The Future of AI: Developing Lacuna - an agent for Revealing Quiet Assumptions in Product Design
A conversational agent named Lacuna is helping product teams uncover hidden assumptions embedded in design decisions. Built with Copilot Studio and powered by Azure AI Foundry, Lacuna analyzes product documents to identify speculative beliefs and assess their risk using design analysis lenses: impact, confidence, and reversibility. By surfacing cognitive biases and prompting reflection, Lacuna encourages teams to validate assumptions through lightweight evidence-gathering methods. This experiment in human-AI collaboration explores how agents can foster epistemic humility and transform static documents into dynamic conversations.Integrating Azure AI Foundry with Copilot Studio: A Strategic and Technical Overview
As organizations accelerate their AI adoption, the need for flexible, scalable, and secure platforms becomes paramount. My previous article, Navigating AI Solutions: Microsoft Copilot Studio vs. Azure AI Foundry | Microsoft Community Hub, represented two powerful yet distinct approaches to building AI agents. While Copilot Studio offers a low-code/no-code interface for rapid deployment, targeting any kind of business user, Azure AI Foundry provides a pro-code environment with deep customization and orchestration capabilities, targeting developer audiences. But what if you would not need to decide between one or the other, but benefit from integrating both platforms and unlock transformative business value across all teams? This is exactly the question I got asked increasingly while I was teaching our “Copilot, Copilot Studio and Azure AI Foundry” Instructor Led Training courses as a Microsoft Technical Trainer. This article starts with the business rationale for integration. From there, I will continue with detailing the influence of cost and ROI parameters as part of decision-making. Last, I will guide you through multiple technical integration capabilities available today, and how both platforms can complement each other. Business Rationale for Integration Copilot Studio is primarily designed for business users to build conversational agents quickly. It excels in rapid prototyping, using a graphical workflow-style interface, identical to Power Automate. Users don’t require much development skills to build such agents. Azure AI Foundry, on the other hand, is tailored for developers and data scientists who are typically in need of model orchestration, customized tool integration and enterprise-grade scalability and governance. Integrating both platforms allows organizations to bridge the gap between business agility and technical depth, enabling the ones closer to the business to prototype while developers can focus on custom features, refining and scale. For example, organizations can start with Copilot Studio for customer-facing bots or internal assistants, but then later, transition to Azure AI Foundry for more complex workflows, multi-agent orchestration or custom model integration. This layered approach supports progressive AI maturity, allowing teams to evolve from simple agents to fully sophisticated AI ecosystems. Cost and ROI Considerations Copilot Studio billing vs Azure AI Foundry consumption cost billing As users interact with Copilot Studio agents, or as the agents perform tasks on behalf of users, users consume Copilot Studio messages. Copilot Studio messages are the key component influencing the monthly cost of using Copilot Studio. Capabilities are available via the Copilot Studio pay-as-you-go meter (pay per message) and the Copilot Studio message pack subscription (25,000 messages monthly) license, or a combination of both. These license options are active on tenant-level. Any user with a Microsoft 365 Copilot license gets access to Copilot Studio, with no message-based charge. More details are available in the Microsoft Copilot Studio Licensing Guide. Azure AI Foundry is part of Azure’s consumption-based model, where you do not get charged for Azure AI Foundry itself, but you get charged a consumption cost for the different models your applications use. This charge can be listed as Microsoft (e.g. Azure OpenAI) or charged through the Azure marketplace (e.g. Cohere). Image: Azure AI Foundry model cost consumption overview from within Azure Cost Analysis Depending on the AI solution architecture your application workloads are based on, you should also take other Azure costs into account such as Azure Storage Accounts, Azure AI Search, Azure App Services, Azure Key Vault and alike. Since Azure AI Foundry charges are identical to any other Azure Resource charges, managing these is not different than your current Azure Cost Analysis approach. ROI and Budget alignment From the previous section, it should be clear that allocating the right budget can become complex, depending on the AI platforms used. By integrating both platforms, organizations can achieve cost optimization, by using Copilot Studio for lightweight tasks but scaling via Azure AI Foundry for compute-intensive operations. Given the lower complexity of building applications with Copilot Studio, they tend to result in early ROI, through Copilot Studio’s fast deployment. Azure AI Foundry’s robust and extensible infrastructure could lead to a longer-term value of ROI optimization. Technical Integration Capabilities HTTP Request Trigger One integration method involves using Copilot Studio’s HTTP Request feature to trigger Foundry Agents. This allows for Natural language prompts in Studio to initiate backend processes in Azure AI Foundry. This allows users to benefit from a seamless flow between conversational UI and enterprise logic, to consult business data, run data analytics or retrieve information across different enterprise application backends. Image: HTTP Request setup within Copilot Studio Topic MCP Protocol Azure AI Foundry now supports Model Context Protocol (MCP), an open standard enabling seamless interaction between large language models (LLMs) and external tools, systems or data sources. MCP provides a model-agnostic interface for tasks such as reading files, executing functions, and handling contextual prompts. Its primary goal is to simplify the integration of LLMs with third-party systems by addressing the complexity of building custom connectors for each tool or data source. MCP Tools can be integrated into your AI solutions using Azure AI Foundry Agent Service or through common development language SDKs or REST API. Check this Microsoft Learn module for more technical details on how to configure this or check out MCP-for beginners on YouTube https://aka.ms/MCP-for-beginners. Recently, the Model Context Protocol (MCP) Connector also became available as a new tool directly within Copilot Studio. Image: Model Context Protocol Connector Tool in Copilot Studio By integrating MCP Tools from within either Foundry Agent Service or through Copilot Studio, the organization can benefit from the standardized approach to allow connectivity to different enterprise systems, data endpoints or external applications. Simplifying the complexity and providing a smooth interaction irrespective of the AI platform used, provides major benefits to both business users and developer teams building these applications. Azure AI Foundry Models available to Copilot Studio (preview feature) Azure AI Foundry Models provides +11,000 models for you to choose from, offered by both Microsoft and an extensive range of model providers such as OpenAI, DeepSeek, Black Forest Labs, Meta and many more. On top of existing models offered, organizations can also create their own customized models by fine-tuning from within Azure AI Foundry. For example, imagine an organization building an IT support agent, which interacts with end-users using a chat interface and natural language. Users might be able to provide screenshots of errors, as well as describe technical issues in their own words. Traditional LLMs could struggle with recognizing specific screenshot details or business-specific terminology used by custom in-house developed applications, as they are not trained in this kind of information. That’s where fine-tuned models could be a solution. At the time of writing this article, a new preview feature became available to Copilot Studio customers, allowing them to use any Azure AI Foundry model, both catalog and fine-tuned ones, as the primary model for their Copilot Studio Agents. (FYI, follow this link for all details on the Copilot Studio Roadmap and features list) Image: Copilot Studio New Feature setting to enable AI Foundry model integration Conclusion Integrating Copilot Studio and Azure AI Foundry is not just a technical exercise, but rather a strategic move which aligns business goals, cost efficiency, and adoption readiness. By leveraging the strengths of both platforms, organizations can build AI solutions that are agile, scalable, and secure. Your business can focus on developing (or ‘making’ if not code-based) AI Agents, without facing bottlenecks or unneeded complexity or isolation of workloads. Instead of asking the question of which platform to use for building AI applications, organizations should invest in and benefit from a tight integration between both platforms, quickly enabling teams from both the business side as well as developers, to create AI-influenced applications that provide immediate business value, without compromise. #MicrosoftLearn #SkilledByMTTAnnouncing Public Preview for Business Process Solutions
In today’s AI powered enterprises, success hinges on access to reliable, unified business information. Whether you are deploying AI-augmented workflows or fully autonomous agentic solutions, one thing is clear: trusted, consistent data is the fuel that drives intelligent outcomes. Yet in many organizations, data remains fragmented across best of breed applications – creating blind spots in cross-functional processes and throwing roadblocks in the path of automation. Microsoft is dedicated to tackle these challenges, delivering a unified data foundation that accelerates AI adoption, simplifies automation and reduces risk – empowering businesses to unlock the full potential of unified data analytics and agentic intelligence. Our new solution offers cross-functional insights across previously siloed environments and includes: Prebuilt data models for enterprise business applications in Microsoft Fabric Source system data mappings and transformations Prebuilt dashboards and reports in Power BI Prebuilt AI Agents in Copilot Studio (coming soon) Integrated Security and Compliance By unifying Microsoft’s Fabric and AI solutions we can rapidly accelerate transformation and derisk AI rollout through repeatable, reliable, prebuilt solutions. Functional Scope Our new solution currently supports a set of business applications and functional areas, enabling organizations to break down silos and drive actionable insights across their core processes. The platform covers key domains such as: Finance: Delivers a comprehensive view of financial performance, integrating data from general ledger, accounts receivable, and accounts payable systems. This enables finance teams to analyze trends, monitor compliance, and optimize cash flow management all from within Power BI. The associated Copilot agent provides not only access to this data via natural language but will also enable financial postings. Sales: Provides a complete perspective on customers’ opportunity to cash journeys, from initial opportunity through invoicing and payment via Power BI reports and dashboards. The associated Copilot agent can help improve revenue forecasting, by connecting structured ERP and CRM data with unstructured data from Microsoft 365, also tracking sales pipeline health and identify bottlenecks. Procurement: Supports strategic procurement and supplier management, consolidating purchase orders, goods receipts, and vendor invoicing data into a complete spend dashboard. This empowers procurement teams to optimize sourcing strategies, manage supplier risk, and control spend. Manufacturing: (coming soon): Will extend coverage to manufacturing and production processes, enabling organizations to optimize resource allocation and monitor production efficiency. Each item within Business Process Solutions is delivered as a complete, business-ready offering. These models are thoughtfully designed to ensure that organizations can move seamlessly from raw data to actionable execution. Key features include: Facts and Dimensions: Each model is structured to capture both transactional details (facts) and contextual information (dimensions), supporting granular analysis and robust reporting across business processes. Transformations: Built-in transformations automatically prepare data for reporting and analytics, making it compatible with Microsoft Fabric. For example, when a business user needs to compare sales results from Europe, Asia, and North America, the solution transformations handle currency conversion behind the scenes. This ensures that results are consistent across regions, making analysis straightforward and reliable—without the need for manual intervention or complex configuration. Insight to Action: Customers will be able to leverage prebuilt Copilot Agents within Business Process Solutions to turn insight into action. These agents are deeply integrated not only with Microsoft Fabric and Microsoft Teams, but also connected source applications, enabling users to take direct, contextual actions across systems based on real-time insights. By connecting unstructured data sources such as emails, chats, and documents from Microsoft 365 apps, the agents can provide a holistic and contextualized view to support smarter decisions. With embedded triggers and intelligent agents, automated responses could be initiated based on new insights -- streamlining decision-making and enabling proactive, data-driven operations. Ultimately, this will empower teams to not just understand what is happening on a wholistic level, but to also take faster and smarter actions, and with greater confidence. Authorizations: Data models are tailored to respect organizational security and access policies, ensuring that sensitive information is protected and only accessible to authorized users. The same user credential principles apply to the Copilot agents when interacting with/updating the source system in the user-context. Behind the scenes, the solution automatically provisions the required objects and infrastructure to build the data warehouse, removing the usual complexity of bringing data together. It guarantees consistency and reliability, so organizations can focus on extracting value from their data rather than managing technical details. This reliable data foundation serves as one of the key informants of the agentic business processes. Accelerated Insights with Prebuilt Analytics Building on these robust data models, Business Process Solutions offer a suite of prebuilt Power BI reports tailored to common business processes. These reports provide immediate access to key metrics and trends, such as financial performance, sales effectiveness, and procurement efficiency. Designed for rapid deployment, they allow organizations to: Start analyzing data from day one, without lengthy setup or customization. Adapt existing reports for your organization’s exact business needs. Demonstrate best practices for leveraging data models in analytics and decision-making. This approach accelerates time-to-value and also empowers users to explore new analytical scenarios and drive continuous improvement. Extensibility and Customization Every organization is unique and our new solution is designed to support this, allowing you to adapt analytics and data models to fit your specific processes and requirements. You can customize scope items, bring in your own tables and views, integrate new data sources as your business evolves, and combine data across Microsoft Fabric for deeper insights. Similarly, the associated agents will be customizable from Copilot Studio to adapt to your specific Enterprise apps configuration. This flexibility ensures that, no matter how your organization operates, Business Process Solutions helps you unlock the full value of your data. Data integration Business Process Solutions uses the same connectivity options as Microsoft Fabric and Copilot Studio but goes further by embedding best practices that make integration simpler and more effective. We recognize that no single pattern can address the diverse needs of all business applications. We also understand that many businesses have already invested in data extraction tools, which is why our solution supports a wide range of options, from native connectivity to third-party options that bring specialized capabilities to the table. With Business Process Solutions we ensure data can be interacted with in a reliable and high-performant way, whether working with massive volumes or complex data structures. Getting started If your organization is ready to unlock the value of unified analytics, getting started is simple. Just send us a request using the form at: https://aka.ms/JoinBusAnalyticsPreview. Our team will guide you through the next steps and help you begin your journey.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 blogMicrosoft Copilot Studio: Créer et déployer un chatbot
Qu’est-ce que Copilot Studio ? Copilot Studio est un outil faisant parti de Microsoft 365 qui permet de créer facilement des Chatbots. Un Agent Copilot est capable de discuter avec les utilisateurs en langage naturel. Ils peuvent répondre à des questions, guider les utilisateurs ou même déclencher des actions comme envoyer un courriel ou récupérer des données. Un outil sans code Avec Copilot, pas besoin d’avoir une connaissance en programmation. Son interface est simple, on peut écrire des questions, définir des réponses, ajouter des boutons et même le connecter à d’autres services comme Power Automate, SharePoint ou la Dataverse. Un grand avantage : Décrire pour créer Avec Copilot Studio, on peut décrire en quelque lignes ce que l’on veut que notre chatbot fasse, et Copilot Studio génère automatiquement un chatbot de base avec des sujets et des réponses. Exemple : « Tu es un assistant virtuel pour les étudiants d’une université. Tu réponds aux questions sur les horaires de cours, l’accès à la bibliothèque, les inscriptions, et les coordonnées du secrétariat. » Copilot Vs Chatbot classique Contrairement à un simple chatbot basé sur des mots-clés, un Agent Copilot peut : Comprendre le langage naturel S’adapter aux différentes manières de poser une question Se connecter à des données personnalisées. Être déployé sur plusieurs canaux comme Microsoft Teams, Power Apps, site web, etc… Exemple concret : Un responsable d’un projet d’étudiant qui veut créer un assistant virtuel pour répondre aux questions fréquentes sur le projet : Quels sont les horaires de la bibliothèque ? Comment envoyer un rapport final ? Qui est le coordinateur pédagogique ? En quelque minutes, on pourrait créer un agent Copilot qui répond à toutes ces questions. C’est ça l’utilité de Copilot Studio. Créer Un Copilot Agent Accéder à Copilot Studio - Depuis votre navigateur préfère (Edge, Chrome…) - Ouvrir la page : https://copilotstudio.microsoft.com - Connectez-vous à votre compte Microsoft (Scolaire ou professionnel) - Une fois connecté, vous arrivez sur le tableau de bord - Dans la fenêtre de chat, sous Décrire votre assistant pour le créer, mettons ce texte - Vous êtes un assistant qui aide à répondre aux questions sur les déplacements en toute sécurité dans le pays et à l’international. Veuillez répondre poliment. - Avec ceci, Copilot Studio crée un assistant qui comprend déjà les thèmes, et que l’on peut ensuite personnaliser. D’ici l’assistant est presque prêt, dans la conversation, Copilot nous suggère un nom pour notre assistant Conseiller Sécurité Voyage. On peut confirmer ceci si le nom nous va. - On met « Ok » dans la case de conversation et tapé sur Entré - Sélectionner le bouton Créer pour continuer A ce niveau, Copilot Studio nous configure notre chatbot et nous redirige vers la page de configuration. Sur cette nouvelle page, on peut modifier certaines parties comme le nom du chatbot, les instructions, ajouter des connaissances, des déclencheurs, des requêtes et autres. Descendons un peut vers le bas pour ajouter des connaissances et activer l’option Recherche sur le Web. Dans la fenêtre Ajouter des connaissances, on sélectionne Sites web publics et taper ceci (https://europa.eu/youreurope/citizens/ ) dans la case Lien du site web public puis taper sur ajouter ensuite taper sur Ajouter à l’assistant pour fermer la fenêtre. Tester et déployer Copilot Maintenant notre Copilot est prêt. Dans la partie Tester votre assistant, on peut tester le chatbot voir si la connaissance ajoutée fonctionne bien. Avec le teste fait dans cette démo, l’assistant nous retour une réponse avec des liens redirigeant vers le site ajouté a la liste des connaissances. Selon les réponses données par l’assistant ou le chatbot, on peut corriger les sujets si nécessaire. Ce test permet d’assurer que notre chatbot comprend bien l’utilisateur et répond de manière utile. Notre chatbot est prêt, on peut le publier. - Cliquez sur Publier en haut à droite. - La publication prend quelques secondes. Maintenant le chatbot est prêt à être utiliser dans d’autres plateformes ou canaux. On peut l’ajouter à Microsoft Teams, à un site web, à SharePoint et autre plateforme. Conclusion Créer un Copilot personnalisé avec Microsoft Copilot Studio, c’est à la portée de tous même sans expérience en développement. En un bout de temps, on peut : - Décrire l’assistant en un phrase simple - Laisser l’intelligence artificielle générer les sujets de discussion - Ajouter des réponses et des actions - Le tester, le publier et le déployer sur Microsoft Teams, SharePoint, site web et autres. Curieux d’exploiter l’univers de l’intelligence artificielle, Copilot Studio est un excellent point de départ pour apprendre à créer des expériences interactives utiles et modernes. Pourquoi ne pas créer ton premier Copilot dès aujourd’hui ? Pour plus d’information, aller sur la page de Microsoft Learn : https://learn.microsoft.com/fr-fr/microsoft-copilot-studio/New Microsoft Applied Skill Alert – Create Agents in Microsoft Copilot Studio (APL-7008)
Hi Friends👋 If you’ve been demoing Copilot Studio in your classes, here’s a quick way to validate and showcase those agent-building skills—without sitting a full certification exam. Why grab this micro-credential? Hands-on, half-day lab — prove you can build, publish & govern generative-AI agents end-to-end. Instant résumé boost — digital badge drops into Credly the moment you pass. Perfect add-on to PL-300/PL-400/PL-700 prep or any Power Platform course you teach. Lab tasks you’ll master Design the agent persona & generative AI instructions Build topics, variables & rich dialogues (Adaptive Cards included!) Call Dataverse data with Power Automate flows Publish to Microsoft Teams & the web, then secure with content moderation Prep in three steps Run the free learning path: Create agents in Microsoft Copilot Studio (9 bite-size modules). Skim the official study guide checklist (APL-7008). Spin up a trial tenant for learners and let them practice before the live lab. Ready? 👉 Copilot Studio Applied Skill Let’s keep empowering our students (and ourselves) to build the next generation of AI agents inside Microsoft 365. If you earn the badge, drop it below—would love to celebrate your win! 🏆 #CopilotStudio #AppliedSkills #PowerPlatform #GenerativeAIMastering Agent Governance in Microsoft 365
The "Mastering Agent Governance in Microsoft 365" series is based on the Administering and Governing Agents whitepaper published by Microsoft and designed to educate IT leaders, compliance officers, and decision-makers about the importance of governance for AI agents in Microsoft 365, particularly in highly regulated industries like Healthcare and Life Sciences (HLS). The six-episode series cover the growing role of agents, the risks of unmanaged agents, and the strategic importance of governance frameworks. Empowering innovation while protecting patient data and ensuring compliance In the age of AI-powered productivity, agents—automated digital assistants built with tools like Microsoft 365 Copilot, SharePoint, and Copilot Studio—are transforming how work gets done. From streamlining clinical documentation to automating regulatory reporting, agents are becoming indispensable in Healthcare and Life Sciences (HLS). But with great power comes great responsibility. Why Governance Can’t Be an Afterthought In highly regulated industries like HLS, where data sensitivity and compliance are paramount, the rise of autonomous agents introduces new risks: Unauthorized data access could expose protected health information (PHI). Unmonitored agent behavior could lead to regulatory violations. Lack of lifecycle controls could result in outdated or insecure agents operating in production environments. Agent governance isn’t just an IT concern—it’s a business imperative. It ensures that innovation doesn’t outpace compliance, and that every agent deployed aligns with organizational policies, security standards, and regulatory frameworks like HIPAA, GDPR, and FDA 21 CFR Part 11. Understanding the Agent Landscape Microsoft 365 supports a spectrum of agent creators: End Users using SharePoint or Copilot templates to automate simple tasks. Makers building more complex agents in Copilot Studio. Developers crafting sophisticated, enterprise-grade agents with Azure AI and Teams Toolkit. Each persona requires a different level of oversight. For example, a clinical researcher using SharePoint to build a data retrieval agent may need minimal governance, while a developer building a patient-facing chatbot must adhere to strict data protection and validation protocols. Governance in Action Microsoft provides a layered governance model: Tool Controls: Define what agent creators can do within tools like Copilot Studio and SharePoint. Content Controls: Ensure agents only access data they’re authorized to use, leveraging Microsoft Purview for sensitivity labeling and DLP. Agent Management: Monitor usage, enforce lifecycle policies, and block non-compliant agents via the Microsoft 365 Admin Center. This framework allows organizations to empower innovation while maintaining control—critical in environments where patient safety and regulatory compliance are non-negotiable. The Business Case for Governance For HLS organizations, agent governance delivers tangible benefits: Reduced compliance risk through proactive policy enforcement. Improved operational efficiency by enabling safe automation. Greater trust from patients, regulators, and internal stakeholders. In short, governance is the foundation that allows agents to scale safely and sustainably.Leveraging Copilot Studio to Create Powerful Web Focused Bots for Designated Roles
In the ever-evolving landscape of digital transformation, tools that simplify complex processes and empower users with minimal coding expertise are becoming increasingly essential. One such groundbreaking tool is Copilot Studio, a versatile platform that enables the creation of web-focused bots designed for specific roles within an organization. By leveraging the power of generative AI, Copilot Studio not only streamlines the development process but also enhances the capabilities of these bots, making them indispensable assets in the modern workplace.
Ignite 2024: Streamlining AI Development with an Enhanced User Interface, Accessibility, and Learning Experiences in Azure AI Foundry portal
Announcing Azure AI Foundry, a unified platform that simplifies AI development and management. The platform portal (formerly Azure AI Studio) features a revamped user interface, enhanced model catalog, new management center, improved accessibility and learning, making it easier than ever for Developers and IT Admins to design, customize, and manage AI apps and agents efficiently.
