When Kevin Scott, Microsoft’s CTO, took the stage ahead of the annual Build developer conference in Seattle, his remarks clearly set the trajectory for a new era in how artificial intelligence helpers—often called “agents”—will interact. While the public is already adjusting to the presence of AI in everything from web search to operating systems, Scott’s vision reaches further: he imagines a future where AI assistants from competing companies not only coexist but can actively collaborate, remember prior interactions, and build upon them. This collaborative, evolving landscape forms what he terms the “agentic web”—and it’s more than just technical jargon. It’s a significant shift in both strategy and philosophy for Big Tech, with profound implications for users, developers, and the entire digital ecosystem.
Artificial intelligence agents—sometimes labeled AI helpers or copilots—are autonomous digital entities designed to perform specific tasks on a user’s behalf. While some are familiar in the guise of chatbots handling customer queries or virtual assistants managing appointments, the ambitions now extend to much higher levels of autonomy. Imagine agents capable of squashing software bugs, co-authoring documents, optimizing networks, or even negotiating on your behalf for digital services—all without direct step-by-step instructions.
The hitch? Currently, each company’s agent is siloed. Google’s agents mostly work with Google services, OpenAI’s with their own API ecosystem, and Microsoft’s Copilot excels within Windows and Microsoft 365. This closed-ecosystem approach stifles the broader utility AI can offer; if you want to coordinate, you’re forced to glue the parts together manually, if at all possible.
Just as HTTP and TCP/IP unlocked the collaborative potential of the World Wide Web in the 1990s, MCP is aiming to become the plumbing for tomorrow’s agentic web. As Scott noted, this isn’t just about technology; it’s about democratizing the future—the protocol is designed so that anyone’s ideas and innovations can participate in shaping how AI agents interact.
Independent reports and technical summaries confirm that MCP is a direct response to the risk of balkanized AI ecosystems. According to Anthropic’s technical documents and confirmations from third-party analysts, MCP is structured to ensure privacy, user control, and interoperability. Google’s public statements further corroborate their interest in supporting standards that break down barriers between proprietary AI agents.
Scott drew a parallel to human cognition: we don’t recall every detail, but we do retain relevant highlights that help us solve future problems. Yet, for AI, this kind of lasting, meaningful memory comes with real technological and economic trade-offs. Storing and accessing vast conversational histories, with enough granularity to be helpful but enough abstraction to be efficient, rapidly increases the need for compute power and expensive, large-scale storage.
Preliminary internal reports from Microsoft’s research divisionand academic collaborators suggest this approach not only reduces hardware costs but also builds a more robust “working memory” for the agent. Importantly, this strategy introduces some of the same selective recall humans rely on, supporting both efficiency and relevance.
While this method shows significant promise, industry experts also flag potential risks. For instance, the act of selectively summarizing conversations means some nuance or detail may be inadvertently discarded, impacting the agent’s subsequent performance on complex tasks. Furthermore, as Scott acknowledges, even this memory-light approach still places a non-trivial burden on computational infrastructure.
A scenario where agents from different companies retrieve summarized “roadmaps” of your digital behavior may turbocharge productivity but could equally open new privacy risks if not transparently governed. As several privacy advocates have observed recently, effective oversight and robust default settings are crucial; otherwise, users may unwittingly grant sweeping permissions to third-party AIs.
Experts point to past efforts in the instant messaging and social networking realms as cautionary tales; despite early promises of federation and open APIs, most platforms eventually re-segregated to protect user lock-in and data assets. Whether AI shakes out differently is an open question.
Industry watchers highlight this as a double-edged sword. While the productivity and creativity gains could be enormous, so too is the possibility that opaque, poorly-audited webs of AI collaboration could lead to novel forms of misuse or unintended consequences, from privacy breaches to automated collusion and error propagation.
Reports from the Windows development team and third-party testers confirm Copilot’s early memory features—such as remembering recently opened files or recurring file operations—help fill the gap between stateless commands and genuine digital assistance. However, current capabilities remain limited primarily to the local Windows environment.
The next logical step, as outlined at Build, is for Copilot to federate tasks and context with agents running on other platforms, such as email assistants, cloud-based AI for document management, or even third-party device controllers. When and how these integrations arrive will be a key bellwether for the broader MCP effort.
However, oversight, competition, and real transparency will be needed to ensure this future serves users first—not just a handful of platform providers. The industry’s success will hinge on making these new AI agents both powerful and trustworthy, with memory and interoperability working hand-in-hand—not as isolated technical achievements, but as pillars of a truly open, empowered digital world.
For now, the jury is still out on how quickly these ambitions will become reality. But one thing is clear: the foundations being laid today will shape the AI-driven internet of the future—and who gets to participate in it. As MCP matures and more players join the effort, users can expect AI helpers that not only respond more intelligently to immediate needs but also coordinate, remember, and amplify collective creativity across company lines—a transformative leap, if it lives up to its promise.
Source: extremetech.com Microsoft: AI Helpers From Different Companies Should Work Together
AI Agents: The New Layer of Digital Assistance
Artificial intelligence agents—sometimes labeled AI helpers or copilots—are autonomous digital entities designed to perform specific tasks on a user’s behalf. While some are familiar in the guise of chatbots handling customer queries or virtual assistants managing appointments, the ambitions now extend to much higher levels of autonomy. Imagine agents capable of squashing software bugs, co-authoring documents, optimizing networks, or even negotiating on your behalf for digital services—all without direct step-by-step instructions.The hitch? Currently, each company’s agent is siloed. Google’s agents mostly work with Google services, OpenAI’s with their own API ecosystem, and Microsoft’s Copilot excels within Windows and Microsoft 365. This closed-ecosystem approach stifles the broader utility AI can offer; if you want to coordinate, you’re forced to glue the parts together manually, if at all possible.
A Call For Open Standards
It’s against this backdrop that Microsoft is now pushing for open standards—shared blueprints that allow these AI agents to communicate and cooperate, regardless of who built them. Specifically, Microsoft is supporting a protocol known as the Model Context Protocol (MCP), an open-source framework first developed by Anthropic and now backed by Google.Just as HTTP and TCP/IP unlocked the collaborative potential of the World Wide Web in the 1990s, MCP is aiming to become the plumbing for tomorrow’s agentic web. As Scott noted, this isn’t just about technology; it’s about democratizing the future—the protocol is designed so that anyone’s ideas and innovations can participate in shaping how AI agents interact.
Model Context Protocol: Bridging Rival Silos
The MCP is engineered to allow AI agents to share the “context” of a user’s ongoing goals, permissions, and past interactions across different platforms. For instance, a productivity agent from one vendor could request help from another agent specializing in data analytics, passing relevant context securely so the user doesn’t have to repeat information or manually connect the dots.Independent reports and technical summaries confirm that MCP is a direct response to the risk of balkanized AI ecosystems. According to Anthropic’s technical documents and confirmations from third-party analysts, MCP is structured to ensure privacy, user control, and interoperability. Google’s public statements further corroborate their interest in supporting standards that break down barriers between proprietary AI agents.
Why Memory Matters: The Transactional AI Trap
One critical flaw in today’s AI assistants is their “transactional” nature—each interaction is largely stateless. If you ask your AI helper to handle a task today, and return tomorrow with a follow-up, there’s a good chance the agent won’t remember your prior history. As anyone who has tried to carry on a multi-step project with a digital assistant knows, this shortcoming quickly limits usefulness.Scott drew a parallel to human cognition: we don’t recall every detail, but we do retain relevant highlights that help us solve future problems. Yet, for AI, this kind of lasting, meaningful memory comes with real technological and economic trade-offs. Storing and accessing vast conversational histories, with enough granularity to be helpful but enough abstraction to be efficient, rapidly increases the need for compute power and expensive, large-scale storage.
Structured Retrieval Augmentation: A Path Forward
To combat these constraints, Microsoft is developing a technology called structured retrieval augmentation. In essence, this method has the AI agent extract concise, semantically-rich nuggets from each turn of the conversation. These are then stitched into a roadmap—a compact, structured outline of what’s been asked, answered, and resolved.Preliminary internal reports from Microsoft’s research divisionand academic collaborators suggest this approach not only reduces hardware costs but also builds a more robust “working memory” for the agent. Importantly, this strategy introduces some of the same selective recall humans rely on, supporting both efficiency and relevance.
While this method shows significant promise, industry experts also flag potential risks. For instance, the act of selectively summarizing conversations means some nuance or detail may be inadvertently discarded, impacting the agent’s subsequent performance on complex tasks. Furthermore, as Scott acknowledges, even this memory-light approach still places a non-trivial burden on computational infrastructure.
The Broader Landscape: Risks and Real-World Hurdles
The vision sketched by Microsoft—a world of interoperable, memory-enhanced AI agents—sounds compelling. However, several significant hurdles remain, both technical and societal.Balancing Privacy, Security, and Utility
Whenever agents exchange context or summarize conversations, questions around data privacy and security emerge. Even if protocols like MCP are open-source and auditable, real-world deployments will depend on implementations that adhere to strict privacy-by-design principles. Microsoft, Anthropic, and Google all claim rigorous precautions, but independent audits remain sparse.A scenario where agents from different companies retrieve summarized “roadmaps” of your digital behavior may turbocharge productivity but could equally open new privacy risks if not transparently governed. As several privacy advocates have observed recently, effective oversight and robust default settings are crucial; otherwise, users may unwittingly grant sweeping permissions to third-party AIs.
The Economics of Intelligence at Scale
Improving agent memory through structured summaries, as Microsoft proposes, still doesn’t eliminate the cost issue. While less intensive than wholesale logs, even lightweight context storage and retrieval can become expensive when supporting millions or billions of users. Industry-wide, operators will need to find business models that support such features without passing unsustainable costs to end users—especially as regulatory requirements around data retention and explainability grow.Interoperability and Competition
Perhaps the thorniest challenge is aligning rivals around shared standards. While Microsoft, Google, and Anthropic are publicly supportive of MCP, some large players remain conspicuously absent from the discussion. Apple, Amazon, Meta, and OpenAI have yet to make substantial public commitments to such interoperability. Unless these and other major vendors participate, the dream of a truly “agentic web” risks stalling at the concept stage.Experts point to past efforts in the instant messaging and social networking realms as cautionary tales; despite early promises of federation and open APIs, most platforms eventually re-segregated to protect user lock-in and data assets. Whether AI shakes out differently is an open question.
Potential for Emergent Collective Intelligence
On the other hand, if the MCP vision succeeds, it could lay the groundwork for something much greater than today’s personal assistants. Interlinked AI agents, each specializing in unique domains but able to cooperate, could form a kind of emergent collective intelligence—one that helps businesses, communities, and individuals tackle complex, multi-disciplinary problems that no single agent (or company) could handle alone.Industry watchers highlight this as a double-edged sword. While the productivity and creativity gains could be enormous, so too is the possibility that opaque, poorly-audited webs of AI collaboration could lead to novel forms of misuse or unintended consequences, from privacy breaches to automated collusion and error propagation.
Microsoft Copilot as an Early Testbed
Microsoft’s own Copilot, now integrated directly into Windows 11’s File Explorer, offers a concrete preview of how memory and interoperability features might evolve in mainstream products. Users can invoke Copilot to perform context-aware searches, automate repetitive file management tasks, and even suggest next actions based on prior usage patterns.Reports from the Windows development team and third-party testers confirm Copilot’s early memory features—such as remembering recently opened files or recurring file operations—help fill the gap between stateless commands and genuine digital assistance. However, current capabilities remain limited primarily to the local Windows environment.
The next logical step, as outlined at Build, is for Copilot to federate tasks and context with agents running on other platforms, such as email assistants, cloud-based AI for document management, or even third-party device controllers. When and how these integrations arrive will be a key bellwether for the broader MCP effort.
Critical Reflections: Strengths and Limitations
Notable Strengths
- Democratization of Innovation: By backing open standards like MCP, Microsoft and allies are explicitly pushing back against a walled-garden future. This should empower more startups and independent developers to build competitive AI helpers.
- Efficiency by Selective Memory: Structured retrieval augmentation strikes a thoughtful balance between user value and system cost. While not flawless, it addresses the pain points most users feel when AI helpers “forget” ongoing projects or preferences.
- Foundation for Collective Problem-Solving: An interlinked web of AI agents could become a catalyst for tackling large, cross-disciplinary challenges—from business process automation to personalized education and healthcare.
Potential Risks
- Privacy Minefields: Summarized but persistently stored conversational context is still user data—and could be highly sensitive in aggregate. MCP and similar protocols must bake in robust privacy controls from the outset.
- Economic Uncertainty: Efficient agent memory is still not free. Operators, both large and small, will face tough choices around pricing, infrastructure investment, and fair access as AI utility scales.
- Inertia from the Largest Players: Unless industry giants beyond Microsoft, Google, and Anthropic buy in, the agentic web could wind up as another technical silo—just on a bigger, more complex scale.
- Risk of Unintended Consequences: With great power comes new hazards. Collaborative AIs could amplify both the positive and the negative in digital society, from supercharged teamwork to high-velocity errors or automated manipulations.
Outlook: Are We On the Verge of the Agentic Web?
As AI moves from command-and-response tools to persistent helpers, the stakes continue to rise for how these systems interoperate, remember, and are governed. Microsoft’s candid recognition of AI’s current limitations, coupled with its proactive advocacy for standards like MCP, marks a significant moment in tech history. If their vision of a truly agentic web materializes, the next decade could see a renaissance in digital productivity and collaboration.However, oversight, competition, and real transparency will be needed to ensure this future serves users first—not just a handful of platform providers. The industry’s success will hinge on making these new AI agents both powerful and trustworthy, with memory and interoperability working hand-in-hand—not as isolated technical achievements, but as pillars of a truly open, empowered digital world.
For now, the jury is still out on how quickly these ambitions will become reality. But one thing is clear: the foundations being laid today will shape the AI-driven internet of the future—and who gets to participate in it. As MCP matures and more players join the effort, users can expect AI helpers that not only respond more intelligently to immediate needs but also coordinate, remember, and amplify collective creativity across company lines—a transformative leap, if it lives up to its promise.
Source: extremetech.com Microsoft: AI Helpers From Different Companies Should Work Together
