Navigating AI Solutions: Microsoft Copilot Studio vs. Azure AI Foundry
Are you looking to build custom Copilots but unsure about the differences between Copilot Studio and Azure AI Foundry? As a Microsoft Technical Trainer with over a decade of experience, I've spent the last 18 months focusing on Azure AI Solutions and Copilot. Through numerous workshops, I've seen firsthand how customers benefit from AI solutions beyond Microsoft Copilot. Microsoft 365 Copilot Chat offers seamless integration with Generative AI for tasks like document creation, content summarization, and insights from M365 solutions such as Email, OneDrive, SharePoint, and Teams. It ensures compliance with organizational security, governance, and privacy policies, making it ideal for immediate AI assistance without customization. On the other hand, platforms like Copilot Studio and Azure AI Foundry provide greater customization and flexibility, tailoring AI assistance to specific business processes, workflows, and data sources for more relevant support. In this blog, I'll share insights on building custom copilots, and the tools Microsoft offers to support this journey. Technical Insights into Two Leading AI Platforms Copilot Studio and Azure AI Foundry are two flagship platforms within the Microsoft AI ecosystem, each tailored for distinct purposes. Both are integral to the development and deployment of AI-driven solutions. Let's dive into a comprehensive comparison to explore how they differ in scope, target audience, and use cases. Target Audience Copilot Studio Copilot Studio is ideal for business users and developers looking to implement conversational AI with minimal setup. It is well-suited for industries like retail, customer service, and human resources. Azure AI Foundry Azure AI Foundry caters to software developers, data scientists, and technical decision-makers focused on building complex, scalable AI solutions. It is commonly used by enterprises in healthcare, manufacturing, and finance. Core Solution Focus Copilot Studio Copilot Studio is centered around creating and customizing conversational copilots and bots, often made available to users as ‘virtual assistants’. It emphasizes a low-code/no-code environment, making it accessible to organizations looking to integrate AI-powered assistants into their workflows, all without the need of developing and writing code. Its primary goal is to enable tailored conversational experiences through customizable plugins – offering both Microsoft and 3rd party connectors to interact with, generative AI, and integration with tools like Microsoft Teams, Power Platform, Slack, Facebook and others. Copilot Studio is accessible from https://copilotstudio.microsoft.com and can be used through different licensing options. Image 1: Copilot Studio interface with the different tabs to customize your copilot, as well as the testing pane. Azure AI Foundry Azure AI Foundry, conversely, is a robust platform designed for developing AI applications and solutions at scale. It focuses on foundational AI tools, including an extensive AI Large Language Model catalog, where the models allow fine-tuning, tracing, evaluations, and observability. Targeted at developers and data scientists, Azure AI Foundry provides access to a suite of pre-trained models, a unified SDK, and deeper integration with Azure’s cloud ecosystem. The Azure AI Foundry Management Center is available from https://ai.azure.com. While there is no specific license cost for using Azure AI Foundry, note that the different underlying Azure services such as Azure OpenAI, Azure AI Search and the LLMs will incur consumption costs. Image 2: Azure AI Foundry Management Center, allowing for model deployment, fine-tuning, AI Search indexes integration and more. Capabilities Overview Customizability Copilot Studio enables organizations to build conversational bots with extensive customization options. The best part is that users don’t need to have developer skills and can add plugins, integrate APIs, and tailor responses dynamically. For example, a retail company can create a chatbot using Copilot Studio to assist customers in real-time, pull product data from SharePoint and answer queries about pricing and availability. You could also build a virtual assistant that helps conference attendees with questions and provides info on speakers, schedule, traveling information and more. Image 3: Conference Virtual Assistant responding to a prompt about the conference agenda and offering detailed information on titles, speakers, sessions, and timings. Azure AI Foundry specializes in advanced AI capabilities like Retrieval-Augmented Generation (RAG), model benchmarking, and multi-modal integrations. For instance, Azure AI Foundry allows a healthcare organization to use generative AI models to analyze large datasets and create research summaries while ensuring data compliance and security. Image 4: Azure AI Foundry Safety + Security management options, follow Microsoft Responsible AI Framework guidelines. Ease of Use Copilot Studio is designed with simplicity in mind. Its interface supports drag-and-drop functionality, prebuilt templates, and intuitive prompt creation. Users with minimal technical expertise can quickly deploy solutions without complex coding. Azure AI Foundry, while powerful, demands higher technical proficiency. Its SDKs and APIs are tailored for experienced developers seeking granular control over AI workflows. For example, Azure AI Foundry’s model fine-tuning capabilities require understanding of machine learning, while Copilot Studio abstracts much of this complexity. Integration with Other Platforms and Tools Copilot Studio Integration Copilot Studio seamlessly integrates with Microsoft Office applications like Teams, Outlook, and OneDrive, offering conversational plugins that enhance productivity. For instance, organizations can extend Microsoft 365 Copilot with enterprise-specific scenarios, such as HR bots for employee onboarding. Image 4: For example, Copilot Studio can integrate with email and Microsoft Dynamics. Azure AI Foundry Integration Azure AI Foundry connects deeply with the Azure ecosystem, including Azure Machine Learning, Azure OpenAI Service, and Azure AI Search. Developers and AI Engineers can experiment with multiple models, deploy AI workflows, and its unified SDK supports integration into GitHub, Visual Studio, and Microsoft Fabric. It also provides integration with other AI tools such as Prompt Flow, Semantic Kernel and more. Image 5: The VSCode Prompt Flow extension can be used by developers to build and validate chat functionality, while connecting to Azure AI Foundry in the backend. Use Case Examples Real-Time Assistance with Copilot Studio An airline can use Copilot Studio to create an interactive chatbot that assists travelers with flight details, weather forecasts, and booking management. The platform’s dynamic chaining capabilities allow the bot to call multiple APIs (e.g., weather and ticketing services) and provide contextual answers seamlessly. Advanced AI Applications with Azure AI Foundry A manufacturing company can leverage Azure AI Foundry to optimize production processes. By using multi-modal models, the company can analyze visual data from factory cameras alongside operational metrics to identify inefficiencies and recommend improvements. Getting Started I hope it is becoming clearer by now, which path you could follow to start building your custom copilots. As a Learn expert, I also know that customers mostly learn best by doing. To get you started, I would personally recommend going through the following Microsoft Learn tutorials: Copilot Studio: Create and deploy an agent - This tutorial guides you through creating and deploying an agent using Copilot Studio. It covers adding knowledge to your agent, testing content changes in real-time, and deploying your agent to a test page: Link to tutorial. Building agents with generative AI - This tutorial helps you create an agent with generative AI capabilities. It provides a summary of available features and prerequisites for getting started: Link to tutorial. Create and publish agents - This module introduces key concepts for creating agents based on business scenarios that customers and employees can interact with: Link to tutorial. Azure AI Foundry: Build a basic chat app in Python - This tutorial walks you through setting up your local development environment with the Azure AI Foundry SDK, writing prompts, running app code, tracing LLM calls, and running basic evaluations: Link to tutorial. Use the chat playground - This QuickStart guides you through deploying a chat model and using it in the chat playground within the Azure AI Foundry portal: Link to tutorial. Azure AI Foundry documentation - This comprehensive documentation helps developers and organizations rapidly create intelligent applications with prebuilt and customizable APIs and models: Link to tutorial. Conclusion While Copilot Studio and Azure AI Foundry share Microsoft’s vision for democratizing AI, they are typically used by different audiences and serve distinct purposes. Copilot Studio is the go-to platform for conversational AI and low-code deployments, making it accessible for businesses and their users, aiming to enhance customer and employee interactions. Azure AI Foundry is a powerhouse for advanced AI application development, enabling organizations to leverage cutting-edge models and tools for data-driven insights and innovation, but it requires advanced development skills to build such AI-inspired applications. Choosing between Copilot Studio and Azure AI Foundry depends on the specific needs and technical expertise of the organization. If you are new to AI, a good place to start is with Copilot Studio and then to grow into a more advanced scenario with Azure AI Foundry.How Amdocs CCoE leveraged Azure AI Agent Service to build intelligent email support agent.
In this blogpost you will learn how Amdocs CCoE team improved their SLA by providing technical support for IT and cloud infrastructure questions and queries. They used Azure AI Agent Service to build an intelligent email agent that helps Amdocs employees with their technical issues. This post will describe the development phases, solution details and the roadmap ahead.From diagrams to dialogue: Introducing new multimodal functionality in Azure AI Search
Discover the new multimodal capabilities in Azure AI Search, enabling integration of text and complex image data for enhanced search experiences. With features like image verbalization, multimodal embeddings, and intuitive portal wizard configuration, developers can build AI applications that deliver comprehensive answers from both text and complex visual content. Discover how multimodal search empowers RAG apps and AI agents with improved data grounding for more accurate responses, while streamlining development pipelines.From Extraction to Insight: Evolving Azure AI Content Understanding with Reasoning and Enrichment
First introduced in public preview last year, Azure AI Content Understanding enables you to convert unstructured content—documents, audio, video, text, and images—into structured data. The service is designed to support consistent, high-quality output, directed improvements, built-in enrichment, and robust pre-processing to accelerate workflows and reduce cost. A New Chapter in Content Understanding Since our launch we’ve seen customers pushing the boundaries to go beyond simple data extraction with agentic solutions fully automating decisions. This requires more than just extracting fields. For example, a healthcare insurance provider decision to pay a claim requires cross-checking against insurance policies, applicable contracts, patient’s medical history and prescription datapoints. To do this a system needs the ability to interpret information in context, perform more complex enrichments and analysis across various data sources. Beyond field extraction, this requires a custom designed workflow leveraging reasoning. In response to this demand, Content Understanding now introduces Pro mode which enables enhanced reasoning, validation, and information aggregation capabilities. These updates allow the service to aggregate and compare results across sources, enrich extracted data with context, and deliver decisions as output. While Standard mode continues to offer reliable and scalable field extraction, Pro mode extends the service to support more complex content interpretation scenarios—enabling workflows that reflect the way people naturally reason over data. With this update, Content Understanding now solves a much larger component of your data processing workflows, offering new ways to automate, streamline, and enhance decision-making based on unstructured information. Key Benefits of Pro Mode Packed with cutting-edge reasoning capabilities, Pro mode revolutionizes document analysis. Multi-Content Input Process and aggregate information across multiple content files in a single request. Pro mode can build a unified schema from distributed data sources, enabling richer insight across documents. Multi-Step Reasoning Go beyond basic extraction with a process that supports reasoning, linking, validation, and enrichment. Knowledge Base Integration Seamlessly integrate with organizational knowledge bases and domain-specific datasets to enhance field inference. This ensures outputs can reason over the task of generating the output using the context of your business. When to Use Pro Mode Pro mode, currently limited to documents, is designed for scenarios where content understanding needs to go beyond surface-level extraction—ideal for use cases that traditionally require postprocessing, human review and decision-making based on multiple data points and contextual references. Pro mode enables intelligent processing that not only extracts data, but also validates, links, and enriches it. This is especially impactful when extracted information must be cross-referenced with external datasets or internal knowledge sources to ensure accuracy, consistency, and contextual depth. Examples include: Invoice processing that reconciles against purchase orders and contract terms Healthcare claims validation using patient records and prescription history Legal document review where clauses reference related agreements or precedents Manufacturing spec checks against internal design standards and safety guidelines By automating much of the reasoning, you can focus on higher value tasks! Pro mode helps reduce manual effort, minimize errors, and accelerate time to insight—unlocking new potential for downstream applications, including those that emulate higher-order decision-making. Simplified Pricing Model Introducing a simplified pricing structure that significantly reduces costs across all content modalities compared to previous versions, making enterprise-scale deployment more affordable and predictable. Expanded Feature Coverage We are also extending capabilities across various content types: Structured Document Outputs: Improved handling of tables spanning multiple pages, recognition of selection marks, and support for additional file types like .docx, .xlsx, .pptx, .msg, .eml, .rtf, .html, .md, and .xml. Classifier API: Automatically categorize/split and route documents to appropriate processing pipelines. Video Analysis: Extract data across an entire video or break a video into chapters automatically. Enrich metadata with face identification and descriptions that include facial images. Face API Preview: Detect, recognize, and enroll faces, enabling richer user-aware applications. Check out the details about each of these capabilities here - What's New for Content Understanding. Let's hear it from our customers Customers all over the globe are using Content Understanding for its powerful one-stop solution capabilities by leveraging advance modes of reasoning, grounding and confidence scores across diverse content types. ASC: AI-based analytics in ASC’s Recording Insights platform allows customers to move to a 100% compliance review coverage of conversations across multiple channels. ASC’s integration of Content Understanding replaces a previously complex setup—where multiple separate AI services had to be manually connected—with a single multimodal solution that delivers transcription, summarization, sentiment analysis, and data extraction in one streamlined interface. This shift not only simplifies implementation and accelerates time-to-value but also received positive customer feedback for its powerful features and the quick, hands-on support from Microsoft product teams. “With the integration of Content Understanding into the ASC Recording Insights platform, ASC was able to reduce R&D effort by 30% and achieve 5 times faster results than before. This helps ASC drive customer satisfaction and stay ahead of competition.” —Tobias Fengler, Chief Engineering Officer, ASC. To learn more about ASCs integration check out From Complexity to Simplicity: The ASC and Azure AI Partnership.” Ramp: Ramp, the all-in-one financial operations platform, is exploring how Azure AI Content Understanding can help transform receipts, bills, and multi-line invoices into structured data automatically. Ramp is leveraging the pre-built invoice template and experimenting with custom extraction capabilities across various document types. These experiments are helping Ramp evaluate how to further reduce manual entry and enhance the real-time logic that powers approvals, policy checks, and reconciliation. “Content Understanding gives us a single API to parse every receipt and statement we see—then lets our own AI reason over that data in real time. It's an efficient path from image to fully reconciled expense.” — Rahul S, Head of AI, Ramp MediaKind: MK.IO’s cloud-native video platform, available on Azure Marketplace—now integrates Azure AI Content Understanding to make it easy for developers to personalize streaming experiences. With just a few lines of code, you can turn full game footage into real-time, fan-specific highlight reels using AI-driven metadata like player actions, commentary, and key moments. “Azure AI Content Understanding gives us a new level of control and flexibility—letting us generate insights instantly, personalize streams automatically, and unlock new ways to engage and monetize. It’s video, reimagined.” —Erik Ramberg, VP, MediaKind Catch the full story from MediaKind in our breakout session at Build 2025 on May 18: My Game, My Way, where we walk you through the creation of personalized highlight reels in real-time. You’ll never look at your TV in the same way again. Getting Started For more details about the latest from Content Understanding check out Reasoning on multimodal content for efficient agentic AI app building Wednesday, May 21 at 2 PM PST Build your own Content Understanding solution in the Azure AI Foundry. Pro mode will be available in the Foundry starting June 1 st 2025 Refer to our documentation and sample code on Content Understanding Explore the video series on getting started with Content UnderstandingIntroducing Built-in AgentOps Tools in Azure AI Foundry Agent Service
A New Era of Agent Intelligence We’re thrilled to announce the public preview of Tracing, Evaluation, and Monitoring in Azure AI Foundry Agent Service, features designed to revolutionize how developers build, debug, and optimize AI agents. With detailed traces and customizable evaluators, AgentOps is here to bridge the gap between observability and performance improvement. Whether you’re managing a simple chatbot or a complex multi-agent system, this is the tool you’ve been waiting for. What Makes AgentOps Unique? AgentOps offers an unparalleled suite of functionalities that cater to the challenges AI developers face today. Here are the two cornerstone features: 1. Integrated Tracing Functionality AgentOps provides full execution tracing, offering a detailed, step-by-step breakdown of how agents process queries, interact with tools, and make decisions. By leveraging OpenTelemetry-supported traces, developers can gain insights into critical aspects of agent workflows, including: Execution Paths: Visualize an agent’s full reasoning and decision-making process across multi-agent workflows. Performance Monitoring: Track timestamps, latency, and token consumption to identify bottlenecks and optimize agent efficiency. Tool Invocation Logs: Monitor the success, failure rates, and duration of tools like file search, Grounding with Bing Search, code interpreters, OpenAPI, and more. Detailed Request/Response: Access granular logs for every interaction and activity thread, helping developers debug with precision. 2. Advanced Evaluation Framework AgentOps isn’t just about tracing; it elevates evaluation to a new level with cutting-edge features that allow developers to assess and improve agent behavior through built-in and customizable metrics. Here’s what the evaluation functionality brings to the table: Comprehensive Metrics Azure AI Foundry Agent Service enables statistical analysis of agent outputs within Agent Playground using new evaluation metrics, including: Performance Evaluators: Measure latency, token consumption, request logs, and tool invocation efficiency across each step of the agent’s activity thread. Quality Evaluators: Assess outputs for intent resolution, coherence, fluency, and accuracy, ensuring high-quality responses. Safety Evaluators: Identify risks in agent responses, such as hate speech, indirect attacks, and code vulnerabilities. 3. Monitor Azure AI Foundry Agent Service Continue to monitor and assess your system using Azure Monitor. The following Azure Monitoring Metrics are now available in the Azure Portal through Hubs and Projects, and are coming soon on Foundry Developer Platform: Type Description Dimensions IndexedFiles Number of files indexed for file search in workspace ["ErrorCode", "Status", "VectorStoreId"] Agents Number of events for AI Agents in workspace ["EventType"] Messages Number of events for AI Agent messages in workspace ["EventType", "ThreadId"] Runs Number of runs by AI Agents in workspace ["AgentId", "RunStatus", "StatusCode", "StreamType"] Threads Number of events for AI Agent threads in workspace ["EventType"] ToolCalls Tool calls made by AI Agents in workspace ["AgentId", "ToolName"] Tokens Count of tokens by AI Agents in this workspace ["AgentId", "TokenType"] These monitoring metrics enhance the visibility and operational insights needed for AI agent workflows, ensuring robust analysis and optimization. From there, continuously evaluate and monitor your agent in production with Azure AI Foundry Observability. Seamless Integration AgentOps integrates deeply into your existing workflows and tools, meeting developers where they are. With support for SDKs, portals, and third-party observability tools like Weights & Biases, you can start tracing and evaluating your agents with minimal setup. Whether you’re using Azure AI Foundry, OpenTelemetry, or custom pipelines, AgentOps in Foundry Agent Service works effortlessly across diverse AI ecosystems. Why AgentOps Matters AgentOps solves the most pressing challenges faced by AI developers today, including: Debugging Complexity: Simplify error detection and resolution with end-to-end execution visibility. Fine-Tuning Efficiency: Optimize agent performance by identifying bottlenecks and improving cost-effectiveness. Building Trust: Enhance the reliability and explainability of your agents with quality and safety evaluators. What’s Next? Explore the documentation to get started with AgentOps in Azure AI Foundry Agent Service. Evaluate your AI agents locally with Azure AI Evaluation SDK. View Monitoring data reference for metrics created for Azure AI Foundry Agent Service.Azure OpenAI Fine Tuning is Everywhere
Model customization is now in your favorite Azure OpenAI regions! We're announcing Global Training, the most accessible and affordable way to customize OpenAI models with Azure AI Foundry. We're also announcing Developer Tier, the perfect fit for evaluating the quality of your new model promoting to production.
Dynamic Tool Discovery: Azure AI Agent Service + MCP Server Integration
At the time of this writing, Azure AI Agent Service does not offer turnkey integration with Model Context Protocol (MCP) Servers. Discussed here is a solution that helps to leverage MCP's powerful capabilities while working within the Azure ecosystem. The integration approach piggybacks on the Function integration capability in the Azure AI Agent Service. By utilizing an MCP Client to discover and register tools from an MCP Server as Functions with the Agent Service, we create a seamless integration layer between the two systems. Built using the Microsoft Bot Framework, this application can be published as an AI Assistant across numerous channels like Microsoft Teams, Slack, and others. For development and testing purposes, we've used the Bot Framework Emulator to run and validate the application locally. Architecture Overview The solution architecture consists of several key components: MCP Server: Hosted in Azure Container Apps, the MCP Server connects to Azure Blob Storage using Managed Identity, providing secure, token-based authentication without the need for stored credentials. Azure AI Agent Service: The core intelligence platform that powers our AI Assistant. It leverages various tools including: Native Bing Search tool for retrieving news content Dynamically registered MCP tools for storage operations GPT-4o model for natural language understanding and generation Custom AI Assistant Application: Built with the Microsoft Bot Framework, this application runs locally during development but could be hosted in Azure Container Apps for production use. It serves as the bridge between user interactions and the Azure AI Agent Service. Integration Layer: The MCP client within our application discovers available tools from the MCP Server and registers them with the Azure AI Agent Service, enabling seamless function calling between these systems. Technical Implementation MCP Tool Discovery and Registration The core of our integration lies in how we discover MCP tools and register them with the Azure AI Agent Service. Let's explore the key components of this process. Tool Discovery Process The agent.py file contains the logic for connecting to the MCP Server, discovering available tools, and registering them with the Azure AI Agent Service: # Fetch tool schemas from MCP Server async def fetch_tools(): conn = ServerConnection(mcp_server_url) await conn.connect() tools = await conn.list_tools() await conn.cleanup() return tools tools = asyncio.run(fetch_tools()) # Build a function for each tool def make_tool_func(tool_name): def tool_func(**kwargs): async def call_tool(): conn = ServerConnection(mcp_server_url) await conn.connect() result = await conn.execute_tool(tool_name, kwargs) await conn.cleanup() return result return asyncio.run(call_tool()) tool_func.__name__ = tool_name return tool_func functions_dict = {tool["name"]: make_tool_func(tool["name"]) for tool in tools} mcp_function_tool = FunctionTool(functions=list(functions_dict.values())) This approach dynamically creates Python function stubs for each MCP tool, which can then be registered with the Azure AI Agent Service. Agent Creation and Registration Once we have our function stubs, we register them with the Azure AI Agent Service: # Initialize agent with tools toolset = ToolSet() toolset.add(mcp_function_tool) toolset.add(bing) # Adding the Bing Search tool # Create or update agent with the toolset agent = project_client.agents.create_agent( model=config.aoai_model_name, name=agent_name, instructions=agent_instructions, tools=toolset.definitions ) The advantage with this approach is that it allows for dynamic discovery and registration. When the MCP Server adds or updates tools, you can simply run the agent creation process again to update the registered functions. The picture below shows the tool actions discovered from the MCP Server are registered as Functions in the AI Agent Service upon Agent creation/updation. Executing Requests Using the MCP Client When a user interacts with the bot, the state_management_bot.py handles the function calls and routes them to the appropriate handler: # Process each tool call tool_outputs = [] for tool_call in tool_calls: if isinstance(tool_call, RequiredFunctionToolCall): # Get function name and arguments function_name = tool_call.function.name args_json = tool_call.function.arguments arguments = json.loads(args_json) if args_json else {} # Check if this is an MCP function if is_mcp_function(function_name): # Direct MCP execution using our specialized handler output = await execute_mcp_tool_async(function_name, arguments) else: # Use FunctionTool as fallback output = functions.execute(tool_call) The system is designed to be loosely coupled - the Agent only knows about the tool signatures and how to call them, while the MCP Server handles the implementation details of interacting with Azure Storage. Running the Application The application workflow consists of two main steps: 1. Creating/Updating the Agent This step discovers available tools from the MCP Server and registers them with the Azure AI Agent Service: python agent.py This process: Connects to the MCP Server Retrieves the schema of all available tools Creates function stubs for each tool Registers these stubs with the Azure AI Agent Service 2. Running the AI Assistant Once the agent is configured with the appropriate tools, you can run the application: python app.py Users interact with the AI Assistant through the Bot Framework Emulator using natural language. The assistant can: Search for news using Bing Search Summarize the findings Store and organize summaries in Azure Blob Storage via the MCP Server References Here is the GitHub Repo for this App. It has references to relevant documentation on the subject Here is the GitHub Repo of the MCP Server Here is a video demonstrating this Application in action Conclusion This implementation demonstrates a practical approach to integrating Azure AI Agent Service with MCP Servers. By leveraging the Function integration capability, we've created a bridge that allows these technologies to work together seamlessly. The architecture is: Flexible: New tools can be added to the MCP Server and automatically discovered Maintainable: Changes to storage operations can be made without modifying the agent Scalable: Additional capabilities can be easily added through new MCP tools As Azure AI Agent Service evolves, we may see native integration with MCP Servers in the future. Until then, this approach provides a robust solution for developers looking to combine these powerful technologies.
