers accurate and context-aware responses. Serenity Star’s native connectors to platforms such as Microsoft Teams, WhatsApp, and Google Tag Manager are also part of this architecture, delivering answers directly in the collaboration and communication tools enterprises already use every day. Figure 1: Serenity Star Architecture This routing and orchestration architecture applies to the multi-tenant SaaS deployments and dedicated customer instances offered by Serenity Star. Azure Database for PostgreSQL provides native Row-Level Security (RLS) capabilities, a key advantage for securely managing multi-tenant environments. Multi-tenant deployments allow organizations to get started quickly with lower overhead, while dedicated instances meet the needs of enterprises with strict compliance and data sovereignty requirements. Optimizing for scale The same architecture that powers retrieval, routing, and multi-channel delivery also provides a foundation for performance at scale. As adoption grows, the team continuously monitors query volume, response times, and resource efficiency across both multi-tenant and dedicated environments. To stay ahead of demand, SubgenAI actively experiments with new Azure Database for PostgreSQL features such as DiskANN for faster vector search. These optimizations keep latency low even as more users and connectors are added. The result is a platform that maintains sub-60-second response times for 99 percent of chart generations, regardless of deployment model or integration point. With this systematic approach to scaling, organizations can deploy fully functional AI agents that are connected to their preferred communication platforms in just 15 minutes instead of hours. For enterprises that have struggled with failed AI projects, Serenity Star offers not only a secure and compliant solution but also one proven to grow with their needs. Why Azure Database for PostgreSQL is a cornerstone The knowledge component of AI agents relies heavily on retrieval-augmented generation (RAG) systems that perform similarity searches against embedded content. This requires a database capable of handling efficient vector search while maintaining enterprise-grade reliability and security. SubgenAI evaluated multiple vector database options. However, Azure Database for PostgreSQL with PGVector emerged as the clear winner. There were several compelling reasons for this. One is its mature technology, which provides immediate credibility with enterprise customers. Two, the ability to scale GenAI use cases with features like DiskANN for accurate and scalable vector search. There, the flexibility and appeal of using an open-source database with a vibrant and fast-moving community. As CPO Leandro Harillo explains: “When we tell them their data runs on Azure Database for PostgreSQL, it’s a relief. It's a well-known technology versus other options that were born with this new AI revolution.” As an open-source relational database management system, Azure Database for PostgreSQL offers extensibility and seamless integration with Microsoft’s enterprise ecosystem. It has a trusted reputation that appeals to organizations with strict data sovereignty and compliance requirements such as those in healthcare and insurance where reliability and governance are non-negotiable. The integration with Azure's broader ecosystem also simplified implementation. With Serenity Star built entirely on Azure infrastructure, Azure Database for PostgreSQL provided seamless connectivity and consistent performance characteristics. The fast response times necessary for real-time agent interactions are the result, along with maintaining the reliability demanded by enterprise customers. Semantic Kernel: Enabling model flexibility at scale Enterprise AI success requires the ability to experiment with different models and adapt quickly as technology evolves. Semantic Kernel makes this possible, supporting over 300 LLMs and embedding models through a unified interface. With Serenity Star, organizations can make genuine choices about their AI implementations without vendor lock-in. Companies can use embedding models from OpenAI through Azure deployments, ensuring their information remains in their own infrastructure while accessing cutting-edge capabilities. If business requirements change or new models emerge, switching becomes a configuration change rather than a development project. Semantic Kernel's comprehensive connector ecosystem also accelerated SubgenAI's own development process. Interfaces for different vector databases enabled rapid prototyping and comparison during the evaluation phase. “Semantic Kernel helped us to be able to try the different ones and choose the one that fit better for us,” notes Julia Schroder, VP of Product. The SubgenAI team has also extended Semantic Kernel to support more features in Azure Database for PostgreSQL, which is easier because of how well-known and popular PostgreSQL is. SubgenAI has also contributed improvements back to the community. This collaborative approach ensures the platform benefits from the latest developments while helping advance the broader ecosystem. Proven impact of Azure Database for PostgreSQL across industries Because organizations struggle to deliver production-ready agents because of long development cycles, lack of observability, and compliance, the effectiveness of Azure Database for PostgreSQL and other Azure services is reflected in deployment metrics and customer feedback. Production-ready agents typically require around 30 iterations for basic implementations. Complex use cases demand significantly more refinement. One GenAI customer in medical education required over 200 iterations to perfect an agent that evaluates medical students through complex case analysis. Azure PostgreSQL and other Azure services support hour-long iteration cycles rather than week-long sprints, which made this level of refinement economically feasible. Cost efficiency is another significant advantage. SubgenAI provisions and configures models in Microsoft Foundry, which eliminates idling GPU resources while providing detailed cost breakdowns. Users can see exactly how tokens are consumed across prompt text, RAG context, and tool usage, enabling data-driven optimization decisions. Consulting partnerships validate the platform's market position. One consulting firm with 50,000 employees is delighted with the easier implementation, faster deployment, and reliable production performance. Conclusion The combination of Azure Database for PostgreSQL and Semantic Kernel has enabled SubgenAI to address the fundamental challenges that cause 95 percent of enterprise AI projects to fail. Organizations using Serenity Star bypass the traditional barriers of lengthy development cycles, limited observability, and compliance hurdles that typically derail AI initiatives. The platform's architecture delivers measurable results, including a 50 percent reduction in coding time, support for complex agents requiring 200+ iterations, and deployment capabilities that compress months-long projects into 15-minute implementations. Azure Database for PostgreSQL provides the enterprise-grade foundation that customers in regulated industries require, while Semantic Kernel ensures organizations retain flexibility as AI technology evolves. This technological partnership creates a reliable pathway for companies to deploy production-ready AI agents without sacrificing data sovereignty or operational control. Through the reliability of Azure Database for PostgreSQL and the flexibility of Semantic Kernel, Serenity Star delivers an enterprise-ready foundation that makes AI practical, scalable, and sustainable.
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.Building an Agentic, AI-Powered Helpdesk with Agents Framework, Azure, and Microsoft 365
The article describes how to build an agentic, AI-powered helpdesk using Azure, Microsoft 365, and the Microsoft Agent Framework. The goal is to automate ticket handling, enrich requests with AI, and integrate seamlessly with M365 tools like Teams, Planner, and Power Automate.
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'
