The answer usually isn’t model quality or tooling. It’s whether organizations are establishing the right constraints, setting the right ambition, and designing for scale from day one. The counterintuitive lesson I keep seeing: the organizations moving fastest are the ones that kept their guardrails tightest.

As I shared with Keith, organizations that succeed don’t treat AI as a side project. One customer we discussed, a leading American multinational financial services company, is a good example of getting this right. Their first AI deployment wasn’t about a vague AI-adoption goal, or a generalist chatbot. It focused on a concrete, human problem: employees across branches spending time searching through hundreds of forms and procedures during customer interactions. The agent allowed branch staff—across consumer, small business, and other roles—to describe what they needed in natural language and be routed instantly to the right process.

The impact was immediate: shorter wait times, smoother interactions, and more time focused on the customer, without introducing new risk. Importantly, the agent didn’t approve transactions or make financial decisions. It helped people move faster, and the guardrails were exactly what made that possible at scale.

By setting clear ambition while focused on a high-impact, low-risk use case, they were able to build confidence in the technology. They created momentum while not forcing the organization to compromise governance.

Humans stay in control

Scaling AI successfully means keeping people in the loop. I’ve seen that the most successful implementations are explicitly human led. Transparency is key. Audit trails, activity logs, and clear explanations of agent behavior build trust and make systems easier to improve over time.

Success also requires the right kind of guardrails. In regulated industry workflows, it’s not enough for an agent to be right, it needs to be explainable. For example, in a power of attorney verification—agents can handle extraction, matching, and analysis in seconds. Ambiguous or high-risk cases are escalated to humans. With agents as colleagues, teams can spend less time on routine checks and more time applying judgments where they actually matter.

Scaling without sprawl

As adoption grows, the challenge isn’t just proliferation—it’s clarity. Successful organizations distinguish between:

• Personal productivity agents.
• Team-level agents.
• Enterprise agents.

Each category carries different expectations for governance and oversight. A personal agent helping someone summarize documents doesn’t carry the same risk as an agent touching customer data across systems. Builders can solve problems for themselves or their teams, but expanding solutions generally triggers reviews and accountability.

Governance as an enabler

The fastest-moving organizations have created clear, governed pathways for experimentation and deployment of agentic solutions. New solutions are started in constrained environments with limited data access. As they prove value and maturity, they can be promoted into environments with broader reach and tighter oversight. This approach enables innovation while maintaining visibility, accountability, and control over data access and sharing.

Here’s the reality leaders can’t ignore: people will use AI regardless. The choice is whether they do it inside your platform, with your data protected and your policies enforced, or outside of it.

The urgency has changed

Over the past year what’s changed most isn’t the technology—it’s the level of urgency. Organizations no longer ask why they need AI. They ask how to govern it so they can move now.

In the organizations I’ve worked with, the platforms and controls are often already there. The differentiator is execution: choosing the right first problems, designing with governance in mind, and scaling in a way that keeps humans firmly in control.

That’s how AI moves from a pilot to production—and stays there.

Watch my full conversation with Keith Kirkpatrick to hear more real-world examples and lessons from regulated enterprises putting AI to work today.

The post Scaling AI with purpose: How organizations are balancing ambition and control appeared first on Microsoft Power Platform Blog.

In a recent conversation I had with Dion Hinchcliffe at Futurum, we spent time unpacking a shift I’m seeing consistently across enterprises experimenting with AI. It’s not just about copilots or chat interfaces. It’s about something deeper: a change in how work is designed, governed, and operated when systems can reason and act with intent.

For decades, applications have been the primary interface between people and systems. Work meant navigating menus, filling out forms, and clicking through screens carefully designed to constrain what users could do. Productivity improvements came incrementally—better layouts, faster load times, and more automation behind the scenes—but the underlying engagement model stayed the same. People adapted to software.

That model no longer holds.

As organizations race to adopt AI, a new challenge is becoming clear: translating human intent into systems that can act autonomously—without sacrificing control, security, or trust. Intent-first development addresses that gap by reshaping how agentic applications are designed, governed, and delivered at scale.

Agents as the new interaction layer

Instead of teaching people how to use systems, we can let people express intent—and allow systems to determine how that intent is carried out. This is not about replacing all apps overnight. It’s about changing their role. Apps no longer need to expose every possible action through UI. Instead, they:

• Provide trusted capabilities the agent can invoke
• Enforce business rules and permissions
• Act as systems of record, not systems of navigation

As AI systems become capable of reasoning, acting, and adapting, organizations are beginning to rethink the relationship between humans and software. In an agentic model, the agent becomes the primary interaction surface. A user may no longer need to know which system to open or which workflow to follow. They can simply state what they want to achieve: open a purchase order (PO), resolve this case, prepare a customer briefing.

Behind the scenes, agents orchestrate the necessary steps across systems, policies, and data sources. Procurement rules are applied. Approvals are routed. Records are updated. The user expresses intent once; the system coordinates the work.

Agentic solutions aren’t eliminating applications, but they are changing how people engage with them. Apps are the trusted capabilities agents rely on—serving as systems of record, sources of authority, and enforcement points for business rules and permissions. Applications shift from user destinations to services agents invoke. Agents work because structure already exists.

Rethinking enterprise complexity: Orchestration over navigation

This shift becomes clearer when you look at everyday enterprise processes.

Take something as common as opening a purchase order. Today, that often means navigating multiple tools, involving several teams, and manually coordinating approvals. The complexity isn’t the work itself—it’s knowing how to move through the systems.

With an agent‑first approach, that complexity is inverted. A user can simply say they need to open a PO for a project. The agent determines which background agents are required—vendor management, policy validation, approvals—and orchestrates the process across systems without forcing the user to navigate them.

We see the same pattern emerging in CRM. Rather than sales teams manually updating records, agents can monitor emails, calls, calendars, and systems in the background—keeping data current and surfacing relevant context proactively. The agent becomes the interface to customer intelligence, while the CRM remains the authoritative store behind it.

The value here isn’t conversational UI for its own sake. It’s reducing cognitive load while preserving control.

Agents as the business logic and decision layer 

This shift also changes where business logic lives.

Traditional enterprise systems embed logic deep inside individual applications—rules, workflows, and decision trees hardcoded into each tool. That makes change expensive and reuse difficult. When requirements evolve, logic must be rewritten repeatedly across systems.

Agentic systems invert that model. Logic moves into a shared reasoning layer that sits above systems of record. Agents evaluate intent, context, and constraints, then determine which actions are required right now. Policies, best practices, and exceptions can be defined once and applied consistently across processes instead of being repeatedly embedded in individual applications.

This is where the economics of software start to change. Improvements to reasoning or decision quality can compound across organizational functions—HR, finance, operations, and customer engagement—without rebuilding each system individually. Business value shifts from static workflows to shared enterprise intelligence.

Headless agents as a new layer of digital labor 

Not all agents interact directly with people.

Many of the most impactful agents operate quietly in the background—monitoring systems, reacting to triggers, coordinating tasks autonomously. These “headless” agents update records, flag issues, generate reports, and escalate decisions only when human judgment is required.

Together, conversational and headless agents form a new layer of digital labor. Routine work is handled automatically. Humans stay focused on oversight, judgment, and exceptions. The agent doesn’t replace enterprise logic—it coordinates it.

Operating agentic systems at scale requires a control plane

One point Dion and I kept coming back to is this: the real challenge with agentic systems isn’t building the first one. It’s operating hundreds—or thousands—of them responsibly.

As agents scale across teams and geographies, the questions shift quickly. How do you maintain visibility into what agents are doing and why? How do you enforce security, policy, and compliance consistently as agents act across systems? How do you measure impact, cost, and effectiveness as usage grows?

Without a managed platform, intent first development becomes ungovernable at scale. Logic fragments. Visibility breaks down. Early experimentation turns into operational risk. Governance must mature alongside autonomy.

This is where enterprise readiness becomes decisive.

Governance, lifecycle management, observability, and control aren’t optional add‑ons. They’re the foundation that allows agents to operate safely and reliably. Successful enterprise adoptions hide complexity behind an interface that works the way people already think.  Agents don’t eliminate the need for structure—they depend on stronger, more explicit structure than traditional automation ever required.

From pilots to an enterprise operating model

Most organizations begin with pilots—and that’s the right place to start. But pilots stall when governance, ownership, and measurement are treated as afterthoughts.

The pilots that scale share common patterns: centralized policy management, clear accountability between IT and business teams, built-in monitoring, and an explicit path from experimentation to production. Governance isn’t what slows progress; it’s what gives leaders confidence to move faster.

Over time, this becomes more than a collection of use cases. It becomes an operating model. Work shifts from task execution to outcome driven orchestration. Processes move from periodic redesign to continuous optimization. Systems adapt as business intent evolves.

Building adaptive enterprise systems for an agent-first world

This shift isn’t about predicting the future. It’s about building systems that can adapt as it arrives.

Agentic transformation isn’t just a technical change. It’s an operational one—reshaping how work is designed, governed, and continuously improved across the enterprise. Organizations that invest early in the right foundations—clear intent, strong constraints, and disciplined scale—will be positioned to turn intelligent applications into a durable advantage, not a fleeting experiment.

The most successful organizations won’t ask how to bolt agents onto existing apps. They’ll ask how to redesign systems so agents can sit confidently at the front door—turning intent into action with trust, speed, and scale.

In an agent first world, applications remain systems of authority and agents simply coordinate how and when those capabilities are invoked. Apps evolve:

• From destinations → to services
• From user driven workflows → to agent orchestrated actions
• From “where work happens” → to “how work is made possible”

If you want to hear this thinking unpacked in more detail, I explore these ideas directly with Dion Hinchcliffe at Futurum—from agents as the new interaction layer, to why governance becomes more critical, not less, as autonomy increases. Our conversation gets into real enterprise examples, the challenges of moving beyond pilots, and what it actually takes to operate agentic systems at scale.

I encourage you to watch the full interview to hear how these concepts show up in practice and to learn how intent first development is shaping the future of enterprise AI.

The post From apps to agents: Rearchitecting enterprise work around intent appeared first on Microsoft Power Platform Blog.

Productivity is a starting point, not the goal

Most organizations begin their AI work with personal productivity: meeting summaries, drafting content, finding information faster.

There’s nothing wrong with starting there. The risk is stopping there. Tools help individuals move faster, but they don’t change how ;work flows through the business. The underlying processes stay the same. People still coordinate across systems. Measurement happens after the fact. Frontier Firms, businesses that are operationalizing AI across functions, take the next step: process-level change. They redesign workflows so that agents handle defined tasks end to end, with people stepping in where judgement and context matter most.

Systems of record are moving beyond storing data

The most effective AI deployments shift responsibility from people to agentic systems operating under clear boundaries. In customer service, agents handle routine interactions, gather context across sales, service, and billing and route complex cases with the right information attached. In finance, agents monitor payment patterns, flag exceptions, and initiate follow-up within defined limits. In both cases, the system of record is no longer a passive data store—it owns a workflow. That requires well-defined inputs, rules, and handoffs. Getting those right is the hard part!

The differentiator: governance and measurement

As agents scale, complexity arrives fast. You don’t end up with a handful of automated processes. You end up with hundreds or thousands. Without visibility, teams lose track of what is running and why. Progress in agentic CRM and ERP will not come down to who adds the most agents. It will come down to who can govern them. Frontier Firms also measure outcomes: resolution time, cash collection, pipeline velocity. Every deployment ties back to a metric that already matters to the business. If leaders can’t tell whether the system is helping, progress stalls.

What this means for business leaders

The organizations pulling ahead are redesigning processes with agents, so people can focus on work that creates real impact. That work starts small: one function, one process, one metric. Then it builds through iteration. I discuss these ideas in more detail in my conversation with Mitch Ashley, including how AI moves from experimentation into the core of how a business operates. Watch the full discussion to hear how these patterns are showing up across industries.

What else can you do?

• Turn great ideas into impactful solutions with Microsoft Power Platform
• Learn to transform your business with modern, AI-first applications in Microsoft Power Apps
• Optimize business processes with an end-to-end automation solution built for enterprise with Microsoft Power Automate

*Gartner Q&A with Roxane Edjlali, February 2025: 63% Lack AI-Ready Data

The post Agentic business transformation: What leaders need to get right appeared first on Microsoft Power Platform Blog.

That’s the promise of Adaptive Intelligence, and it’s now within reach. In our recent Ignite session, Jason Huang and I walked through exactly how to build enterprise-grade agents that don’t just automate—they think, adapt, and elevate work. See this in action:

Why Adaptive Intelligence Matters

Security and governance are table stakes. The real question is: are your agents smart enough to be useful?

The real differentiator is intelligence. Agents that understand your business. That remember how you work. That act proactively, not reactively.

That’s Adaptive Intelligence. And it’s built on two foundations:

1. Work IQ + Dataverse: Work IQ is the intelligence layer that gives agents context, memory, and inference. Dataverse extends Work IQ with trusted business data: giving agents the ability to reason over your structured enterprise data while staying inside governed boundaries. Together, they transform Dataverse from a system of record into an intelligent system of action.
2. Agent 365: the control plane that makes agents enterprise-ready by default. It handles registry, access control, visualization, interoperability, and security—so IT doesn’t lose control as agents scale.

When Work IQ, Dataverse, and Agent 365 come together, agents don’t just automate. They think. They adapt. They elevate work.

How We Built It

The agent in our demo, Aligna, is an AI teammate for a fictitious construction company (Zava Construction). In the demo, a site coordinator chats with Aligna in Teams: “@Aligna, I’m at the Adatum site, log this plumbing issue, inform the client…” — and Aligna handles the rest.

Here’s how each component powers that workflow:

Step 1: Microsoft Agent Framework

Aligna is built on the Microsoft Agent Framework—it’s what enables the agent to receive the Teams prompt, decide which tools to call, and orchestrate the entire workflow. When the user describes the plumbing issue, the Agent Framework routes the request to the right tools (Dataverse MCP for logging, Mail MCP for communication) and manages the execution flow.

Step 2: Dataverse MCP

When the user says “log this plumbing issue,” Aligna uses Dataverse MCP (now in GA) to create the issue record directly in Dataverse—no forms, no app-switching. All business data (clients, contacts, projects, vendors) lives in Dataverse, and MCP provides the standardized interface for Aligna to read and write that data through natural language.

Step 3: Mail MCP Server

When the user says “inform the client,” Aligna uses the Mail MCP Server to draft and send the email—all within the same conversational flow. The client gets notified about the issue without the user ever leaving Teams.

Step 4: Agent 365 SDK

Every action Aligna takes—logging the issue, sending the email—is recorded via the Agent 365 SDK for compliance and audit. IT gets full observability into what the agent did, when, and why—without requiring extra instrumentation from developers.

Step 5: Dataverse SDK for Python

For developers building agents like Aligna, the Dataverse SDK for Python (in Public Preview) provides direct programmatic access to Dataverse—ideal for custom data operations, schema setup, batch processing, or integrating with Python-based ML pipelines. Also leveraged: Azure OpenAI for model inference

Turn Your Business Data into Agent Intelligence

The tools are here. The documentation is live. Now it’s your turn.

Start building agents that truly understand your business:

1. Connect to your data — Use Dataverse MCP to give your agents instant access to business data. It’s GA and ready for production.
2. Write in your language — Python developer? The Dataverse SDK for Python lets you prototype in minutes. .NET team? The Agent Framework has you covered.
3. Make it enterprise-ready — Register with Agent 365 and your agent inherits identity, governance, and observability from day one.

Your competitors aren’t waiting. The agents your organization needs—agents that think, adapt, and elevate work—you can build them now.

Read More

Related Documentation:

• Dataverse MCP: Connect to Dataverse with MCP 
• Dataverse SDK for Python: SDK Overview
• Agent 365 Overview: Agent 365 Documentation
• Agent 365 SDK: Agent 365 SDK Overview
• Microsoft Agent Framework: Agent Framework Documentation
• Mail MCP Server: Mail MCP Server Reference

Related Ignite & Blog Resources:

• Ignite 2025 Book of News
• Microsoft Agent 365 Interoperability for Smart, Secure Productivity – Microsoft 365 Developer Blog

The post Build Agent 365-ready Adaptive Intelligence with Work IQ and Dataverse appeared first on Microsoft Power Platform Blog.

The Upwork and Microsoft Power Platform collaboration opens a world of possibilities for organizations eager to leverage innovative low-code solutions, AI Agents, and automation tools. Businesses can now access certified professionals through Upwork’s Power Platform Experts page, helping them accelerate project timelines and unlock new efficiencies. This approach offers flexibility and access to specialized talent, helping organizations stay competitive in a fast-moving digital landscape.

According to Upwork’s Future Workforce Report, freelancers collectively generated $1.5 trillion in earnings in 2024 as independent professionals. For developers, consultants, and solution architects specializing in Microsoft Power Platform, this partnership is an invitation to elevate careers and expand their reach. By signing up to become an Upwork Power Platform Partner Expert, professionals can connect with a global clientele and apply their certifications to real-world projects. While individual results may vary, this program enables participants to contribute to transformative solutions for businesses of all sizes.

Microsoft certifications can serve as a launchpad to freelance opportunities in this program, enabling professionals to contribute to innovative projects while building a flexible and rewarding career.

Redefine what’s possible, join the Power Platform Expert Freelancers to explore new opportunities, supercharge your projects, and innovate globally.

The post Empowering a New Era of Agent-Based Experiences: Upwork and Microsoft Power Platform Partnership appeared first on Microsoft Power Platform Blog.

Recent research conducted by Forrester Consulting and commissioned by Microsoft shows a strong correlation between generative AI (gen-AI) readiness and low-code maturity, suggesting that the increased use of AI-fueled tools in development is boosting low-code adoption. 

Power Platform and agents could be your gateway to a future of accelerated development, improved efficiency, and greater overall productivity. Prior to implementation, it’s important for organizations to determine their level of readiness, or maturity index, for these AI-powered, low-code tools. 

Changing development preferences with low-code and generative AI

Low-code platforms emerged to help organizations adapt to the digital shift and manage complex software development processes. Before the introduction of AI, Power Platform already allowed business users with little or no formal coding experience to create custom applications and streamline workflows, accelerating the development process and empowering more people within an organization to participate in digital transformation. The introduction of AI and Microsoft Copilot capabilities to agents and Power Platform further enhances development by allowing even more people to create innovative solutions using natural language. 

“One of the big benefits for us of Power Platform is that it all works together so seamlessly. That’s making it even easier to leverage the benefits of AI and add it to applications that have a real impact on our business.” 

Brian Hodel, Senior Analyst, Business Systems, T-Mobile 

Forrester’s research highlights a shift in application development towards low-code platforms and AI tools. Notably, the adoption of generative AI-infused capabilities is projected to boost low-code development by 28%, signaling a major shift towards these innovative methodologies such as Power Platform and agents. 

Understanding where your organization stands can identify areas for improvement and strategic growth, helping to ensure that you maximize the benefits of these modern development methodologies. But where should you begin? 

Assigning maturity level 

To effectively prepare for AI-first development, organizations should assess their readiness to integrate AI into low-code platforms, ensure that robust governance policies are in place for AI usage, and address data security gaps. The Forrester Consulting AI and low-code readiness assessment commissioned by Microsoft provides insights and recommendations to boost development efficiency, guide investment decisions, and improve data management. 

Specifically, the assessment measures: 

• Low-code maturity—evaluating your organization’s current use (if any) of low-code tools and overall strategy for low-code development. 
• AI readiness—assessing your organization’s understanding of generative AI, governance and policies for AI usage, and ability to integrate AI capabilities.
• Data security and governance—identifying gaps in data curations, access controls, and training to manage and secure data effectively. 

This proactive approach helps organizations make informed decisions and enhances their overall readiness for AI-first development. Dive deeper into why this assessment is instrumental for businesses aiming to stay ahead. 

Evaluate your organization’s current development strategy 

Understanding your organization’s current use of low-code tools and the level of adoption is a crucial first step for staying competitive. Forrester’s research indicates that 78% of development leaders say their firms empower non-IT employees through a citizen developer strategy or plan to do so in the next 12 months, showing how crucial it is to understand the integration of these tools into workflows and their effectiveness in achieving development goals. 

Assessing your organization’s current use of low-code tools is not just about understanding where you stand; it is about strategically positioning yourself to harness the full potential of these low-code and AI technologies, driving innovation, and staying competitive in a rapidly evolving digital landscape. 

Assess your organization’s understanding of AI

By using Power Platform and agents, organizations can automate repetitive tasks, generate code, and provide intelligent recommendations, accelerating the development lifecycle. AI-infused tools in Power Platform and agents offer transformative potential—but to harness its power effectively, it is crucial to assess your organization’s existing knowledge and readiness. According to Forrester’s research, 81% of IT leaders state that most or all developers in their organization use generative AI tools for various tasks, emphasizing the widespread adoption and necessity of understanding these tools. 

Before deploying Power Platform and agents, IT leaders should assess existing governance and security policies regarding AI usage to comprehensively understand the organization’s readiness for AI-first development. This approach helps to ensure that the integration of AI tools is innovative, secure, and strategically sound. 

Evaluate your existing security and governance standards 

The integration of AI and low-code platforms has the potential to greatly improve development efficiency and drive innovation. Additionally, it presents new opportunities for maintaining robust organizational security and governance. 

As businesses increasingly adopt low-code and AI technology, IT leaders must identify any existing gaps in their data curation, access controls, and training throughout implementation and usage. Forrester’s research highlights the importance of governance in facilitating safe and effective use of generative AI tools. Addressing these challenges is crucial to harnessing the full potential of low-code and AI technologies, driving innovation and maintaining competitiveness in a rapidly evolving digital landscape. 

In addition to incorporating a diverse development strategy, nearly 75% of IT leaders anticipate increased investments in AI-enhanced development tools over the next year, highlighting the need to address gaps in security and governance strategies. Identifying gaps in data curation, access controls, and training is crucial for secure and effective AI and low-code integration. Building and maintaining a robust plan for data security and governance practices are essential to both avoid inaccuracies that could compromise AI models and low-code applications and foster a culture of security awareness and proactive risk management. 

By identifying and addressing these key gaps, IT leaders can help to ensure that their organizations manage and secure data effectively while embracing agents and AI in Power Platform. 

Key recommendations for enhancing readiness 

Based on the findings, Forrester provides several recommendations in the study to assist organizations in improving their low-code and AI maturity¹. Power Platform and agents can provide substantial support in implementing these recommendations. 

• Establish a proactive development program. Use the core managed platform capabilities within Power Platform to enhance security, governance, and operations at scale. Use the Power Platform Center of Excellence (CoE) to provide structured tools, best practices, and resources, facilitating effective management and support of the platform. Aligning these activities with organizational objectives encourages innovation and improves developer efficiency to continuously benefit from AI-first development. 
• Empower developers with AI-first development platforms. Use platforms such as Power Platform and agents to expedite development processes while integrating AI-powered business insights. AI-powered agents boost developer efficiency, enhance code quality, and reduce the time needed to bring new applications to market. Tools like Microsoft Copilot Studio facilitate the incorporation of generative AI features in low-code environments. 
• Classify, secure, and expose data and endpoints. Deploy data governance frameworks through Power Platform to classify, secure, and expose data and endpoints. This involves setting up data loss prevention (DLP) policies and using managed identities for secure access. The platform helps to ensure curated, clean, and secure data sources and endpoints, offering robust security, compliance, and operational excellence. 
• Govern through pragmatic risk management. Implement automated risk management processes with Power Platform and Power Platform admin center (PPAC), tailored to application complexity and risk levels. PPAC provides continuous monitoring, real-time alerts, and adherence to compliance standards, helping to ensure managed security, governance, operations, and availability. This comprehensive approach helps mitigate risks and maintain a secure development strategy. 
• Scale AI experimentation through citizen development. Allow IT leaders to spearhead AI experiments and develop innovative solutions using Power Platform and agents. This approach fosters continuous improvement and innovation. Providing training and support for developers at all levels enhances their technical skills and process knowledge, empowering them to effectively use AI capabilities. 

Take the readiness assessment today 

Begin your journey toward AI-first development with our AI and low-code readiness assessment. This crucial first step will set the foundation for implementing cutting-edge development tools in Power Platform, allowing your organization to realize the full benefits of AI-first development. 

Take the assessment now and unlock the full potential of AI capabilities in the Power Platform.

AI and low-code readiness assessment

Help your organization get the most out of Microsoft Power Platform.

Take the assessment

Explore the Power Platform Implementation Guide to discover how Power Platform and agents can help boost your score.

By addressing gaps and boosting maturity, your organization can better prepare for the future of AI-supported development. Empower developers, secure data, and transform application development with Power Platform. 

Source:

¹ Improve Productivity And Efficiency With GenAI-Infused Low-Code Development Tools, a commissioned study conducted by Forrester Consulting on behalf of Microsoft, 2025. 

The post Identify your readiness for AI-first development with agents and Microsoft Power Platform appeared first on Microsoft Power Platform Blog.

Microsoft 365 Copilot represents a leap forward in AI-powered assistance, designed to streamline workflows and enhance productivity. However, as with any robust system, ensuring data protection, governance, and monitoring are paramount.

The types of agents available within Microsoft Copilot range from task-specific agents that automate repetitive actions to conversational agents that assist with customer service inquiries. With such a wide range of agent capabilities, how do organizations balance security and governance concerns with the desire to bring great innovation and meet the demands of their makers and agent creators?

This guide explores the key aspects of securing and managing agents built with Microsoft Copilot Studio, Copilot Studio agent builder, and SharePoint agents, from data protection practices to governance at scale, visibility, and monitoring.

Data Protection

Data protection is a cornerstone of Microsoft Copilot, ensuring that sensitive information remains secure and compliant with organizational policies. Here are the primary components:

Encryption and Isolation

All data managed by Microsoft Copilot is encrypted both in transit and at rest, ensuring robust protection against unauthorized access. Data isolation mechanisms further safeguard sensitive information by preventing cross-tenant data leakage.

Persistent Label Inheritance and DLP Policies

Agents use persistent label inheritance, meaning any new content generated inherits the sensitivity labels from the source content. This ensures that data loss prevention (DLP) policies are consistently applied, reducing the risk of data breaches.

Conditional Access and Endpoint Management

To enhance security, organizations can leverage risk-based conditional access and endpoint management. This allows administrators to set policies that control access based on user risk levels and device compliance, ensuring that only authorized users can access sensitive data.

Governance at Scale

Effective governance ensures that agents are used responsibly and in alignment with organizational policies. Here’s how to manage governance at scale:

Agent Administration

Microsoft Copilot provides administration through the Microsoft 365 Admin Center and Power Platform Admin Center. This allows for streamlined management of permissions, policies, and compliance settings across the organization.

Connector Management Policies

When building agents with Microsoft Copilot Studio, makers can choose from 1500+ connectors offered by Power Platform or build custom connectors by calling REST APIs to enrich the data used with their agents. Administrators can create and enforce connector management policies to govern data flows across those connectors and services. These policies help prevent data leakage and ensure that sensitive information is adequately protected.

Visibility and Monitoring

Visibility and monitoring are critical for maintaining the security and efficiency of agent deployments. Here are the key strategies:

Agent Inventory

Agents built with Agent Builder can be viewed in the Microsoft 365 admin center, where admins can view and search the inventory of shared agents in their tenant and block the sharing of agents. To view the usage of agents built by your organization using Microsoft Copilot Studio, Agent Builder, and Teams Toolkit, visit the Usage report in the Microsoft 365 admin center. Learn more at aka.ms/MACAgentReport.

Currently in public preview, admins who need to view their custom agents built in Microsoft Copilot Studio can view agent inventory in the Power Platform admin center, on the Manage page and inventory section.

Data Security Posture Management (DSPM) for AI

DSPM for AI provides insights for IT and security teams to proactively discover data risks, such as data in user prompts, and receive recommended actions and insights for quick responses. This tool helps administrators identify potential security vulnerabilities and take proactive measures to mitigate them.

Agent Data Security and Compliance

Agents built with Copilot Studio, Copilot Studio agent builder, and SharePoint agents include comprehensive activity logging and auditing capabilities. Administrators can have clear visibility into user interactions, detect anomalies, and risky AI usage. Additionally, administrators can ensure compliance with organizational policies and can govern user prompts and agent responses with audit, eDiscovery, retention policies, and non-compliant usage detection.

Copilot Dashboard and Analytics

The Copilot Dashboard offers real-time analytics on usage, performance, and security. This visibility enables organizations to make informed decisions, optimize operations, and ensure compliance with regulatory requirements.

Setting Up Pay-As-You-Go and Azure Metering for Consumption Planning

Administrators have the option to choose between pre-paid user licenses or metered billing based on actual usage. This flexibility allows customers to gradually increase adoption while effectively managing costs. Additionally, we are committed to enhancing cost management capabilities to facilitate easier management at scale. Future enhancements will include functionalities such as the ability to charge back expenses to specific business units and set expenditure caps.

Effective consumption planning is essential for managing costs and ensuring optimal resource utilization.

Bring Agents into Your Organization Securely

Ensuring robust security and governance for Microsoft Copilot and its agents is critical for maintaining data integrity, compliance, and efficiency. By implementing comprehensive data protection measures, centralized governance, and advanced monitoring, organizations can leverage the full potential of Microsoft Copilot while safeguarding their data. Additionally, setting up pay-as-you-go and Azure metering helps manage costs and optimize resource usage, ensuring a sustainable and scalable deployment.

The post Enable Robust Security and Governance for Agents in Microsoft 365 Copilot appeared first on Microsoft Power Platform Blog.

The Evolution of Managed Environments

For those of you who have been with us on this journey, you may already be familiar with the concept of managed environments. Managed environments have been our solution to help organizations manage their digital assets at scale. As we’ve engaged with many of you and your organizations, we’ve identified common questions and concerns that arise from different roles within your organization.

When we talk to individuals in the security part of your organization, especially those who report to the Chief Information Security Officer (CISO), the questions often revolve around data protection. “How do I ensure my data is protected from threats?” “How do I prevent data exfiltration?” “How do I stay compliant and limit risk?” These are critical concerns that need robust solutions.

On the other hand, when we engage with the Chief Information Officer (CIO) and their team, the focus shifts to governance. “How do I establish guardrails to ensure solutions are well-governed from day one?” “How do I gain visibility and insights into what’s happening?” “How do I understand costs and drive greater ROI?” These questions highlight the need for effective governance frameworks.

For those in Operations Director roles, the emphasis is on operational excellence. “What does it mean to run tier-one applications at enterprise scale?” “How do I know when things aren’t working well?” “How do I ensure effective change management over these applications?” These operational concerns require a robust set of tools and practices to address them effectively.

Introducing Managed Governance, Security, and Operations

To address these diverse needs, we are proud to announce the evolution of managed environments into three distinct pillars: Managed Governance, Managed Security, and Managed Operations. Each pillar is designed to provide the capabilities you need to manage your digital assets effectively.

Managed Governance focuses on providing IT teams with the tools to oversee and manage their digital environments. This includes features like management at scale, which allows you to group environments based on organizational structure, application classification, or data sensitivity. The environment feature provides full visibility into your environments and resources, enabling you to manage innovation and productivity while mitigating risks. Reactive governance capabilities allow IT teams to respond swiftly to issues, ensuring consistent enforcement of governance policies.

Managed Security brings together all the capabilities needed to secure your low-code apps, agents, and automations. This includes security posture management, which provides intelligent guidance for scalable and efficient enterprise-grade security. Enhanced identity and access management tools ensure that only authorized users can access specific resources, with features like IP firewall, IP cookie binding, and conditional access for individual Canvas Apps. Data protection and privacy are at the core of managed security, with robust controls to ensure the confidentiality and integrity of personal information. AI-powered threat protection capabilities, integrated with Microsoft Sentinel, enable unified detection and response to suspicious activities.

Managed Operations focuses on ensuring seamless and reliable performance of your digital assets. This includes application lifecycle management (ALM) systems with approval-based production changes, service principal-based deployment, and deployment pipelines. Enhanced observability capabilities allow you to leverage Azure App Insights for instant alerts, out-of-the-box health metrics, and real-time guidance for better runtime health. Data resilience provides flexible backup and retention options to ensure quick and efficient data recovery in the event of a disaster.

The Power Platform Admin Center

All these capabilities are integrated into the Power Platform Admin Center, which has a new look and experience as of today. The Admin Center provides a unified interface for managing governance, security, and operations, making it easier for IT teams to oversee their environments.

The enhancements announced at Microsoft Ignite 2024 marked a significant step forward in the realms of managed governance, managed security, and managed operations. These new capabilities provide organizations with the tools they need to ensure robust governance, seamless operations, and top-notch security, empowering them to navigate the complexities of the digital landscape with confidence.

Whether you are an IT professional, a security administrator, or a business leader, these new capabilities offer valuable insights and practical solutions to enhance your organization’s digital strategy. Stay tuned for more updates and best practices as we continue to explore the future of managed governance, operations, and security within the Microsoft Power Platform.

The post Mastering the Microsoft Power Platform with managed governance, security, and operations appeared first on Microsoft Power Platform Blog.

Many organizations have a long history of using SharePoint lists and Excel as a starting point to manage data. These tools are familiar, accessible, and widely used across teams. However, as businesses grow and their data management needs evolve, they reveal significant limitations, leaving the business struggling to keep up with modern demands. 

In this blog, we will illustrate Dataverse’s capacity to address these challenges:

1. Explore a scenario where an organization has established point solutions with SharePoint lists and Excel. 
2. Demonstrate the progression these systems often undergo, from simplicity to complexity. 
3. Show how Dataverse can migrate organizations to a modern Power Platform architecture that meets the demanding needs of today’s data-driven world. 

“With Dataverse, we can control data access down to specific roles, making the app more secure and providing a more tailored user experience. Managers only see what they need to see.”  

Dennis Schimmel, Head of Lean goes Digital at ZF.  

Read the full ZF story

Moving Beyond Excel 

Excel is often used as a quick and easy way to store and share data, but this simplicity comes with risks. 

• Excel lacks granular security controls, making it difficult to enforce who can see or edit specific data.  
• Access to personally identifiable information (PII) cannot be readily controlled, opening the organization to compliance risks.  
• Editing rights in Excel tend to be all-or-none, leading to confusion over editing responsibilities.
• Users can inadvertently make structural changes to a spreadsheet that break dependent downstream systems.

As an organization’s data to support business processes becomes more complex, Excel begins to falter. It isn’t designed to scale for large teams or enterprise-level operations, and its interface, while functional for basic spreadsheets, is inadequate for interactive, user-friendly business applications. Organizations face challenges with version control, where tracking changes becomes cumbersome and error prone. Critical business rules or validations must often be implemented manually, leaving room for human error. Excel also fails to offer built-in application lifecycle management, leaving organizations struggling to manage the development and deployment of tools that rely on this functionality. 

How Dataverse addresses key issues with Excel: 

SharePoint List Limitations 

SharePoint lists provide added structure for collaboration and security but introduce their own specific challenges. One limitation is SharePoint’s list view threshold, which can impact performance and usability when working with more than 5,000 items. While larger lists are possible, they require additional configuration and management to avoid throttling issues. For organizations managing large datasets, this can create a bottleneck. SharePoint’s access controls meet basic needs but may lack flexibility for more complex security requirements. 

Unlike a relational database, SharePoint does not support native referential integrity – meaning data relationships between lists cannot be enforced. As data volumes grow and complexity increases, SharePoint may throttle high-volume queries, impacting performance and slowing down operations. It also lacks advanced protection measures, potentially exposing sensitive information to compliance and security issues.

Additionally, SharePoint’s design presents challenges for modern business needs. Its limitations in handling complex data relationships, such as the cap on relational lookups and lack of support for model-driven interactions, make it difficult to build scalable solutions. For businesses looking to implement sophisticated workflows or data models, SharePoint may have too many limitations. These structural constraints can affect the ability of organizations to streamline operations or scale their data strategies effectively. 

How Dataverse addresses key issues with SharePoint Lists: 

Dataverse addresses common limitations with Excel and SharePoint Lists

By addressing the common limitations of SharePoint and Excel, Dataverse positions organizations for long-term success, offering a seamless transition to a modern, efficient data management strategy. 

Streamlining Data Management with Dataverse: Transitioning from SharePoint and Excel 

The Challenge 

Imagine a mid-sized organization relying on SharePoint lists and Excel spreadsheets to manage customer data. At first, its data management strategy worked well enough – but over time, this approach led to mounting challenges. The procurement team used SharePoint lists to track equipment requests, sales managers relied on Excel sheets to calculate performance bonuses, and customer records were stored in a SaaS CRM. As the company grew, cracks began to show. 

Procurement’s SharePoint list started out simple, only containing customer names for dropdown menus. Eventually new needs emerged, necessitating the addition of shipping addresses, then customer classifications and budget thresholds. Meanwhile, the sales department created a separate Excel sheet to track performance metrics, which evolved to include calculations for monthly bonuses. When the sales team needed payroll integration, employee social security numbers (SSNs) were added to the file. 

This patchwork system introduced major challenges: 

It was clear that the organization needed a better solution. 

The Solution 

To modernize its approach, the organization migrated its fragmented systems to Dataverse, gaining a unified, secure, and scalable platform. 

The Results 

The transformation to Dataverse delivered immediate and lasting benefits: 

• Data Accuracy: A centralized repository eliminated discrepancies between systems, resolving issues like shipping errors and reducing manual maintenance. 
• Stronger Security: Sensitive data was protected by enterprise-grade security features, ensuring compliance with regulatory requirements. 
• Increased Efficiency: Automated workflows saved time and reduced the need for IT intervention. 
• Scalability: With Dataverse, the organization’s systems now support millions of records and can grow alongside the business. 

Building a Future-Ready Organization 

By transitioning to Dataverse, the organization resolved its data challenges and unlocked new opportunities. IT is now empowered to leverage this centralized data within Copilot Studio, enhancing AI-driven customer insights with a holistic enterprise view and enabling smarter decision making. 

Dataverse isn’t just a platform—it’s a foundation for secure, scalable, and integrated operations that empower businesses to thrive in a data-driven world. 

Moving Forward with Dataverse 

As organizations grow and their data management needs become increasingly complex, the limitations of tools like Excel and SharePoint become undeniable. Dataverse provides a modern, scalable, and secure platform that bridges the gap between the agility demanded by business users and the governance required by IT professionals. By centralizing data, enhancing security, automating workflows, and empowering makers, Dataverse offers a sustainable path to address today’s challenges while preparing for tomorrow’s AI opportunities. 

Whether you’re looking to consolidate siloed data, improve governance, or build innovative solutions without compromising on security, Dataverse is the cornerstone of a future-proof data strategy. It’s time to leave behind the inefficiencies of legacy tools and embrace the power of Dataverse to transform how your organization manages and leverages its data. 

“We realized that we’d hit a bit of a crossroads. We could see that the business was using Power Apps to great effect. But we also saw that there were some future limitations in terms of using Microsoft SharePoint as the data source and not being able to connect to other business applications.” 

Roy Young, Senior Solution Architect at Centrica 

Read the full Centrica story 

Next steps

If you’re ready to revolutionize the way your organization manages data, it’s time to explore what Dataverse can do for you. 

• Learn More: Visit the Microsoft Dataverse Overview to dive deeper into its capabilities and benefits. 
• Discover Power Platform: Explore how Power Apps, Power Automate, Power Pages, and Power BI integrate seamlessly with Dataverse to drive business transformation. 
• Leverage AI: Check out how Dataverse data can be turned into knowledge with the Power Platform AI Builder 
• Try it now: Get Started using Dataverse 
• Check out our podcast: Low Code Approach | Microsoft  

Further Reading: Customer Success Stories 

Explore how other organizations have successfully leveraged Microsoft Dataverse: 

• ZF Group: Established an enterprise-wide model for citizen developers using Dataverse. 
• Centrica:  Developed over 800 business solutions with Power Platform and implemented data management using Dataverse. They saw that there were some future limitations in terms of using Microsoft SharePoint as the data source and not being able to connect to other business applications. 
• Banco de Crédito del Perú: Developed three targeted solutions using Power Platform, Dataverse, and AI Builder, enhancing productivity and customer satisfaction. 

These case studies demonstrate how a shift away from future limiting tools towards Dataverse can drive digital transformation, foster innovation, and improve operational efficiency across various industries. 

The post Transforming Data Management with Dataverse: Moving Beyond SharePoint Lists and Excel appeared first on Microsoft Power Platform Blog.

From disconnected systems to ad-hoc spreadsheets, many businesses rely on manual efforts to piece data together—often at the expense of accuracy, efficiency, and scalability. The result? Fragmented insights and missed opportunities.

“When we started with Power Platform, we made the decision to jump directly into Dataverse as we saw it as the most secure and reliable way to store and manage our data – especially our mission critical data. It was a natural choice for us and it has worked amazingly well.”

Andreas Fauske, Power Platform lead at Lerøy

Read the full Lerøy story

Microsoft Dataverse: Enterprise Data Platform

Enter Microsoft Dataverse, the enterprise data platform designed to simplify how organizations integrate, manage, and access their business data. As the backbone of Microsoft Power Platform and Microsoft Dynamics 365, Dataverse provides a fully managed, secure infrastructure that centralizes and streamlines data management while ensuring enterprise-grade security, compliance, and scalability. It minimizes duplication, maintains data integrity, and enables seamless integration across applications, making it a trusted foundation for innovation. By unifying diverse data types into a structured, future-ready model, Dataverse empowers both business users and IT professionals—offering the agility to build solutions quickly with low-code tools while meeting the rigorous governance and security needs of the enterprise.

But that’s not all. With Dataverse, your data is also agent-ready—primed for AI integration and intelligent automation. By leveraging Dataverse, you’re setting the stage for cutting-edge innovations like AI-powered bots and assistants, ensuring your data is not just organized but optimized for the future.

By connecting data within a unified data platform, organizations can seamlessly integrate AI agents wherever needed, boosting productivity and leveraging their rich data sources more effectively.

Visit the Microsoft Dataverse Overview to dive deeper into its capabilities and benefits.

“Scaling innovation securely is possible, even in highly regulated environments. BCP’s experience with Power Platform demonstrates that digital transformation can be a strategic advantage, fostering growth and innovation while maintaining compliance.”

Ivan Contreras Olortegui, Lead of Cloud Center of Excellence, Banco de Crédito del Perú

Read the full Banco de Crédito del Perú story

Today’s Challenge: Buy vs Build

In today’s data-driven business landscape, organizations face a dilemma: build custom solutions or buy off-the-shelf software. This choice, is made repeatedly across departments, has led to a complex ecosystem of disparate systems, legacy applications, and ad-hoc solutions.

Such a fragmented approach to data management and application development creates significant challenges, such as data silos, inconsistent information, limited scalability, and security concerns. There is a clear and urgent need for a unified, scalable, and secure data platform that can be extended to support the organizations growing need to consume data, with the support of AI agents. This is where Dataverse comes in, bridging the gap by providing seamless access to data whenever and wherever users need it.

“Dataverse provides the enterprise security and scalability we need for larger or more strategic apps and gives us the tools to handle complex data.”

Freek Matheij, Global Citizen Development Director at Arcadis

Read the full Arcadis story

Modern Data Platform Architecture

Showing how Dataverse securely manages data from multiple sources, integrates it with Dynamics 365 and Power Platform, and is readily available for Copilot Studio.

Why use Dataverse?

Dataverse provides a secure, unified, fully managed data platform that empowers businesses to:

Dataverse for AI agents in Copilot Studio

As AI changes the ways we work, traditional workflows are getting reimagined as business process-oriented agents. However, even as makers are building more apps and agents than ever before, the majority of enterprise data still remains untapped.

To help makers unlock the full potential of their agents, we’ve made Dataverse a native experience in Copilot Studio. Makers can access enterprise data from Dataverse as valuable knowledge to customize and extend an agent’s range of functionality. This includes line of business (LoB) data from Microsoft Dataverse and third-party data from external sources (Salesforce, Zendesk, ServiceNow). 

When incorporating Dataverse as knowledge, a customized agent is created, as demonstrated in Peppermint’s Personal Injury Claims agent demo. By integrating with Dataverse, businesses can accelerate business processes and drive transformative growth. 

See what the people are saying

The following organizations have successfully leveraged Microsoft Dataverse:

• Lerøy Seafood Group: Implemented Power Platform with Dataverse to improve data sharing and analysis, utilizing the Center of Excellence Starter Kit for structured development.
• ManpowerGroup: Created a strategic workforce planning tool combining Dataverse, dataflows, and Azure OpenAI Service in just two months.
• Deutsche Bahn: Deutsche Bahn’s community of citizen developers drives innovation at scale with Power Platform to drastically reduces errors and save enormous amounts of time.
• PricewaterhouseCoopers (PwC): Migrated from a legacy system to a multitenant platform using Power Pages and Dataverse, improving innovation speed and reducing costs.

These case studies demonstrate how Dataverse can drive digital transformation, foster innovation, and improve operational efficiency across various industries.

What’s next?

In our next post, we’ll deep-dive into a common scenario of point solutions developed with Excel and SharePoint, laying out the traditional evolutionary path these systems often take, and how these can be migrated to a modern Power Platform architecture that meets the demanding needs of today’s data-driven world.

If you’re ready to revolutionize the way your organization manages data, it’s time to explore what Dataverse can do for you. Ready to learn more, check out the following resources:

• Learn More: Visit the Microsoft Dataverse Overview to dive deeper into its capabilities and benefits or visit our webpage.
• Discover Power Platform: Explore how Power Apps, Power Automate, Power Pages, and Power BI integrate seamlessly with Dataverse to drive business transformation.
• Leverage AI: Check out how Dataverse data can be turned into knowledge with the Power Platform AI Builder.
• Check out our podcast: Low Code Approach | Microsoft  

The post Agent-Ready Data Management: How Dataverse Transforms your Business appeared first on Microsoft Power Platform Blog.