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: Autonomous Agents for Identifying the Root Cause of Cloud Service Incidents
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Have you ever wondered how meteorologists predict tomorrow's weather, or how businesses anticipate future sales? These predictions rely on analyzing patterns over time, known as time series forecasts. With advancements in artificial intelligence, forecasting the future has become more accurate and accessible than ever before. Understanding Time Series Forecasting Time series data is a collection of observations recorded at specific time intervals. Examples include daily temperatures, monthly sales figures, or hourly website visitors. By examining this data, we can identify trends and patterns that help us predict future events. Forecasting involves using mathematical models to analyze past data and make informed guesses about what comes next. Traditional Forecasting Methods: ARIMA and Prophet Two of the most popular traditional methods for doing time series forecasting are ARIMA and Prophet. ARIMA, which stands for AutoRegressive Integrated Moving Average, predicts future values based on past data. It involves making the data stationary by removing trends and seasonal effects, then applying statistical techniques. However, ARIMA requires manual setup of parameters like trends and seasonality, which can be complex and time-consuming. It's best suited for simple, one-variable data with minimal seasonal changes. Prophet, a forecasting tool developed by Facebook (now Meta), automatically detects trends, seasonality, and holiday effects in the data, making it more user-friendly than ARIMA. Prophet works well with data that has strong seasonal patterns and doesn't need as much historical data. However, it may struggle with more complex patterns or irregular time intervals. Introducing Nixtla TimeGEN-1: A New Era in Forecasting Nixtla TimeGEN-1 represents a significant advancement in time series forecasting. Unlike traditional models, TimeGEN-1 is a generative pretrained transformer model, much like the GPT models, but rather than working with language, it's specifically designed for time series data. It has been trained on over 100 billion data points from various fields such as finance, weather, energy, and web data. This extensive training allows TimeGEN-1 to handle a wide range of data types and patterns. One of the standout features of TimeGEN-1 is its ability to perform zero-shot inference. This means it can make accurate predictions on new datasets without needing additional training. It can also be fine-tuned on specific datasets for even better accuracy. TimeGEN-1 handles irregular data effortlessly, working with missing timestamps or uneven intervals. Importantly, it doesn't require users to manually specify trends or seasonal components, making it accessible even to those without deep technical expertise. The transformer architecture of TimeGEN-1 enables it to capture complex patterns in data that traditional models might miss. It brings the power of advanced machine learning to time series forecasting – and related tasks like anomaly detection – making the process more efficient and accurate. Real-World Comparison: TimeGEN-1 vs. ARIMA and Prophet To test these claims, I decided to run an experiment to compare the performance of TimeGEN-1 with ARIMA and Prophet. I used a retail dataset where the actual future values were known, which in data science parlance is known as a "backtest." In my dataset, ARIMA struggled to predict future values accurately due to its limitations with complex patterns. Prophet performed better than ARIMA by automatically detecting some patterns, but its predictions still didn't quite hit the mark. TimeGEN-1, however, delivered predictions that closely matched the actual data, significantly outperforming both ARIMA and Prophet. The accuracy of these models was measured using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). TimeGEN-1 had the lowest MAE and RMSE, indicating higher accuracy. This experiment highlights how TimeGEN-1 can provide more precise forecasts, even when compared to established methods. The Team Behind TimeGEN-1: Nixtla Nixtla is a company dedicated to making advanced predictive insights accessible to everyone. It was founded by a team of experts passionate about simplifying forecasting processes while maintaining high accuracy and efficiency. The team includes Max Mergenthaler Canseco, CEO; Azul Garza, CTO; and Cristian Challu, CSO, experts in the forecasting field with extensive experience in machine learning and software engineering.< Their collective goal is to simplify the forecasting process, making powerful tools available to users with varying levels of technical expertise. By integrating TimeGEN-1 into easy-to-use APIs, they ensure that businesses and individuals can leverage advanced forecasting without needing deep machine learning knowledge. The Azure AI Model Catalog TimeGEN-1 is one of the 1700+ models that are now available in the Azure AI model catalog. The model catalog is continuously updated with the latest advancements, like TimeGEN-1, ensuring that users have access to the most cutting-edge tools. Its user-friendly interface makes it easy to navigate and deploy models, and Azure's cloud infrastructure provides the scalability needed to run these models, allowing users to handle large datasets and complex computations efficiently. In the following video, I show how Data Scientists and Developers can build time series forecasting models using data stored in Microsoft Fabric paired with the Nixtla TimeGEN-1 model. The introduction of Nixtla TimeGEN-1 marks a transformative moment in time series forecasting. Whether you're a data scientist, a business owner, or a student interested in AI, TimeGEN-1 opens up new possibilities for understanding and predicting future trends. Explore TimeGEN-1 and thousands of other models through the Azure AI model catalog today!
