In the early months of this year, Microsoft’s Build developer conference set a clear strategic direction: embracing not just AI as a set of tools, but an ecosystem of agents fundamental to the next phase of both software development and the broader internet. This shift, while gradual in public narrative, lands as a pivotal moment in the ongoing collision between the enterprise, open-source collaboration, and the fast-evolving role of artificial intelligence across every layer of the web and the modern workforce.
The concept of AI agents is not new. What’s novel—and notable—is the scale and integration now on display. Microsoft, leveraging its cloud, platform foothold, and deep partnerships, has begun to position these agents as integral to professional workflows as, say, the graphical user interface or Internet protocols were in previous waves.
According to the company’s latest figures, over 15 million developers are now utilizing GitHub Copilot, taking advantage of agent modes and AI-driven code review to streamline development and deployment lifecycles. This is more than an isolated statistic: it marks a profound change in how code is conceived and evolved, underscoring the symbiotic relationship between developer productivity and AI augmentation.
Similarly, more than 230,000 organizations—including an impressive 90% of the Fortune 500—have adopted Copilot Studio to build and deploy tailored AI agents and automations. Microsoft 365 Copilot is broadening its reach, helping hundreds of thousands of users tackle daily tasks in research, brainstorming, and solution development. All of this points to an industry on the cusp of major transformation, with enterprise-scale adoption proving out what was, until quite recently, still largely experimental.
There’s an unmistakable analogy here to the open standards and protocols that defined the early web, suggesting both opportunity and risk: will this new ecosystem remain as open and interoperable as Microsoft claims, or repeat the cycles of fragmentation that have dogged previous advances? Microsoft’s public commitment to open protocols, shared infrastructure, and collaborative development is encouraging, but as with any industry defining its own future, skepticism remains warranted—especially as commercial incentives guide development priorities.
Scalability is a recurring theme; Microsoft boasts an ability to support deployments running from individual workstations up to vast cloud clusters, all while integrating with the security and compliance tooling demanded by the modern enterprise. By reducing start-up friction and enabling immediate experimentation with prebuilt models—or highly customized solutions—Foundry establishes a new baseline for developer productivity.
This effort dovetails with related upgrades to Azure AI Foundry, which now aggregates more than 1,900 Microsoft- and partner-hosted AI models, including the provision to host Grok 3 and Grok 3 Mini models from xAI within its architecture. A highlight here is the Model Leaderboard, a transparent way to benchmark and compare model performance across a spectrum of tasks—something that’s been missing from much proprietary AI tooling to date, and a move that could encourage competition and improve overall quality for end-users.
The announcement that GitHub Copilot Chat in Visual Studio Code will move to open-source is significant for two reasons. First, it signals Microsoft’s commitment to transparency and extensibility, which is vital for institutional trust and developer adoption. Second, it enshrines a model of collaborative evolution for coding tools that, if widely adopted, could erase many of the pain points of proprietary limitation that have dogged productivity tools in the past.
The implications for workflow are broad. Teams can now manage prompt libraries, run model evaluations, and externally integrate controls—all within GitHub’s familiar environment. This contextual, in-platform AI presence offers a glimpse of what future IDEs might look like: AI-native, highly extensible, and community-driven.
This approach, if implemented robustly, could help organizations avoid the notorious “shadow IT” dilemma and ensure that every AI-driven workflow is properly accounted for, auditable, and compliant from the start.
Additionally, with agent orchestration features such as Semantic Kernel and AutoGen now unified into a consolidated SDK, and Model Context Protocol (MCP) support rolled out across platforms, developers can build, connect, and monitor multi-agent systems with a level of control impossible just a few years ago.
Yet, some in the development community note that independent, cross-platform validation mechanisms for these observability metrics are lacking. Although Microsoft’s solution is comprehensive for those within its ecosystem, organizations mixing and matching tooling across vendors or open-source stacks may find they need to engineer additional validation processes.
MCP is particularly powerful when paired with new authorization specifications, which allow users to grant agent and AI applications access to data and services with their existing sign-in methods. Microsoft’s announcement of a public MCP server registry further boosts transparency and discoverability.
Of particular interest is the newly announced NLWeb project: an open standard reminiscent of what HTML did for the early web, but for conversational interfaces. NLWeb endpoints are also MCP servers, enabling websites to expose their content and services to agentic access. If the project gains broad adoption, it could accelerate the creation of web experiences where human-AI collaboration is first-class and seamless. Still, the success of NLWeb will almost certainly depend on uptake by other major tech players and standards bodies; Microsoft alone cannot determine its fate.
Furthermore, the new multi-agent orchestration within Copilot Studio enables the kind of skill composition and hand-off that complex, integrated business processes require. Instead of a single agent performing isolated tasks, clusters of agents can coordinate across domains, each bringing specialized capability but sharing contextually significant data and permissions.
Microsoft claims these features tide well with its security posture: the agents operate within the Microsoft 365 service boundary, inheriting established compliance and governance models. However, as some security researchers note, even the most rigorously designed agent boundaries may face unexpected interactions—especially as the complexity and autonomy of agent orchestration grows. It is an area that warrants continued independent scrutiny, especially as financial and legal operations become more agent-mediated.
Complementing the leaderboard is a new Model Router, designed to select the optimal model for specific queries or tasks in real-time. This is particularly useful as organizations often have access to dozens, if not hundreds, of models—both custom and curated from third-party providers. The router’s efficiency and neutrality will be closely watched, as any bias towards Microsoft’s own models over external competitors could undermine the integrity of its open ecosystem posture.
Bringing agent-based orchestration to science promises to accelerate both the time-to-market for new products and the speed at which new hypotheses are tested and validated. Yet, as with all innovation targeting sensitive, high-stakes domains like healthcare and drug development, independent validation and verification will be paramount. Organizations betting heavily on Discovery should insist on clear transparency both in model training and real-world performance evaluation.
For organizations, the challenge and promise are two sides of the same coin: build faster, smarter, with agents that not only understand your intent but actively reshape the fabric of how work is done. Yet, success will depend not just on Microsoft’s stewardship, but on the consistent, robust participation of the wider developer and scientific communities—and on vigilant oversight to ensure that claims of openness, transparency, and security are met in practice, not just in press releases.
The open agentic web is emerging. Whether it will deliver on its inclusive, interoperable promise—or fall prey to the same pitfalls of earlier tech revolutions—will depend on actions, not aspirations, in the months and years ahead. For those building, leading, and using these tools, the time for experimentation and informed advocacy is now.
Source: The Official Microsoft Blog Microsoft Build 2025: The age of AI agents and building the open agentic web - The Official Microsoft Blog
Entering the Era of AI Agents
The concept of AI agents is not new. What’s novel—and notable—is the scale and integration now on display. Microsoft, leveraging its cloud, platform foothold, and deep partnerships, has begun to position these agents as integral to professional workflows as, say, the graphical user interface or Internet protocols were in previous waves.According to the company’s latest figures, over 15 million developers are now utilizing GitHub Copilot, taking advantage of agent modes and AI-driven code review to streamline development and deployment lifecycles. This is more than an isolated statistic: it marks a profound change in how code is conceived and evolved, underscoring the symbiotic relationship between developer productivity and AI augmentation.
Similarly, more than 230,000 organizations—including an impressive 90% of the Fortune 500—have adopted Copilot Studio to build and deploy tailored AI agents and automations. Microsoft 365 Copilot is broadening its reach, helping hundreds of thousands of users tackle daily tasks in research, brainstorming, and solution development. All of this points to an industry on the cusp of major transformation, with enterprise-scale adoption proving out what was, until quite recently, still largely experimental.
Building the Open Agentic Web: Microsoft’s Strategic Vision
The phrase “open agentic web” featured prominently in Microsoft’s keynote vision this year. It refers to a future in which AI agents act not just as passive assistants, but as empowered entities capable of performing tasks, making decisions, and seamlessly collaborating across individual, team, organizational, and even end-to-end business contexts.There’s an unmistakable analogy here to the open standards and protocols that defined the early web, suggesting both opportunity and risk: will this new ecosystem remain as open and interoperable as Microsoft claims, or repeat the cycles of fragmentation that have dogged previous advances? Microsoft’s public commitment to open protocols, shared infrastructure, and collaborative development is encouraging, but as with any industry defining its own future, skepticism remains warranted—especially as commercial incentives guide development priorities.
Windows AI Foundry: Closing the Developer Experience Loop
One of the conference’s standout announcements was Windows AI Foundry, positioned as the most flexible, reliable, and scalable AI developer platform available for both training and inference. Historically, Windows has enjoyed a reputation as a “developer’s platform,” and AI Foundry extends that promise: with unified APIs for vision and language, the platform lets developers work with open source LLMs natively or bring proprietary models to train, fine-tune, and deploy, whether locally or in the cloud.Scalability is a recurring theme; Microsoft boasts an ability to support deployments running from individual workstations up to vast cloud clusters, all while integrating with the security and compliance tooling demanded by the modern enterprise. By reducing start-up friction and enabling immediate experimentation with prebuilt models—or highly customized solutions—Foundry establishes a new baseline for developer productivity.
This effort dovetails with related upgrades to Azure AI Foundry, which now aggregates more than 1,900 Microsoft- and partner-hosted AI models, including the provision to host Grok 3 and Grok 3 Mini models from xAI within its architecture. A highlight here is the Model Leaderboard, a transparent way to benchmark and compare model performance across a spectrum of tasks—something that’s been missing from much proprietary AI tooling to date, and a move that could encourage competition and improve overall quality for end-users.
GitHub Copilot: From Editor Assistant to Agentic Partner
Another focal point is the ongoing evolution of GitHub Copilot. Far from the simple autocomplete assistant that debuted in 2021, Copilot is now transitioning into a robust AI “agent”—capable of not just suggesting code but automating and evaluating entire coding tasks, integrating best-in-class models, and offering far more granular enterprise controls.The announcement that GitHub Copilot Chat in Visual Studio Code will move to open-source is significant for two reasons. First, it signals Microsoft’s commitment to transparency and extensibility, which is vital for institutional trust and developer adoption. Second, it enshrines a model of collaborative evolution for coding tools that, if widely adopted, could erase many of the pain points of proprietary limitation that have dogged productivity tools in the past.
The implications for workflow are broad. Teams can now manage prompt libraries, run model evaluations, and externally integrate controls—all within GitHub’s familiar environment. This contextual, in-platform AI presence offers a glimpse of what future IDEs might look like: AI-native, highly extensible, and community-driven.
Strengthening Security and Governance in the Age of AI Agents
One of the recurring criticisms of rapid AI adoption is the risk of agent sprawl and associated governance. Microsoft’s response comes in the form of the new Entra Agent ID, which automatically assigns unique, directory-based identities to agents created using Microsoft Copilot Studio or Azure AI Foundry. These identities are then governed via Microsoft’s Purview platform, which brings established enterprise strengths—fine-grained security, compliance controls, and automated policy enforcement—right into the heart of the agent development and deployment process.This approach, if implemented robustly, could help organizations avoid the notorious “shadow IT” dilemma and ensure that every AI-driven workflow is properly accounted for, auditable, and compliant from the start.
Additionally, with agent orchestration features such as Semantic Kernel and AutoGen now unified into a consolidated SDK, and Model Context Protocol (MCP) support rolled out across platforms, developers can build, connect, and monitor multi-agent systems with a level of control impossible just a few years ago.
Discoverability, Observability, and Compliance
As agents proliferate, understanding and auditing their actions becomes ever more critical. Microsoft is addressing this with Azure AI Foundry Observability, which merges metrics on performance, quality, cost, and safety into a unified dashboard. This service also offers detailed tracing, allowing organizations to trace back specific agent decisions and system behaviors. It’s a crucial hallmark for both regulatory compliance and internal accountability.Yet, some in the development community note that independent, cross-platform validation mechanisms for these observability metrics are lacking. Although Microsoft’s solution is comprehensive for those within its ecosystem, organizations mixing and matching tooling across vendors or open-source stacks may find they need to engineer additional validation processes.
The Open Protocol Push: Model Context Protocol and NLWeb
A foundational tenet of Microsoft’s vision is the commitment to open standards that increase agent interoperability. The Model Context Protocol (MCP) is being rolled out across GitHub, Copilot Studio, Dynamics 365, Azure AI Foundry, Semantic Kernel, and even Windows 11. Participation in the MCP Steering Committee is framed as evidence of a serious intent to avoid “walled garden” pitfalls.MCP is particularly powerful when paired with new authorization specifications, which allow users to grant agent and AI applications access to data and services with their existing sign-in methods. Microsoft’s announcement of a public MCP server registry further boosts transparency and discoverability.
Of particular interest is the newly announced NLWeb project: an open standard reminiscent of what HTML did for the early web, but for conversational interfaces. NLWeb endpoints are also MCP servers, enabling websites to expose their content and services to agentic access. If the project gains broad adoption, it could accelerate the creation of web experiences where human-AI collaboration is first-class and seamless. Still, the success of NLWeb will almost certainly depend on uptake by other major tech players and standards bodies; Microsoft alone cannot determine its fate.
Microsoft 365 Copilot: Tuning and Multi-Agent Orchestration
Productivity suites remain the center of gravity for enterprise AI adoption, and Microsoft 365 Copilot is quickly becoming more than a static assistant. With the new Copilot Tuning capabilities, organizations can now tailor models using proprietary workflows and datasets, all within a low-code environment. This means businesses—from law firms to consultancies—are equipped to create agents attuned to their own expertise, compliance, and style conventions.Furthermore, the new multi-agent orchestration within Copilot Studio enables the kind of skill composition and hand-off that complex, integrated business processes require. Instead of a single agent performing isolated tasks, clusters of agents can coordinate across domains, each bringing specialized capability but sharing contextually significant data and permissions.
Microsoft claims these features tide well with its security posture: the agents operate within the Microsoft 365 service boundary, inheriting established compliance and governance models. However, as some security researchers note, even the most rigorously designed agent boundaries may face unexpected interactions—especially as the complexity and autonomy of agent orchestration grows. It is an area that warrants continued independent scrutiny, especially as financial and legal operations become more agent-mediated.
Azure AI Foundry and Model Evaluation
Azure AI Foundry’s unified platform now supports advanced model customization, secure data integration, and enterprise-grade governance. A standout for developers is the Model Leaderboard—a transparent, up-to-date ranking of top-performing AI models across different categories and business tasks. This not only helps organizations select fit-for-purpose models but also increases pressure on vendors to continually improve quality, transparency, and safety standards.Complementing the leaderboard is a new Model Router, designed to select the optimal model for specific queries or tasks in real-time. This is particularly useful as organizations often have access to dozens, if not hundreds, of models—both custom and curated from third-party providers. The router’s efficiency and neutrality will be closely watched, as any bias towards Microsoft’s own models over external competitors could undermine the integrity of its open ecosystem posture.
Accelerating Scientific Discovery: Microsoft Discovery
While most media coverage will inevitably focus on business productivity and developer workflows, Microsoft also made a notable play toward the scientific community with the introduction of Microsoft Discovery. This extensible platform aims to transform research and development processes with agentic AI, from pharmaceutical R&D to sustainability studies.Bringing agent-based orchestration to science promises to accelerate both the time-to-market for new products and the speed at which new hypotheses are tested and validated. Yet, as with all innovation targeting sensitive, high-stakes domains like healthcare and drug development, independent validation and verification will be paramount. Organizations betting heavily on Discovery should insist on clear transparency both in model training and real-world performance evaluation.
Strengths, Caveats, and the Road Ahead
Microsoft’s announcements at Build depict an ecosystem in transition, where the lines between development, deployment, and day-to-day productivity are blurring under the influence of ever-smarter, more collaborative AI agents. The company’s strengths are clear:- Deep platform integration across Windows, Azure, and Microsoft 365
- A strong, transparent focus on open agentic web standards (MCP, NLWeb)
- A demonstrable commitment to developer empowerment, with open-source contributions and extensibility front-and-center
- Emphasis on enterprise-grade security, governance, and compliance frameworks
- The risk of over-centralized control, even within a so-called open ecosystem
- The practical challenge of integrating open protocols with proprietary cloud infrastructure
- Potential security and privacy challenges as multi-agent orchestration increases in sophistication and autonomy
- Ensuring transparent and equitable model ranking and routing, especially as more models enter the ecosystem
Conclusion: An Inflection Point for Developers and the Web
Microsoft Build 2025 may be remembered as the moment the company (and perhaps, by extension, much of the enterprise tech sector) doubled down on agents as foundational to the future of software, services, and the internet itself. For developers, the opportunity lies in unfettered access to powerful agent tools, transparent standards, and rich models—paired with clear accountability frameworks and an open-source ethos.For organizations, the challenge and promise are two sides of the same coin: build faster, smarter, with agents that not only understand your intent but actively reshape the fabric of how work is done. Yet, success will depend not just on Microsoft’s stewardship, but on the consistent, robust participation of the wider developer and scientific communities—and on vigilant oversight to ensure that claims of openness, transparency, and security are met in practice, not just in press releases.
The open agentic web is emerging. Whether it will deliver on its inclusive, interoperable promise—or fall prey to the same pitfalls of earlier tech revolutions—will depend on actions, not aspirations, in the months and years ahead. For those building, leading, and using these tools, the time for experimentation and informed advocacy is now.
Source: The Official Microsoft Blog Microsoft Build 2025: The age of AI agents and building the open agentic web - The Official Microsoft Blog
