Most organizations can easily identify dozens—or hundreds—of potential agentic use cases. The challenge they often face is determining how to move from their long list of options to real solutions that drive meaningful business impact, without stalling in pilot mode or creating risk along the way.

The organizations I see getting real traction—what we at Microsoft call Frontier Firms—start in a very specific place.

Start with workflows, not agents

Successful teams begin by asking, “Which workflows matter most to the business?”

Apps already encode how work gets done. They reflect real processes, real users, and real governance. That makes them the right foundation. When you introduce agents into existing applications and workflows, you’re building on sound, existing structures: security models, data access, ownership, and accountability.

This is why we consistently see better outcomes when customers start their agentic transformation with solutions they’ve already created using Power Apps and then layering in agents. Those companies are benefitting from all the hard work they’ve done defining roles, permissions, and end‑to‑end flows. Agents are how they extend those investments, building on their existing, strong foundation.

Vision needs a roadmap

Many organizations have a strong agentic vision. Fewer have a clear path to execution.

Some teams invest heavily in governance and controls, but these same teams can struggle to show early returns. Others build a compelling proof of concept, then hit friction when they try to scale it across a department or function.

The teams that break through are very deliberate about outcomes. They decide which parts of a workflow are well suited to agents, where human judgment still matters, and how those pieces connect. They focus on completing a workflow, not automating isolated steps.

Customer relationship management (CRM) is a good example. With a CRM solution, there is opportunity in identifying where agents can handle research, triage, or preparation, while humans deliver maximum value by staying focused on decisions, relationships, and outcomes.

Adoption is a capability, not an event

One of the biggest misconceptions about agents is that adoption will take care of itself. That’s not what we see in practice.

People need clarity about what an agent does, where it adds value, and how it fits into their daily work. Using agents effectively is a skill that develops over time. Learning and successful adoption require guidance, repetition, and reinforcement.

The organizations that scale successfully treat adoption as a shared muscle. Managers become power users. Learning becomes social. Teams run prompt‑a‑thons, share examples, and compare what’s working. The momentum that creates matters more than any single feature.

Security and governance enable speed

Governance is often framed as a constraint. In reality, it’s what allows teams to move faster with confidence.

As agents become more capable, managing access, intent, and oversight becomes critical. A single compromised identity can expose far more than intended if guardrails aren’t in place.

That risk is a driver for customers to establish agentic centers of excellence early. These teams focus on ideation, governance, and scale together. Once guardrails are clearly defined and in place, we see teams spending less time debating risk and more time delivering value to the business.

Focus on value, then scale deliberately

One thing I’ve seen in Frontier Firms is that they are realistic about their journey. They are generally starting small, proving the value of an agentic transformation, and then connecting additional workflows over time.

As I shared with Mitch, the strong first use cases I’ve seen are those that are repetitive, meaningful, and measurable. When a solution reduces friction for employees or customers and creates visible impact it’s often an indicator of an optimal workflow. With that initial success, it becomes much easier to extend the model across adjacent scenarios.

This approach helps customers avoid what you might call ‘pilot purgatory.’ Successful pilots don’t stay pilots. They become solutions that spread across a business because people ask for them.

Measure what actually matters

Just counting agents isn’t enough though. Real measurement of success looks at sustained usage, employee experience, and customer outcomes alongside efficiency.

As I shared with Mitch, at Microsoft, we’ve proven AI-powered success firsthand in our customer service teams. With the same number of people, we’re handling roughly twice the volume of customer cases. We’ve seen our customer satisfaction improve. And, our employees can now spend more time solving problems instead of answering the same questions repeatedly.

That combination—efficiency, experience, and capacity for innovation—is the signal we have that transformation is real.

What separates leaders from the rest

The organizations leading with agents today share a few traits. They start with workflows. They invest in governance early. They treat adoption as a capability. And they stay focused on business value, not novelty.

Agents don’t replace people. They change where people spend their time and energy. The companies that understand that are building a foundation that will serve them well as this technology continues to evolve.

Watch my full conversation with Mitch Ashley to hear more about how organizations are moving from ideas to impact and what Frontier Firms are doing differently to drive successful agentic transformations.

The post From Ideas to Impact: How Frontier Firms scale agents with confidence appeared first on Microsoft Power Platform Blog.

In my recent conversation with Keith Kirkpatrick, President and Research Director with Futurum Group, we spent less time talking about how quickly agents can act—and more time talking about who stays in control, how work actually gets done, and why intelligent apps matter more than ever. 

The future of work isn’t about humans racing to keep up with machines. It’s about humans stepping into higher-value roles—guiding, shaping, and supervising intelligent systems that operate at scale.

Intelligent apps change the role of the human

As agents become more capable, the role of the business user changes. People keep working—but they do less manual work. 

Humans are increasingly moving away from step-by-step processes to designing the flow, defining the rules, and deciding where judgment matters in their workflows. Intelligent apps become the place where all that happens. Apps are evolving, becoming intelligent, adaptive operating surfaces where people and agents work together. 

That’s an important shift. Agents are not replacing apps. Agents and apps are working together. Agents now show up inside apps, embedded in places where work already happens, with the right context, data, and governance. 

Human-in-the-loop isn’t a checkbox, it’s an imperative 

One of the most practical questions Keith raised was about control: How do organizations decide what agents can do on their own—and when humans need to step in? 

The answer is design. 

Take something like processing a request for proposal (RFP) or an insurance claim. An organization might decide that transactions under a certain threshold proceed automatically, while higher-risk cases require review. That decision goes beyond technical limits. It reflects business risk, regulatory requirements, and confidence in the process. 

The important part is this: those boundaries are intentional, adjustable, and visible. You don’t hardcode them once and walk away. You refine them as conditions change and as confidence grows. 

That’s what human-in-the-loop really means: putting judgment where it matters most. 

Automation works best when agents specialize 

Another theme we discussed was scale. As organizations move beyond single workflows, they quickly discover that one giant “do-everything” agent doesn’t hold up, and is likely not the optimal path for impact and scale.  

What does scale is multiagent orchestration. Instead of building one monolithic agent, teams break processes into smaller, specialized agents—each responsible for a specific function. One agent validates data. Another checks records. Another recommends an outcome. Humans oversee the system. 

This approach has two benefits. First, it’s more resilient. If something changes, you update one part instead of everything. Second, it creates reuse. An agent built for one process can often support others. 

That’s how automation compounds. Apps, agents, and chat each have a role. Automation works best when you match the right tool to the right task. A mobile app with a barcode scanner is faster when speed matters. A background agent is better when no interaction is needed. And chat earns its place when collaboration, clarification, or exploration is involved. Apps, agents, and chat each have a role, the key is to leverage each option where it makes the most sense. 

When these apps, chat and agents work together, work feels simpler—not more complex. This shift creates new opportunities for people. One of the most overlooked impacts of agentic automation is inclusion. 

When systems can summarize meetings, surface the right information at the right time, and reduce cognitive load, more people can contribute effectively—regardless of working style. For example, meeting transcripts can allow participants to stay fully focused on the discussion, knowing the notes will be available after the meeting. Intelligent assistance doesn’t just increase productivity. It lowers barriers. 

That matters. Not as a side benefit, but as a core outcome of better system design. You don’t plan your way into this—you learn by doing.  

The advice I keep giving customers is straightforward: Start deliberately

You can’t whiteboard every scenario. You can’t predict every edge case. You learn by deploying, observing, adjusting, and scaling—with governance in place from the beginning. 

The organizations that move fastest aren’t reckless. They’re deliberate. They build intelligent apps with clear boundaries, visibility, and accountability—and they evolve from there. 

This shift is already underway. The question isn’t whether intelligent apps and agents will change how work gets done. It’s whether you’ll design for that change—or react to it later. 

If you want to go deeper into how organizations are putting these ideas into practice and to hear how they are making deliberate choices about when to automate, or assist, or hand things back to people, I encourage you to watch my full conversation with Keith Kirkpatrick. We cover real examples, design choices, and what leaders should be thinking about next. I invite you to explore how intelligent apps, agents, and human judgment come together at work and what this could mean for your team. 

The post Intelligent apps, human leadership, and the new shape of work  appeared first on Microsoft Power Platform Blog.

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. 

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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.

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