Microsoft’s annual Build conference has long been a window into the company’s vision for the future of software development. This year, against the backdrop of a rapidly evolving AI landscape, the conference spotlighted a determined push to make the development and deployment of intelligent agents—and the open agentic web—not only feasible, but inviting for every developer. As CEO Satya Nadella put it in his opening keynote, Microsoft's goal is “simple: help every developer build apps and agents that empower people and organisations everywhere.” That ambition forms the backbone of a series of strategic announcements, technology rollouts, and shifts in developer tooling meant to democratize the capabilities formerly reserved for research labs and elite engineering teams.
Nadella’s remarks at Microsoft Build were more than just rhetorical flourishes; they were a clarion call for developers to embrace AI not as an add-on, but as a core collaborator. At the heart of this vision is Microsoft’s new asynchronous coding agent, now embedded directly into GitHub. This tool represents a potent evolution in the way developers interact with AI—far from the clunky, “bolt-on” experiences of early code assistants. Here, the coding agent operates natively, helping developers fix bugs, craft new features, and manage ongoing code maintenance—all in the flow of existing work, and all with a surprising degree of autonomy.
The agent functions almost like a junior developer embedded in a team, churning through issues, proposing code solutions, and handling the monotonous aspects of maintenance. By remaining within GitHub’s familiar interface, the agent avoids context switching, which studies show can sap cognitive energy and productivity. Microsoft claims developers can “experiment with models without leaving the platform,” suggesting a seamlessness that addresses one of the longstanding gripes within the coding community: tool fragmentation.
However, while the integration is impressive, critical voices have raised concerns about over-reliance on autonomous systems—particularly when it comes to managing legacy code or complex architectural changes. Microsoft emphasizes developer oversight, but the degree to which this “agentic” approach can handle nuanced business logic or deep performance debugging remains to be seen.
AI Foundry offers a unified platform to support AI developers across the key dimensions of model training, inference, deployment, and monitoring. Its flagship features include:
However, with escalation in AI adoption comes corresponding concerns about model drift, data leakage, and emerging attack vectors. While Microsoft is leaning heavily into security and governance tooling, critical observers argue that the actual effectiveness of these measures will depend on how they’re implemented in the field—particularly in hybrid cloud environments and when updates must be applied across thousands of endpoints.
With over 1,900 AI models available through its network, Microsoft offers developers granular control over customization, data residency, and governance options. Administrators can enforce compliance policies, set data boundaries, and utilize advanced monitoring right from the outset.
This strategy serves the dual imperative of enterprise readiness and individual developer creativity. Enterprises can standardize on robust, managed tooling for sensitive workloads, while hobbyists and startups benefit from access to leading-edge open models.
However, this balancing act comes with inherent risks. The open model approach can expose developers to a wider range of potential vulnerabilities—both in terms of intellectual property (IP) concerns and supply chain security threats. Microsoft’s promise to “securely manage data, model customisation and governance” will need to be constantly reinforced by prompt security updates and transparent, third-party audits.
If these tools and standards gain traction, developers everywhere stand to reap enormous productivity gains and creative freedom. The ability to customize, deploy, and govern advanced AI at scale—without specialist expertise—could unlock breakthroughs in fields as diverse as medicine, logistics, and education.
Yet, the vision remains contingent on a delicate balance: fostering openness, ensuring security, maintaining control, and nurturing a culture of responsible innovation. Microsoft’s new offerings are bold steps forward. Whether they usher in an agentic web that lives up to the hype, or falter on the shoals of complexity and risk, will depend on the collective will—and wisdom—of the global developer community. For now, the future looks both exhilarating and uncertain, with Microsoft inviting builders everywhere to “experiment, create, and empower.” The open agentic web is just beginning to take shape.
Source: Technology Record Microsoft to ‘help every developer build apps and agents that empower people and organisations everywhere’
Reimagining Developer Productivity: Agents, Autonomy, and Coding Flow
Nadella’s remarks at Microsoft Build were more than just rhetorical flourishes; they were a clarion call for developers to embrace AI not as an add-on, but as a core collaborator. At the heart of this vision is Microsoft’s new asynchronous coding agent, now embedded directly into GitHub. This tool represents a potent evolution in the way developers interact with AI—far from the clunky, “bolt-on” experiences of early code assistants. Here, the coding agent operates natively, helping developers fix bugs, craft new features, and manage ongoing code maintenance—all in the flow of existing work, and all with a surprising degree of autonomy.The agent functions almost like a junior developer embedded in a team, churning through issues, proposing code solutions, and handling the monotonous aspects of maintenance. By remaining within GitHub’s familiar interface, the agent avoids context switching, which studies show can sap cognitive energy and productivity. Microsoft claims developers can “experiment with models without leaving the platform,” suggesting a seamlessness that addresses one of the longstanding gripes within the coding community: tool fragmentation.
However, while the integration is impressive, critical voices have raised concerns about over-reliance on autonomous systems—particularly when it comes to managing legacy code or complex architectural changes. Microsoft emphasizes developer oversight, but the degree to which this “agentic” approach can handle nuanced business logic or deep performance debugging remains to be seen.
Windows AI Foundry: The Unifying Force for App and Agent Development
Beyond just providing yet another toolkit, Microsoft’s Windows AI Foundry is positioned as a cornerstone for developer empowerment. In Nadella’s words: “Foundry is the complete app platform for building apps and agents.” With this declarative, Microsoft is selling more than a product; it’s selling an ecosystem unified under principles of openness, extensibility, and enterprise-grade reliability.AI Foundry offers a unified platform to support AI developers across the key dimensions of model training, inference, deployment, and monitoring. Its flagship features include:
- Training and Inference: Developers can choose to work with open-source large language models (LLMs) directly within the Foundry environment or bring their own proprietary models for conversion and deployment.
- Deployment Flexibility: Whether the intended environment is a local machine via Foundry Local or a global audience in the cloud, the platform strives to optimize for both convenience and scalability.
- Extensive Model Support: Microsoft is expanding native support for models from major open-source players, including Grok, Hugging Face, Meta, and Mistral, alongside its own offerings. This gives developers access to over 1,900 partner-hosted and Microsoft-hosted AI models, making Foundry one of the broadest model catalogs available for secure experimentation and customization.
Azure AI Foundry Agent Service and Observability: Scaling up Security, Governance, and Performance
To complement the foundation laid by Windows AI Foundry, Microsoft is rolling out the Azure AI Foundry Agent Service—a managed platform offering specialized AI agents for a range of tasks, from customer service augmentation to complex data processing. The general availability of this service marks a pivotal step towards mainstream adoption of agentic workflows by providing:- Specialized, Task-Oriented Agents: These are not generic bots, but focused agents designed for deep integration within enterprise workflows.
- End-to-End Observability: The new suite of observability features lets teams monitor metrics like performance, quality, cost, and safety. This visibility is crucial not just for technical tuning, but also for compliance management and risk mitigation.
However, with escalation in AI adoption comes corresponding concerns about model drift, data leakage, and emerging attack vectors. While Microsoft is leaning heavily into security and governance tooling, critical observers argue that the actual effectiveness of these measures will depend on how they’re implemented in the field—particularly in hybrid cloud environments and when updates must be applied across thousands of endpoints.
Embracing Open Standards: Model Context Protocol and NLWeb
One of the most anticipated announcements at Build is Microsoft’s support for new open standards aimed at bridging the chasm between AI-powered apps and the broader web ecosystem.- Model Context Protocol (MCP): This open standard lets apps and assistants tap into trusted services—such as files, data subscriptions, and third-party APIs—through secure, familiar authentication flows. The goal is to empower users to leverage their data across AI experiences without repeated consent prompts or clunky integrations. As organizations increasingly straddle multiple platforms and clouds, protocols like MCP offer the promise of seamless, secure interoperability.
- NLWeb: Standing for “Natural Language Web,” NLWeb is Microsoft’s initiative to make web content directly accessible to AI agents. Rather than scraping sites or relying on brittle APIs, NLWeb enables two-way, natural language conversations between websites and AI models. For developers, this means a straightforward way to script interactions—orders, bookings, basic support—using standard web endpoints. For end users, it heralds a web that’s more accessible, interactive, and, ultimately, conversational.
Cross-Platform Intelligence: Combining Enterprise Security with Open Innovation
It’s clear that Microsoft is positioning itself as both a steward of enterprise security and a champion for open, community-driven development. The integration of multiple leading models—from Meta’s Llama to Mistral and Hugging Face—underscores an industry shift away from single-vendor AI pipelines.With over 1,900 AI models available through its network, Microsoft offers developers granular control over customization, data residency, and governance options. Administrators can enforce compliance policies, set data boundaries, and utilize advanced monitoring right from the outset.
This strategy serves the dual imperative of enterprise readiness and individual developer creativity. Enterprises can standardize on robust, managed tooling for sensitive workloads, while hobbyists and startups benefit from access to leading-edge open models.
However, this balancing act comes with inherent risks. The open model approach can expose developers to a wider range of potential vulnerabilities—both in terms of intellectual property (IP) concerns and supply chain security threats. Microsoft’s promise to “securely manage data, model customisation and governance” will need to be constantly reinforced by prompt security updates and transparent, third-party audits.
Potential Strengths: Accelerating Adoption and Empowerment
Microsoft’s multi-pronged strategy—spanning developer productivity, platform unification, open standards, and security—presents several notable strengths:- Lower Barrier to Entry: By integrating intuitive agentic tools directly into GitHub and standard Windows environments, even novice developers can access powerful AI capabilities without wrestling with complex setup processes.
- Flexible Architecture: The ability to bring your own model, work locally or in the cloud, and interoperate across platforms makes Foundry appealing to a wide array of stakeholders—from scrappy startups building the next breakout app, to Fortune 500s transforming their core business processes.
- Ecosystem Magnetism: By embracing open models and standards, Microsoft positions itself not as a gatekeeper, but as a facilitator of broader industry innovation. This could help sidestep some of the regulatory scrutiny that’s begun to cluster around “walled gardens” in AI.
- Enterprise Trust: With rigorous observability features and deep security integrations, Microsoft is sending a clear signal to enterprise IT leaders: adopting AI doesn’t mean losing control or visibility.
Key Risks and Open Questions: Security, Autonomy, and the Pace of Change
Notwithstanding all the promise, Microsoft’s agentic vision comes with real risks and uncertainties:- Security Threats to the Agentic Web: Open models and conversational interfaces significantly expand the potential attack surface. Novel exploits targeting AI agents, data flows, or protocol implementations could lead to costly breaches. The efficacy of Microsoft’s controls will be tested as adoption broadens.
- Oversight and Autonomy: As AI agents take on greater responsibilities, ensuring that developer intent aligns with agent action is non-trivial. Unintended consequences—from minor logic bugs to catastrophic system outages—remain a threat, especially when AI-driven code changes propagate rapidly through CI/CD pipelines.
- Standards Adoption: MCP and NLWeb, though promising, risk irrelevance if not embraced by other developers, browser vendors, and competing cloud providers. The tech industry’s history is littered with technically superior, but under-adopted standards.
- Hidden Costs and Lock-in: While Foundry promises openness, actual switching costs for large enterprises—once deeply reliant on Microsoft’s management stack—may remain significant. The fine print of licensing, data egress, and premium features could erode the headline narrative of “openness.”
- Regulatory Backlash: The growing political scrutiny on AI tools—spanning privacy, bias, and economic impact—means that even well-governed agentic platforms could suddenly face new compliance burdens.
The Road Ahead: An Agentic Web, If We Can Build It
Microsoft’s Build announcements reflect a fundamental recalibration in the relationship between humans, AI, and the software that powers modern life. The focus is no longer just on building smarter applications, but on engineering a web of intelligent agents—each one capable of transforming raw data, automating routine work, and extending human potential in previously unimaginable ways.If these tools and standards gain traction, developers everywhere stand to reap enormous productivity gains and creative freedom. The ability to customize, deploy, and govern advanced AI at scale—without specialist expertise—could unlock breakthroughs in fields as diverse as medicine, logistics, and education.
Yet, the vision remains contingent on a delicate balance: fostering openness, ensuring security, maintaining control, and nurturing a culture of responsible innovation. Microsoft’s new offerings are bold steps forward. Whether they usher in an agentic web that lives up to the hype, or falter on the shoals of complexity and risk, will depend on the collective will—and wisdom—of the global developer community. For now, the future looks both exhilarating and uncertain, with Microsoft inviting builders everywhere to “experiment, create, and empower.” The open agentic web is just beginning to take shape.
Source: Technology Record Microsoft to ‘help every developer build apps and agents that empower people and organisations everywhere’
