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 blogThe Future of AI: Harnessing AI agents for Customer Engagements
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The Future of AI: Developing Code Assist – a Multi-Agent Tool
Discover how Code Assist, created with Azure AI Foundry Agent Service, uses AI agents to automate code documentation, generate business-ready slides, and detect security risks in large codebases—boosting developer productivity and project clarity.The Future of AI: Computer Use Agents Have Arrived
Discover the groundbreaking advancements in AI with Computer Use Agents (CUAs). In this blog, Marco Casalaina shares how to use the Responses API from Azure OpenAI Service, showcasing how CUAs can launch apps, navigate websites, and reason through tasks. Learn how CUAs utilize multimodal models for computer vision and AI frameworks to enhance automation. Explore the differences between CUAs and traditional Robotic Process Automation (RPA), and understand how CUAs can complement RPA systems. Dive into the future of automation and see how CUAs are set to revolutionize the way we interact with technology.The Future of AI: Unleashing the Potential of AI Translation
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The blog post Customizing AI agents with the Semantic Kernel agent framework discusses the capabilities of the Semantic Kernel SDK, an open-source tool developed by Microsoft for creating AI agents and multi-agent systems. It highlights the benefits of using single-purpose agents within a multi-agent system to achieve more complex workflows with improved efficiency. The Semantic Kernel SDK offers features like telemetry, hooks, and filters to ensure secure and responsible AI solutions, making it a versatile tool for both simple and complex AI projects.The Future of AI: Power Your Agents with Azure Logic Apps
Building intelligent applications no longer requires complex coding. With advancements in technology, you can now create agents using cloud-based tools to automate workflows, connect to various services, and integrate business processes across hybrid environments without writing any code.Use generative AI to extract structured data out of emails
One thing we regularly hear from clients is that they receive information that are key to their business such as order requests via email in an unstructured format and sometimes there are structured information within the body of those emails in a variety of table formats. In today’s fast-paced digital world, businesses need a way to automatically extract, structure, and integrate this information into their existing applications. Whether it’s leveraging AI-powered document processing, natural language processing (NLP), or intelligent automation, the right approach can transform email-based orders into structured, actionable data. In this blog, we’ll explore one such scenario where AI can be leveraged to extract information in tabular format that has been provided within an email. The emails contextually belong to a specific domain, but the tables are not with consistent headers or shapes. Sometimes in the body of one email there could be multiple tables. The problem Statement Extract tabular information with varying table formats from emails The typical approach to this problem involves rule-based processing, where individual tables are extracted and merged based on predefined logic. However, given the variety of email formats from hundreds or even thousands of different senders, maintaining such rule-based logic becomes increasingly complex and difficult to manage. A more optimal solution is leveraging the cognitive capabilities of generative AI, which can dynamically adapt to different table structures, column names, and formatting variations—eliminating the need for constant rule updates while improving accuracy and scalability. To create this sample code, I used below email with test data, with two tables with inconsistent column names. It is going to provide some upcoming trainings information. Please note the difference between the column headers: Hi there, Regarding the upcoming trainings, this is the list: Event Date Description of Event Length Grade 2025-01-21 Digital environments 20 hours 5 2025-03-01 AI for Industry A 10 hours 3 and some further events in below list Date Subject Duration Grade 2025-01-21 Digital environments 2 2 days 1 2025-03-01 AI for Industry B 2 weeks 4 These sessions are designed to be interactive and informative, so your timely participation is crucial. Please make sure to log in or arrive on time to avoid missing key insights. If you have any questions or need assistance, feel free to reach out. Looking forward to seeing you there! Thanks, Azadeh These are the two tables within the email, and we need to extract one consistent table format with all the rows from these two tables. Table 1 Event Date Description of Event Length Grade 2025-01-21 Digital environments 20 hours 5 2025-03-01 AI for Industry A 10 hours 3 Table 2 Date Subject Duration Grade 2025-01-21 Digital environments 2 2 days 1 2025-03-01 AI for Industry B 2 weeks 4 To extract the tabular data into one single table in json format, I am using python with below libraries installed in my environment: pandas beautifulsoup4 openai lxml The Code I use azure OpenAI service with a gpt 4o deployment. Below code is just one way of solving this type of problem and can be customized or improved to fit to other similar problems. I have provided some guidelines about merging the tables and column names similarity in the user prompt. This sample code is using an email message that is saved in 'eml' format in a local path, but the email library has other capabilities to help you connect to a mailbox and get the emails. import email import pandas as pd from bs4 import BeautifulSoup import os from openai import AzureOpenAI endpoint = os.getenv("ENDPOINT_URL", "https://....myendpointurl....openai.azure.com/") deployment = os.getenv("DEPLOYMENT_NAME", "gpt-4o") subscription_key = os.getenv("AZURE_OPENAI_API_KEY", "myapikey) # Initialize Azure OpenAI Service client with key-based authentication client = AzureOpenAI( azure_endpoint=endpoint, api_key=subscription_key, api_version="2024-05-01-preview", ) # Process email content with GPT-4 def extract_information(email_body, client): soup = BeautifulSoup(email_body, "html.parser") body = soup.get_text() print(body) #Prepare the chat prompt chat_prompt = [ { "role": "system", "content": [ { "type": "text", "text": "You are an AI assistant that is expert in extracting structured data from emails." } ] }, { "role": "user", "content": [ { "type": "text", "text": f"Extract the required information from the following email and format it as JSON and consolidate the tables using the common column names. For example the columns length and duration are the same and the columns Event and Subject are the same:\n\n{body}" } ] } ] messages = chat_prompt # Generate the completion completion = client.chat.completions.create( model=deployment, messages=messages, max_tokens=800, temperature=0.1, top_p=0.95, frequency_penalty=0, presence_penalty=0, stop=None, stream=False ) return completion.choices[0].message.content email_file_name = r'...path to your file....\Test Email with Tables.eml' with open(email_file_name, "r") as f: msg = email.message_from_file(f) email_body = "" for part in msg.walk(): if part.get_content_type() == "text/plain": email_body = part.get_payload(decode=True).decode() elif part.get_content_type() == "text/html": email_body = part.get_payload(decode=True).decode() extracted_info = extract_information(email_body, client) print(extracted_info) The output is: ``` [ { "Event": "Digital environments", "Date": "2025-01-21", "Length": "20 hours", "Grade": 5 }, { "Event": "AI for Industry A", "Date": "2025-03-01", "Length": "10 hours", "Grade": 3 }, { "Event": "Digital environments 2", "Date": "2025-01-21", "Length": "2 days", "Grade": 1 }, { "Event": "AI for Industry B", "Date": "2025-03-01", "Length": "2 weeks", "Grade": 4 } ] ``` Key points in the code: Read an email and extract the body Use a gen AI model with the right instructions prompt to complete the task Gen AI will follow the instructions and create a combined consistent table Get the output in the right format, e.g. 'json' I hope you find this blog post helpful, and you can apply it to your use case/domain. Or you can simply get the idea of how to use generative AI to solve a problem, instead of building layers of custom logic.
