Microsoft’s Build 2025 event carved out a pivotal moment in the evolution of developer tooling, artificial intelligence, and open interface standards. Rather than continuing its traditional developer conference script, this year’s Build turned the spotlight directly on AI agents, new open protocols, and interconnected platforms, laying out a bold vision for how software will be built, integrated, and utilized across business, research, and daily computing. The announcements—spanning from Discovery, a new research platform, to open standards like the Model Context Protocol (MCP), and hands-on AI development with Copilot Studio and NLWeb—paint a detailed roadmap for both developers and organizations navigating the AI-powered future.
At the front of this year’s innovations is Discovery, a platform that converges AI agents with Microsoft’s graph-based knowledge systems. Operated on Azure, Discovery is engineered for domains that demand rapid hypothesis generation, simulation, and multi-source analysis. The platform’s ambitious goal is to not just accelerate research cycles, but to fundamentally alter how interdisciplinary teams operate within science, pharmaceuticals, cosmetics, semiconductor design, and beyond.
Where Discovery stands out is in both its openness and its interactivity:
Practical examples shared by Microsoft include scenarios where one agent analyzes sales data, another drafts customer proposals, and a third schedules follow-up meetings—all dynamically, in response to real-world triggers or events. This collaborative agent paradigm, underpinned by an agent-to-agent protocol initially proposed by Google, hints at a broader trend toward platform-agnostic, cross-vendor agent ecosystems. Should this protocol reach widespread adoption (which remains a big “if” given competitive interests), it could enable unprecedented interoperability, allowing agents from disparate platforms (and vendors) to synchronize seamlessly.
A particularly eye-catching feature is the new “Computer Use” mode, which radically extends agents’ control beyond defined APIs. Here, agents can control desktop apps or browser-based UIs directly, interpreting task instructions and visually interacting with software much like a human user would. This not only increases automation coverage in legacy enterprise environments—where APIs may not exist—but also exposes the need for heightened security and oversight, as over-permissive agents could inadvertently trigger data leaks or workflow errors. Microsoft has acknowledged these risks and promises dedicated protections, but the full robustness of these safeguards will only become apparent as deployments scale.
Notably, Copilot Studio’s agent stack links to a growing ecosystem: companies can integrate custom AI models from the Azure AI Foundry, run custom Python analytics, and even deploy agents to WhatsApp—a nod to AI’s spread into consumer-facing communication channels.
Some notable features and implications of MCP include:
A particularly promising workflow is one where developers provide feedback, and the agent incorporates these suggestions into the next output cycle. This natural, conversational loop echoes patterns from research in human-computer interaction—yielding iterative improvements, higher-quality code, and reduced manual labor.
Microsoft also announced plans to open source Copilot for Visual Studio Code, a move likely to supercharge contributions, audits, and custom extensions globally. Provided the open source release maintains parity with the proprietary version and is not artificially restricted, this could accelerate innovation—though skepticism is warranted until actual source releases, governance models, and contribution paths are published.
Key aspects of NLWeb’s vision:
For Windows developers, the opportunity is significant: build specialized agents, integrate proprietary or external models, and participate in broader AI-powered business networks without the friction—historically—associated with cross-platform development and integration. For enterprises, the goal is to automate ever more complex tasks, shorten R&D cycles, and unlock new products—all while maintaining security and compliance.
But as with all technical revolutions, the balance between ambition and reality will be critical. Security, genuine openness, and the rate of industry adoption will determine whether Microsoft’s Build 2025 strategy establishes a sustainable foundation, or simply sparks the next standards war in agent-driven computing.
Ultimately, what is clear from this year’s Build is that the “developer” now extends beyond humans writing code. Increasingly, developers will build, steer, and supervise intelligent agents—while relying on new tools, protocols, and platforms to ensure these agents are helpful, safe, and robust. Microsoft’s roadmap is bold, and the repercussions for Windows, Azure, and the broader software industry will be felt for years to come.
Source: the-decoder.com Microsoft Build 2025 showcases new AI agent tools and open interfaces for developers
At the front of this year’s innovations is Discovery, a platform that converges AI agents with Microsoft’s graph-based knowledge systems. Operated on Azure, Discovery is engineered for domains that demand rapid hypothesis generation, simulation, and multi-source analysis. The platform’s ambitious goal is to not just accelerate research cycles, but to fundamentally alter how interdisciplinary teams operate within science, pharmaceuticals, cosmetics, semiconductor design, and beyond.Where Discovery stands out is in both its openness and its interactivity:
- Researchers are empowered to create specialized agents tuned for narrow tasks (e.g., simulating chemical behavior, reviewing massive troves of literature, or running complex data analyses),
- The entire orchestration can be managed through natural language commands—no traditional programming required, lowering the bar for entry and dramatically accelerating iterative research,
- The system supports deep customization so agents can operate within unique research workflows, effectively personalizing automation for enterprises and advanced researchers.
Copilot Studio’s Multi-Agent Capabilities: Automation at Scale
Another significant stride from Build 2025 is the expansion of Copilot Studio with multi-agent support. This upgrade means that developers and businesses can now orchestrate entire workflows where AI agents collaborate, pass tasks between one another, and address multi-step processes with minimal human intervention.Practical examples shared by Microsoft include scenarios where one agent analyzes sales data, another drafts customer proposals, and a third schedules follow-up meetings—all dynamically, in response to real-world triggers or events. This collaborative agent paradigm, underpinned by an agent-to-agent protocol initially proposed by Google, hints at a broader trend toward platform-agnostic, cross-vendor agent ecosystems. Should this protocol reach widespread adoption (which remains a big “if” given competitive interests), it could enable unprecedented interoperability, allowing agents from disparate platforms (and vendors) to synchronize seamlessly.
A particularly eye-catching feature is the new “Computer Use” mode, which radically extends agents’ control beyond defined APIs. Here, agents can control desktop apps or browser-based UIs directly, interpreting task instructions and visually interacting with software much like a human user would. This not only increases automation coverage in legacy enterprise environments—where APIs may not exist—but also exposes the need for heightened security and oversight, as over-permissive agents could inadvertently trigger data leaks or workflow errors. Microsoft has acknowledged these risks and promises dedicated protections, but the full robustness of these safeguards will only become apparent as deployments scale.
Notably, Copilot Studio’s agent stack links to a growing ecosystem: companies can integrate custom AI models from the Azure AI Foundry, run custom Python analytics, and even deploy agents to WhatsApp—a nod to AI’s spread into consumer-facing communication channels.
The Dual Foundry Approach: Windows and Azure AI Foundry
Microsoft made clear it does not intend to cede ground in either edge or cloud AI computing. By introducing Windows AI Foundry alongside Azure AI Foundry, the company sets out a dual-rail strategy for AI model development and deployment:- Windows AI Foundry empowers developers to run and test AI models directly on Windows devices, using local hardware (with support for CPU, GPU, and NPU detection). This is particularly relevant for privacy-sensitive or resource-constrained scenarios, where data and models should remain on-device. While mostly Windows-focused, there’s some support for macOS—a rare move for Microsoft, suggesting an intent for wider developer reach.
- Azure AI Foundry recruits the scale and centralized management capabilities of Microsoft’s cloud. Organizations can host, train, and serve proprietary and external models (including notable names like DeepSeek R1 and, more recently, Grok 3 from xAI), leveraging them in production systems, research infrastructure, or automation suites like Copilot Studio and Discovery.
The Model Context Protocol: Toward Open, Secure AI Interoperability
At the infrastructure level, Microsoft is betting big on standards. The new Model Context Protocol (MCP), which originated with Anthropic and is now promoted as an open standard, aims to become the universal connector for AI agents, apps, and services. Much as USB-C unified device connections in hardware, MCP aspires to unify context and data exchange in software-based agent environments.Some notable features and implications of MCP include:
- A Windows MCP registry acts as the central clearinghouse for AI agents, interfaces, and services—ambitiously positioning Windows as an “AI bus” for next-generation workflows.
- MCP is designed for openness, supporting integration of both Microsoft and third-party models, and facilitating agent “handshakes” across app and organizational boundaries.
- Microsoft has acknowledged significant security challenges—most notably token theft and prompt injection, which are endemic risks in agent-driven architectures. The company claims to have dedicated safeguards in development; however, until the preview widens beyond select developers and independent audits are conducted, CIOs and CISOs should treat these assurances as intentions rather than guarantees.
AI Agents in Software Development: GitHub Copilot and Open Sourcing
Microsoft doubled down on its vision that AI agents will not just supplement but actively participate in software engineering. Updates to GitHub Copilot suggest the tool is becoming increasingly autonomous—capable of parsing repositories, proposing and committing bug fixes, developing features, and updating documentation. Every step is logged and reviewed, maintaining a developer-in-the-loop posture for accountability, not unlike the release strategy seen recently from OpenAI’s Codex.A particularly promising workflow is one where developers provide feedback, and the agent incorporates these suggestions into the next output cycle. This natural, conversational loop echoes patterns from research in human-computer interaction—yielding iterative improvements, higher-quality code, and reduced manual labor.
Microsoft also announced plans to open source Copilot for Visual Studio Code, a move likely to supercharge contributions, audits, and custom extensions globally. Provided the open source release maintains parity with the proprietary version and is not artificially restricted, this could accelerate innovation—though skepticism is warranted until actual source releases, governance models, and contribution paths are published.
NLWeb: Opening the Web to Natural Language and Agents
The unveiling of NLWeb represents an ambitious step toward melding traditional web content with the agent-driven internet. Touted as an open-source “natural language interface” layer, NLWeb empowers website owners to offer natural language access to their data via both human users and AI agents. The platform works with structured data sources—such as Schema.org metadata, RSS feeds, and JSONL files—making underlying site data accessible in conversational formats.Key aspects of NLWeb’s vision:
- Website operators can choose any AI model, enabling customization, privacy controls, and model diversity.
- Each NLWeb instance can function as an MCP server, federating its content into the broader agent-powered web. This could, in time, enable machine agents to navigate, consume, and act on web content much like today’s users or scrapers—but with richer semantic understanding.
- Microsoft posits NLWeb as a potential standard for the agent-powered web, akin to the original impact of HTML. If even a modest fraction of major sites adopt NLWeb, the implications for search, aggregation, and digital assistants would be profound—potentially enabling precise, context-aware answers and workflows far beyond today’s search engines.
Strengths, Ambitions, and Strategic Risks
Strengths
- Developer Empowerment: By combining low-code/no-code agent interfaces (Discovery, Copilot Studio) with deep technical hooks (Foundry and MCP), Microsoft positions itself as an enabler for a wide array of developer types—from citizen automators to AI specialists.
- Openness and Standards: Continued support for open standards (agent-to-agent protocol, MCP, NLWeb, open-sourced Copilot for VS Code) reduces vendor lock-in, attracts broader ecosystem participation, and diffuses competitive “walled garden” approaches. This could be foundational for healthy AI innovation moving forward.
- Cloud-Edge Synergy: The dual approach (Windows and Azure Foundries) addresses needs across the spectrum: privacy and latency for edge devices, power and scalability for the cloud.
Risks and Questions
- Security and Oversight: The very flexibility and reach that make agent-based automation powerful also increase potential attack surfaces. Agents—especially those granted UI or system-level controls—could be misused, inadvertently trigger failures, or become targets for adversarial input.
- Interoperability and Fragmentation: While MCP and the agent-to-agent protocol are commendable, industry history (think messaging, IoT standards) suggests adoption is never guaranteed. Competing standards from other hyperscalers or regulatory roadblocks could splinter the ecosystem.
- Real-World Performance: Much of Build 2025’s showcase is aspirational—demoed workflows and preview features rather than widely deployed solutions. Enterprises contemplating adoption should demand detailed case studies and proof points, particularly regarding model migration (Windows Foundry to Azure), agent-driven compliance, and operational resilience.
- Open Source Questions: Microsoft’s promise to open source key agent components is laudable, but unless these releases are timely, complete, and foster active external governance, skepticism is warranted. The open source community will quickly judge whether these moves are strategic PR, or true commitments to transparency and auditability.
Looking Ahead: Agentic Futures and the Next Web
Microsoft Build 2025 leaves no doubt that the company is betting the future on agent-driven computing—where software learns, acts, communicates, and adapts with minimal human wiring. The convergence of AI agents with open protocols, seamless cloud/edge interplay, and natural language interfaces signals an inflection point reminiscent of the internet’s early days, when protocols like HTTP and HTML unlocked waves of innovation.For Windows developers, the opportunity is significant: build specialized agents, integrate proprietary or external models, and participate in broader AI-powered business networks without the friction—historically—associated with cross-platform development and integration. For enterprises, the goal is to automate ever more complex tasks, shorten R&D cycles, and unlock new products—all while maintaining security and compliance.
But as with all technical revolutions, the balance between ambition and reality will be critical. Security, genuine openness, and the rate of industry adoption will determine whether Microsoft’s Build 2025 strategy establishes a sustainable foundation, or simply sparks the next standards war in agent-driven computing.
Ultimately, what is clear from this year’s Build is that the “developer” now extends beyond humans writing code. Increasingly, developers will build, steer, and supervise intelligent agents—while relying on new tools, protocols, and platforms to ensure these agents are helpful, safe, and robust. Microsoft’s roadmap is bold, and the repercussions for Windows, Azure, and the broader software industry will be felt for years to come.
Source: the-decoder.com Microsoft Build 2025 showcases new AI agent tools and open interfaces for developers