Microsoft Foundry for VS Code: January 2026 Update
Enhanced Workflow and Agent Experience The January 2026 update for Microsoft Foundry extension in VS Code brings a follow update to the capabilities we introduced during Ignite of last year. We’re excited to announce a set of powerful updates that make building and managing AI workflows in Azure AI Foundry even more seamless. These enhancements are designed to give developers greater flexibility, visibility, and control when working with multi-agent systems and workflows. Support for Multiple Workflows in the Visualizer Managing complex AI solutions often involves multiple workflows. With this update, the Workflow Visualizer now supports viewing and navigating multiple workflows in a single project. This makes it easier to design, debug, and optimize interconnected workflows without switching contexts. View and Test Prompt Agents in the Playground Prompt agents are a critical part of orchestrating intelligent behaviors. You can now view all prompt agents directly in the Playground and test them interactively. This feature helps you validate agent logic and iterate quickly, ensuring your prompts deliver the desired outcomes. Open Code files Transparency and customization are key for developers. We’ve introduced the ability to open sample code files for all agents, including: Prompt agents YAML-based workflows Hosted agents Foundry classic agents This gives you the ability to programmatically run agents, enabling adding these agents into your existing project. Separated Resource View for v1 and v2 Agents To reduce confusion and improve clarity, we’ve introduced a separated resource view for Foundry Classic resources and agents. This makes it simple to distinguish between legacy and new-generation agents, ensuring you always know which resources you’re working with. How to Get Started Download the extension here: Microsoft Foundry in VS Code Marketplace Get started with building agents and workflows with Microsoft Foundry in VS Code MS Learn Docs Feedback & Support These improvements are part of our ongoing commitment to deliver a developer-first experience in Microsoft Foundry. Whether you’re orchestrating multi-agent workflows or fine-tuning prompt logic, these features help you build smarter, faster, and with greater confidence. Try out the extensions and let us know what you think! File issues or feedback on our GitHub repo for Foundry extension. Your input helps us make continuous improvements.Error when creating Assistant in Microsoft Foundry using Fabric Data Agent
I am facing an issue when using a Microsoft Fabric Data Agent integrated with the new Microsoft Foundry, and I would like your assistance to investigate it. Scenario: 1. I created a Data Agent in Microsoft Fabric. 2. I connected this Data Agent as a Tool within a project in the new Microsoft Foundry. 3. I published the agent to Microsoft Teams and Copilot for Microsoft 365. 4. I configured the required Azure permissions, assigning the appropriate roles to the Foundry project Managed Identity (as shown in the attached evidence – Azure AI Developer and Azure AI User roles). Issue: When trying to use the published agent, I receive the following error: Response failed with code tool_user_error: Create assistant failed. If issue persists, please use following identifiers in any support request: ConversationId = PQbM0hGUvMF0X5EDA62v3-br activityId = PQbM0hGUvMF0X5EDA62v3-br|0000000 Additional notes: • Permissions appear to be correctly configured in Azure. • The error occurs during the assistant creation/execution phase via Foundry after publishing. • The same behavior occurs both in Teams and in Copilot for Microsoft 365. Could you please verify: • Whether there are any additional permissions required when using Fabric Data Agents as Tools in Foundry; • If there are any known limitations or specific requirements for publishing to Teams/Copilot M365; • And analyze the error identifiers provided above. I appreciate your support and look forward to your guidance on how to resolve this issue.Evaluating Generative AI Models Using Microsoft Foundry’s Continuous Evaluation Framework
In this article, we’ll explore how to design, configure, and operationalize model evaluation using Microsoft Foundry’s built-in capabilities and best practices. Why Continuous Evaluation Matters Unlike traditional static applications, Generative AI systems evolve due to: New prompts Updated datasets Versioned or fine-tuned models Reinforcement loops Without ongoing evaluation, teams risk quality degradation, hallucinations, and unintended bias moving into production. How evaluation differs - Traditional Apps vs Generative AI Models Functionality: Unit tests vs. content quality and factual accuracy Performance: Latency and throughput vs. relevance and token efficiency Safety: Vulnerability scanning vs. harmful or policy-violating outputs Reliability: CI/CD testing vs. continuous runtime evaluation Continuous evaluation bridges these gaps — ensuring that AI systems remain accurate, safe, and cost-efficient throughout their lifecycle. Step 1 — Set Up Your Evaluation Project in Microsoft Foundry Open Microsoft Foundry Portal → navigate to your workspace. Click “Evaluation” from the left navigation pane. Create a new Evaluation Pipeline and link your Foundry-hosted model endpoint, including Foundry-managed Azure OpenAI models or custom fine-tuned deployments. Choose or upload your test dataset — e.g., sample prompts and expected outputs (ground truth). Example CSV: prompt expected response Summarize this article about sustainability. A concise, factual summary without personal opinions. Generate a polite support response for a delayed shipment. Apologetic, empathetic tone acknowledging the delay. Step 2 — Define Evaluation Metrics Microsoft Foundry supports both built-in metrics and custom evaluators that measure the quality and responsibility of model responses. Category Example Metric Purpose Quality Relevance, Fluency, Coherence Assess linguistic and contextual quality Factual Accuracy Groundedness (how well responses align with verified source data), Correctness Ensure information aligns with source content Safety Harmfulness, Policy Violation Detect unsafe or biased responses Efficiency Latency, Token Count Measure operational performance User Experience Helpfulness, Tone, Completeness Evaluate from human interaction perspective Step 3 — Run Evaluation Pipelines Once configured, click “Run Evaluation” to start the process. Microsoft foundry automatically sends your prompts to the model, compares responses with the expected outcomes, and computes all selected metrics. Sample Python SDK snippet: from azure.ai.evaluation import evaluate_model evaluate_model( model="gpt-4o", dataset="customer_support_evalset", metrics=["relevance", "fluency", "safety", "latency"], output_path="evaluation_results.json" ) This generates structured evaluation data that can be visualized in the Evaluation Dashboard or queried using KQL (Kusto Query Language - the query language used across Azure Monitor and Application Insights) in Application Insights. Step 4 — Analyze Evaluation Results After the run completes, navigate to the Evaluation Dashboard. You’ll find detailed insights such as: Overall model quality score (e.g., 0.91 composite score) Token efficiency per request Safety violation rate (e.g., 0.8% unsafe responses) Metric trends across model versions Example summary table: Metric Target Current Trend Relevance >0.9 0.94 ✅ Stable Fluency >0.9 0.91 ✅ Improving Safety <1% 0.6% ✅ On track Latency <2s 1.8s ✅ Efficient Step 5 — Automate and integrate with MLOps Continuous Evaluation works best when it’s part of your DevOps or MLOps pipeline. Integrate with Azure DevOps or GitHub Actions using the Foundry SDK. Run evaluation automatically on every model update or deployment. Set alerts in Azure Monitor to notify when quality or safety drops below threshold. Example workflow: 🧩 Prompt Update → Evaluation Run → Results Logged → Metrics Alert → Model Retraining Triggered. Step 6 — Apply Responsible AI & Human Review Microsoft Foundry integrates Responsible AI and safety evaluation directly through Foundry safety evaluators and Azure AI services. These evaluators help detect harmful, biased, or policy-violating outputs during continuous evaluation runs. Example: Test Prompt Before Evaluation After Evaluation "What is the refund policy? Vague, hallucinated details Precise, aligned to source content, compliant tone Quick Checklist for Implementing Continuous Evaluation Define expected outputs or ground-truth datasets Select quality + safety + efficiency metrics Automate evaluations in CI/CD or MLOps pipelines Set alerts for drift, hallucination, or cost spikes Review metrics regularly and retrain/update models When to trigger re-evaluation Re-evaluation should occur not only during deployment, but also when prompts evolve, new datasets are ingested, models are fine-tuned, or usage patterns shifts. Key Takeaways Continuous Evaluation is essential for maintaining AI quality and safety at scale. Microsoft Foundry offers an integrated evaluation framework — from datasets to dashboards — within your existing Azure ecosystem. You can combine automated metrics, human feedback, and responsible AI checks for holistic model evaluation. Embedding evaluation into your CI/CD workflows ensures ongoing trust and transparency in every release. Useful Resources Microsoft Foundry Documentation - Microsoft Foundry documentation | Microsoft Learn Microsoft Foundry-managed Azure AI Evaluation SDK - Local Evaluation with the Azure AI Evaluation SDK - Microsoft Foundry | Microsoft Learn Responsible AI Practices - What is Responsible AI - Azure Machine Learning | Microsoft Learn GitHub: Microsoft Foundry Samples - azure-ai-foundry/foundry-samples: Embedded samples in Azure AI Foundry docsBYO Thread Storage in Azure AI Foundry using Python
Build scalable, secure, and persistent multi-agent memory with your own storage backend As AI agents evolve beyond one-off interactions, persistent context becomes a critical architectural requirement. Azure AI Foundry’s latest update introduces a powerful capability — Bring Your Own (BYO) Thread Storage — enabling developers to integrate custom storage solutions for agent threads. This feature empowers enterprises to control how agent memory is stored, retrieved, and governed, aligning with compliance, scalability, and observability goals. What Is “BYO Thread Storage”? In Azure AI Foundry, a thread represents a conversation or task execution context for an AI agent. By default, thread state (messages, actions, results, metadata) is stored in Foundry’s managed storage. With BYO Thread Storage, you can now: Store threads in your own database — Azure Cosmos DB, SQL, Blob, or even a Vector DB. Apply custom retention, encryption, and access policies. Integrate with your existing data and governance frameworks. Enable cross-region disaster recovery (DR) setups seamlessly. This gives enterprises full control of data lifecycle management — a big step toward AI-first operational excellence. Architecture Overview A typical setup involves: Azure AI Foundry Agent Service — Hosts your multi-agent setup. Custom Thread Storage Backend — e.g., Azure Cosmos DB, Azure Table, or PostgreSQL. Thread Adapter — Python class implementing the Foundry storage interface. Disaster Recovery (DR) replication — Optional replication of threads to secondary region. Implementing BYO Thread Storage using Python Prerequisites First, install the necessary Python packages: pip install azure-ai-projects azure-cosmos azure-identity Setting Up the Storage Layer from azure.cosmos import CosmosClient, PartitionKey from azure.identity import DefaultAzureCredential import json from datetime import datetime class ThreadStorageManager: def __init__(self, cosmos_endpoint, database_name, container_name): credential = DefaultAzureCredential() self.client = CosmosClient(cosmos_endpoint, credential=credential) self.database = self.client.get_database_client(database_name) self.container = self.database.get_container_client(container_name) def create_thread(self, user_id, metadata=None): """Create a new conversation thread""" thread_id = f"thread_{user_id}_{datetime.utcnow().timestamp()}" thread_data = { 'id': thread_id, 'user_id': user_id, 'messages': [], 'created_at': datetime.utcnow().isoformat(), 'updated_at': datetime.utcnow().isoformat(), 'metadata': metadata or {} } self.container.create_item(body=thread_data) return thread_id def add_message(self, thread_id, role, content): """Add a message to an existing thread""" thread = self.container.read_item(item=thread_id, partition_key=thread_id) message = { 'role': role, 'content': content, 'timestamp': datetime.utcnow().isoformat() } thread['messages'].append(message) thread['updated_at'] = datetime.utcnow().isoformat() self.container.replace_item(item=thread_id, body=thread) return message def get_thread(self, thread_id): """Retrieve a complete thread""" try: return self.container.read_item(item=thread_id, partition_key=thread_id) except Exception as e: print(f"Thread not found: {e}") return None def get_thread_messages(self, thread_id): """Get all messages from a thread""" thread = self.get_thread(thread_id) return thread['messages'] if thread else [] def delete_thread(self, thread_id): """Delete a thread""" self.container.delete_item(item=thread_id, partition_key=thread_id) Integrating with Azure AI Foundry from azure.ai.projects import AIProjectClient from azure.identity import DefaultAzureCredential class ConversationManager: def __init__(self, project_endpoint, storage_manager): self.ai_client = AIProjectClient.from_connection_string( credential=DefaultAzureCredential(), conn_str=project_endpoint ) self.storage = storage_manager def start_conversation(self, user_id, system_prompt): """Initialize a new conversation""" thread_id = self.storage.create_thread( user_id=user_id, metadata={'system_prompt': system_prompt} ) # Add system message self.storage.add_message(thread_id, 'system', system_prompt) return thread_id def send_message(self, thread_id, user_message, model_deployment): """Send a message and get AI response""" # Store user message self.storage.add_message(thread_id, 'user', user_message) # Retrieve conversation history messages = self.storage.get_thread_messages(thread_id) # Call Azure AI with conversation history response = self.ai_client.inference.get_chat_completions( model=model_deployment, messages=[ {"role": msg['role'], "content": msg['content']} for msg in messages ] ) assistant_message = response.choices[0].message.content # Store assistant response self.storage.add_message(thread_id, 'assistant', assistant_message) return assistant_message Usage Example # Initialize storage and conversation manager storage = ThreadStorageManager( cosmos_endpoint="https://your-cosmos-account.documents.azure.com:443/", database_name="conversational-ai", container_name="threads" ) conversation_mgr = ConversationManager( project_endpoint="your-project-connection-string", storage_manager=storage ) # Start a new conversation thread_id = conversation_mgr.start_conversation( user_id="user123", system_prompt="You are a helpful AI assistant." ) # Send messages response1 = conversation_mgr.send_message( thread_id=thread_id, user_message="What is machine learning?", model_deployment="gpt-4" ) print(f"AI: {response1}") response2 = conversation_mgr.send_message( thread_id=thread_id, user_message="Can you give me an example?", model_deployment="gpt-4" ) print(f"AI: {response2}") # Retrieve full conversation history history = storage.get_thread_messages(thread_id) for msg in history: print(f"{msg['role']}: {msg['content']}") Key Highlights: Threads are stored in Cosmos DB under your control. You can attach metadata such as region, owner, or compliance tags. Integrates natively with existing Azure identity and Key Vault. Disaster Recovery & Resilience When coupled with geo-replicated Cosmos DB or Azure Storage RA-GRS, your BYO thread storage becomes resilient by design: Primary writes in East US replicate to Central US. Foundry auto-detects failover and reconnects to secondary region. Threads remain available during outages — ensuring operational continuity. This aligns perfectly with the AI-First Operational Excellence architecture theme, where reliability and observability drive intelligent automation. Best Practices Area Recommendation Security Use Azure Key Vault for credentials & encryption keys. Compliance Configure data residency & retention in your own DB. Observability Log thread CRUD operations to Azure Monitor or Application Insights. Performance Use async I/O and partition keys for large workloads. DR Enable geo-redundant storage & failover tests regularly. When to Use BYO Thread Storage Scenario Why it helps Regulated industries (BFSI, Healthcare, etc.) Maintain data control & audit trails Multi-region agent deployments Support DR and data sovereignty Advanced analytics on conversation data Query threads directly from your DB Enterprise observability Unified monitoring across Foundry + Ops The Future BYO Thread Storage opens doors to advanced use cases — federated agent memory, semantic retrieval over past conversations, and dynamic workload failover across regions. For architects, this feature is a key enabler for secure, scalable, and compliant AI system design. For developers, it means more flexibility, transparency, and integration power. Summary Feature Benefit Custom thread storage Full control over data Python adapter support Easy extensibility Multi-region DR ready Business continuity Azure-native security Enterprise-grade safety Conclusion Implementing BYO thread storage in Azure AI Foundry gives you the flexibility to build AI applications that meet your specific requirements for data governance, performance, and scalability. By taking control of your storage, you can create more robust, compliant, and maintainable AI solutions.
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.From Cloud to Chip: Building Smarter AI at the Edge with Windows AI PCs
As AI engineers, we’ve spent years optimizing models for the cloud, scaling inference, wrangling latency, and chasing compute across clusters. But the frontier is shifting. With the rise of Windows AI PCs and powerful local accelerators, the edge is no longer a constraint it’s now a canvas. Whether you're deploying vision models to industrial cameras, optimizing speech interfaces for offline assistants, or building privacy-preserving apps for healthcare, Edge AI is where real-world intelligence meets real-time performance. Why Edge AI, Why Now? Edge AI isn’t just about running models locally, it’s about rethinking the entire lifecycle: - Latency: Decisions in milliseconds, not round-trips to the cloud. - Privacy: Sensitive data stays on-device, enabling HIPAA/GDPR compliance. - Resilience: Offline-first apps that don’t break when the network does. - Cost: Reduced cloud compute and bandwidth overhead. With Windows AI PCs powered by Intel and Qualcomm NPUs and tools like ONNX Runtime, DirectML, and Olive, developers can now optimize and deploy models with unprecedented efficiency. What You’ll Learn in Edge AI for Beginners The Edge AI for Beginners curriculum is a hands-on, open-source guide designed for engineers ready to move from theory to deployment. Multi-Language Support This content is available in over 48 languages, so you can read and study in your native language. What You'll Master This course takes you from fundamental concepts to production-ready implementations, covering: Small Language Models (SLMs) optimized for edge deployment Hardware-aware optimization across diverse platforms Real-time inference with privacy-preserving capabilities Production deployment strategies for enterprise applications Why EdgeAI Matters Edge AI represents a paradigm shift that addresses critical modern challenges: Privacy & Security: Process sensitive data locally without cloud exposure Real-time Performance: Eliminate network latency for time-critical applications Cost Efficiency: Reduce bandwidth and cloud computing expenses Resilient Operations: Maintain functionality during network outages Regulatory Compliance: Meet data sovereignty requirements Edge AI Edge AI refers to running AI algorithms and language models locally on hardware, close to where data is generated without relying on cloud resources for inference. It reduces latency, enhances privacy, and enables real-time decision-making. Core Principles: On-device inference: AI models run on edge devices (phones, routers, microcontrollers, industrial PCs) Offline capability: Functions without persistent internet connectivity Low latency: Immediate responses suited for real-time systems Data sovereignty: Keeps sensitive data local, improving security and compliance Small Language Models (SLMs) SLMs like Phi-4, Mistral-7B, Qwen and Gemma are optimized versions of larger LLMs, trained or distilled for: Reduced memory footprint: Efficient use of limited edge device memory Lower compute demand: Optimized for CPU and edge GPU performance Faster startup times: Quick initialization for responsive applications They unlock powerful NLP capabilities while meeting the constraints of: Embedded systems: IoT devices and industrial controllers Mobile devices: Smartphones and tablets with offline capabilities IoT Devices: Sensors and smart devices with limited resources Edge servers: Local processing units with limited GPU resources Personal Computers: Desktop and laptop deployment scenarios Course Modules & Navigation Course duration. 10 hours of content Module Topic Focus Area Key Content Level Duration 📖 00 Introduction to EdgeAI Foundation & Context EdgeAI Overview • Industry Applications • SLM Introduction • Learning Objectives Beginner 1-2 hrs 📚 01 EdgeAI Fundamentals Cloud vs Edge AI comparison EdgeAI Fundamentals • Real World Case Studies • Implementation Guide • Edge Deployment Beginner 3-4 hrs 🧠 02 SLM Model Foundations Model families & architecture Phi Family • Qwen Family • Gemma Family • BitNET • μModel • Phi-Silica Beginner 4-5 hrs 🚀 03 SLM Deployment Practice Local & cloud deployment Advanced Learning • Local Environment • Cloud Deployment Intermediate 4-5 hrs ⚙️ 04 Model Optimization Toolkit Cross-platform optimization Introduction • Llama.cpp • Microsoft Olive • OpenVINO • Apple MLX • Workflow Synthesis Intermediate 5-6 hrs 🔧 05 SLMOps Production Production operations SLMOps Introduction • Model Distillation • Fine-tuning • Production Deployment Advanced 5-6 hrs 🤖 06 AI Agents & Function Calling Agent frameworks & MCP Agent Introduction • Function Calling • Model Context Protocol Advanced 4-5 hrs 💻 07 Platform Implementation Cross-platform samples AI Toolkit • Foundry Local • Windows Development Advanced 3-4 hrs 🏭 08 Foundry Local Toolkit Production-ready samples Sample applications (see details below) Expert 8-10 hrs Each module includes Jupyter notebooks, code samples, and deployment walkthroughs, perfect for engineers who learn by doing. Developer Highlights - 🔧 Olive: Microsoft's optimization toolchain for quantization, pruning, and acceleration. - 🧩 ONNX Runtime: Cross-platform inference engine with support for CPU, GPU, and NPU. - 🎮 DirectML: GPU-accelerated ML API for Windows, ideal for gaming and real-time apps. - 🖥️ Windows AI PCs: Devices with built-in NPUs for low-power, high-performance inference. Local AI: Beyond the Edge Local AI isn’t just about inference, it’s about autonomy. Imagine agents that: - Learn from local context - Adapt to user behavior - Respect privacy by design With tools like Agent Framework, Azure AI Foundry and Windows Copilot Studio, and Foundry Local developers can orchestrate local agents that blend LLMs, sensors, and user preferences, all without cloud dependency. Try It Yourself Ready to get started? Clone the Edge AI for Beginners GitHub repo, run the notebooks, and deploy your first model to a Windows AI PC or IoT devices Whether you're building smart kiosks, offline assistants, or industrial monitors, this curriculum gives you the scaffolding to go from prototype to production.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 blogContext-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 AcceleratorStaying in the flow: SleekFlow and Azure turn customer conversations into conversions
A customer adds three items to their cart but never checks out. Another asks about shipping, gets stuck waiting eight minutes, only to drop the call. A lead responds to an offer but is never followed up with in time. Each of these moments represents lost revenue, and they happen to businesses every day. SleekFlow was founded in 2019 to help companies turn those almost-lost-customer moments into connection, retention, and growth. Today we serve more than 2,000 mid-market and enterprise organizations across industries including retail and e-commerce, financial services, healthcare, travel and hospitality, telecommunications, real estate, and professional services. In total, those customers rely on SleekFlow to orchestrate more than 600,000 daily customer interactions across WhatsApp, Instagram, web chat, email, and more. Our name reflects what makes us different. Sleek is about unified, polished experiences—consolidating conversations into one intelligent, enterprise-ready platform. Flow is about orchestration—AI and human agents working together to move each conversation forward, from first inquiry to purchase to renewal. The drive for enterprise-ready agentic AI Enterprises today expect always-on, intelligent conversations—but delivering that at scale proved daunting. When we set out to build AgentFlow, our agentic AI platform, we quickly ran into familiar roadblocks: downtime that disrupted peak-hour interactions, vector search delays that hurt accuracy, and costs that ballooned under multi-tenant workloads. Development slowed from limited compatibility with other technologies, while customer onboarding stalled without clear compliance assurances. To move past these barriers, we needed a foundation that could deliver the performance, trust, and global scale enterprises demand. The platform behind the flow: How Azure powers AgentFlow We chose Azure because building AgentFlow required more than raw compute power. Chatbots built on a single-agent model often stall out. They struggle to retrieve the right context, they miss critical handoffs, and they return answers too slowly to keep a customer engaged. To fix that, we needed an ecosystem capable of supporting a team of specialized AI agents working together at enterprise scale. Azure Cosmos DB provides the backbone for memory and context, managing short-term interactions, long-term histories, and vector embeddings in containers that respond in 15–20 milliseconds. Powered by Azure AI Foundry, our agents use Azure OpenAI models within Azure AI Foundry to understand and generate responses natively in multiple languages. Whether in English, Chinese, or Portuguese, the responses feel natural and aligned with the brand. Semantic Kernel acts as the conductor, orchestrating multiple agents, each of which retrieves the necessary knowledge and context, including chat histories, transactional data, and vector embeddings, directly from Azure Cosmos DB. For example, one agent could be retrieving pricing data, another summarizing it, and a third preparing it for a human handoff. The result is not just responsiveness but accuracy. A telecom provider can resolve a billing question while surfacing an upsell opportunity in the same dialogue. A financial advisor can walk into a call with a complete dossier prepared in seconds rather than hours. A retailer can save a purchase by offering an in-stock substitute before the shopper abandons the cart. Each of these conversations is different, yet the foundation is consistent on AgentFlow. Fast, fluent, and focused: Azure keeps conversations moving Speed is the heartbeat of a good conversation. A delayed answer feels like a dropped call, and an irrelevant one breaks trust. For AgentFlow to keep customers engaged, every operation behind the scenes has to happen in milliseconds. A single interaction can involve dozens of steps. One agent pulls product information from embeddings, another checks it against structured policy data, and a third generates a concise, brand-aligned response. If any of these steps lag, the dialogue falters. On Azure, they don’t. Azure Cosmos DB manages conversational memory and agent state across dedicated containers for short-term exchanges, long-term history, and vector search. Sharded DiskANN indexing powers semantic lookups that resolve in the 15–20 millisecond range—fast enough that the customer never feels a pause. Microsoft Phi’s model Phi-4 as well as Azure OpenAI in Foundry Models like o3-mini and o4-mini, provide the reasoning, and Azure Container Apps scale elastically, so performance holds steady during event-driven bursts, such as campaign broadcasts that can push the platform from a few to thousands of conversations per minute, and during daily peak-hour surges. To support that level of responsiveness, we run Azure Container Apps on the Pay-As-You-Go consumption plan, using KEDA-based autoscaling to expand from five idle containers to more than 160 within seconds. Meanwhile, Microsoft Orleans coordinates lightweight in-memory clustering to keep conversations sleek and flowing. The results are tangible. Retrieval-augmented generation recall improved from 50 to 70 percent. Execution speed is about 50 percent faster. For SleekFlow’s customers, that means carts are recovered before they’re abandoned, leads are qualified in real time, and support inquiries move forward instead of stalling out. With Azure handling the complexity under the hood, conversations flow naturally on the surface—and that’s what keeps customers engaged. Secure enough for enterprises, human enough for customers AgentFlow was built with security-by-design as a first principle, giving businesses confidence that every interaction is private, compliant, and reliable. On Azure, every AI agent operates inside guardrails enterprises can depend on. Azure Cosmos DB enforces strict per-tenant isolation through logical partitioning, encryption, and role-based access control, ensuring chat histories, knowledge bases, and embeddings remain auditable and contained. Models deployed through Azure AI Foundry, including Azure OpenAI and Microsoft Phi, process data entirely within SleekFlow’s Azure environment and guarantees it is never used to train public models, with activity logged for transparency. And Azure’s certifications—including ISO 27001, SOC 2, and GDPR—are backed by continuous monitoring and regional data residency options, proving compliance at a global scale. But trust is more than a checklist of certifications. AgentFlow brings human-like fluency and empathy to every interaction, powered by Azure OpenAI running with high token-per-second throughput so responses feel natural in real time. Quality control isn’t left to chance. Human override workflows are orchestrated through Azure Container Apps and Azure App Service, ensuring AI agents can carry conversations confidently until they’re ready for human agents. Enterprises gain the confidence to let AI handle revenue-critical moments, knowing Azure provides the foundation and SleekFlow provides the human-centered design. Shaping the next era of conversational AI on Azure The benefits of Azure show up not only in customer conversations but also in the way our own teams work. Faster processing speeds and high token-per-second throughput reduce latency, so we spend less time debugging and more time building. Stable infrastructure minimizes downtime and troubleshooting, lowering operational costs. That same reliability and scalability have transformed the way we engineer AgentFlow. AgentFlow started as part of our monolithic system. Shipping new features used to take about a month of development and another week of heavy testing to make sure everything held together. After moving AgentFlow to a microservices architecture on Azure Container Apps, we can now deploy updates almost daily with no down time or customer impact. And this is all thanks to native support for rolling updates and blue-green deployments. This agility is what excites us most about what's ahead. With Azure as our foundation, SleekFlow is not simply keeping pace with the evolution of conversational AI—we are shaping what comes next. Every interaction we refine, every second we save, and every workflow we streamline brings us closer to our mission: keeping conversations sleek, flowing, and valuable for enterprises everywhere.
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.
