Building the New Agentic Web

In the wake of Microsoft Build 2025, leading AI innovators have sketched out a transformative vision for the internet—what Microsoft CEO Satya Nadella dubs the “open agentic web.” [https://www.youtube.com/watch?v=_a8EnBX8DSU] In this new paradigm, autonomous AI agents carry out complex, domain-specific tasks on behalf of individuals and organizations, orchestrating workflows across diverse services and platforms. This article explores the technical foundations, developer tooling, real-world scenarios, and organizational shifts required to realize the agentic web.

From Apps to a Platform Shift

The computing industry has undergone several platform shifts: from standalone PC applications to integrated suites like Microsoft Office, then to collaboration hubs such as Teams, and finally to cloud-native services. Now, we stand at the threshold of the AI era’s next stage—building a unified stack for agentic applications that can operate at enterprise scale. Developers will need new patterns and primitives—open standards, composable services, and end-to-end orchestration—to assemble multi-agent systems that work together seamlessly.

Reimagining Workflows: Stanford’s AI-Powered Tumor Board

One of the most compelling demonstrations of the agentic web comes from Stanford Medicine. In tumor board meetings—critical gatherings where clinicians review patient data and decide on treatment plans—AI agents now automate data retrieval, analysis, and presentation. A pathology-specialized agent pulls histology images, a genomics agent summarizes genetic mutations, and a literature agent surfaces the latest research, all within Microsoft Teams. Clinicians can then focus on decision-making and teaching, generating slides or summary notes without switching applications (Microsoft Blog).

The Open, Composable Stack

The agentic web is built on four layers:

• Data and Models: Developers choose from hundreds of open-source and commercial large language models (LLMs), vision models, and reasoning engines.
• Agent Platforms: Unified environments like Azure AI Foundry and Copilot Studio let teams design, fine-tune, and deploy agents across cloud and edge.
• Connector Protocols: Open standards such as the Model Context Protocol (MCP) and Agent-to-Agent (A2A) interoperability enable agents to discover, authenticate, and exchange messages with websites, services, and each other.
• User Interfaces: From Teams and Windows to third-party apps and custom dashboards, flexible canvases allow people to interact with and supervise fleets of agents.

Open protocols prevent “agent silos.” An agent built in Copilot Studio can invoke another hosted on Azure AI Foundry or integrate with a third-party service exposing an MCP endpoint. Projects like NLWeb provide a natural-language layer for websites, enabling any site to serve both human visitors and AI agents equally(The Verge).

A “UI for AI” and Agent Management

Just as Outlook unified email, calendar, and contacts, Microsoft 365 Copilot and Teams are evolving into the first multipurpose UI for AI. Users can:

• Chat with agents and issue high-level intents.
• Inspect session logs showing each agent’s actions for transparency and compliance.
• Hand off tasks between agents or escalate to human review.

In practice, knowledge workers become “agent managers,” orchestrating domain-expert agents rather than performing routine tasks themselves. A marketer might spin up an agent to pull product metrics, draft campaign emails, and schedule social posts—all within a single Teams conversation(PYMNTS.com).

Inverting Knowledge Work with Reasoning Models

Traditional workflows involve waiting for colleagues to gather data, compile reports, and distribute briefings. In the agentic web, a single prompt to Copilot can fetch CRM data, internal documents, and external market research; synthesize a concise briefing; and deliver it instantly. Humans shift from data gathering to strategic oversight.

Reasoning models—LLMs designed to decompose high-level intents into orchestrated calls across specialized agents—drive this inversion. A “prepare for customer visit” prompt might spawn sub-tasks: querying the CRM, summarizing recent emails, retrieving financial reports, and drafting slide decks(Stratechery).

Developers, AI-Driven Code, and the Future of Programming

Developers were among the earliest adopters of AI agents. GitHub Copilot evolved from real-time code suggestions to an asynchronous coding agent capable of multi-file edits, refactoring, and CI/CD integration. Today’s Copilot coding agent lets teams offload tasks such as bug fixing, test-coverage improvements, and documentation generation(VentureBeat).

In the near future, 90–95% of written code may originate from AI agents. Yet human expertise remains central: every AI-generated change is reviewed by developers before deployment. Domain-specific agents, fine-tuned on proprietary data, will offer organizations a sustainable competitive advantage.

Domain-Specific Agents and the Reinforcement Loop

Generic agents perform broad tasks, but the real value arises from agents fine-tuned on company workflows, data, and customer feedback. Copilot Tuning and Azure AI Foundry Model Customization enable enterprises to imbue agents with specific domain knowledge and writing styles. When agents act—publishing documents, engaging customers, or executing trades—they generate reinforcement signals (clicks, ratings, revenue) that feed back into continuous retraining. This virtuous cycle drives ongoing improvement and differentiation(Microsoft Blog).

Organizational Reinvention in the Agent Era

Embracing the agentic web requires more than technology adoption; it demands a cultural and operational overhaul. Organizations must:

• Adopt “agent-first” product strategies, treating agents as a new class of interfaces and services.
• Empower frontline workers to create and customize agents for their workflows, diffusing AI expertise across teams.
• Reskill employees as agent managers—authoring prompts, monitoring session logs, and fine-tuning performance.
• Foster an open culture that shares learnings and best practices rather than hoarding AI expertise in centralized centers of excellence.

Successful organizations will look to Microsoft’s own reinventions—shifting from client/server to cloud, from devices to services—as models for embracing AI.

Proactive, On-Device Agents and Ubiquitous AI

While cloud-based agents dominate headlines, there is a parallel surge in on-device capabilities enabled by Windows AI Foundry and Copilot-plus PCs. Agents running locally can summarize emails in Outlook or surface calendar conflicts without network calls, enhancing privacy and resilience. The goal aligns with Mark Weiser’s vision of ubiquitous computing—technology so seamless that it “disappears,” yet remains transparent through session logs and explicit consent(PYMNTS.com).

Global Impact: Healthcare, Education, and Beyond

Nadella emphasizes tangible social impact over tech company hero worship. The agentic web promises productivity gains in sectors plagued by inefficiency:

• Healthcare accounts for nearly 20% of U.S. GDP. Multi-agent clinical workflows, like Stanford’s tumor board solution, reduce administrative burdens, cut costs, and free clinicians to focus on patient care.
• Education benefits from AI-driven learning assistants. Early World Bank studies in Nigeria show that giving teachers Copilot-style agents measurably improves student outcomes.
• SMEs and nonprofits gain access to specialized expertise. Small organizations can build and deploy domain-specific agents without massive budgets, democratizing access to AI-powered workflows.

Overcoming Challenges: Accuracy, Liability, and Trust

As AI agents take on critical tasks, concerns around accuracy, legal liability, and trust intensify. Recent studies show that even advanced systems complete less than 30% of complex tasks without errors, underscoring the need for human oversight(PYMNTS.com). Multi-agent errors can be hard to trace, raising questions about accountability. Companies are exploring “judge” agents to monitor and correct mistakes, while legal experts anticipate holding the deepest-pocketed parties liable for damages(WIRED).

Building trust also requires strong security, identity, and governance controls. Microsoft’s Entra Agent ID assigns unique identities to agents, preventing “agent sprawl.” Integration with Purview ensures data security and compliance. Detailed observability dashboards provide metrics on performance, cost, quality, and safety, helping organizations govern their fleets of agents(Microsoft Blog).

The Road Ahead

The open agentic web represents a paradigm shift as transformative as the move from mainframes to PCs or desktops to smartphones. By weaving together open models, standardized protocols, composable platforms, and flexible UIs, Microsoft and its partners are laying the scaffolding for an AI-powered future. In this world, humans move from routine execution to high-value oversight—managing intelligent agent fleets, crafting strategy, and delivering impact.

To thrive in the agent era, organizations should:

• Experiment hands-on with Copilot Studio and Azure AI Foundry.
• Implement Model Context Protocol (MCP) on websites to become agent-accessible.
• Empower employees to author custom agents and embed AI into line-of-business applications.
• Adopt governance frameworks for security, compliance, and observability.
• Cultivate an “agent-first” culture that values continuous learning and open collaboration.

As Nadella reminds us, “You don’t get fit by watching others go to the gym.” The time is now for every developer, knowledge worker, and business leader to embrace the agentic era—building the open, interoperable web that fulfills technology’s promise to make a real difference in people’s lives.