The Agentic Web

Each major technological wave fundamentally reshapes the internet. The combination of network protocols (TCP/IP, HTTP) and web technologies (HTML, browsers) defined the PC era, prioritizing open information access. Mobile hardware (sensors, GPS, powerful chips) and operating systems drove the app economy, unlocking location-aware, always-on, hardware-integrated experiences. Today, AI – encompassing generative models, large language models, and autonomous agents – represents the next seismic shift. This forces a critical question: What kind of internet infrastructure does AI truly need to unleash its full potential?

Unlocking AI Capabilities: The Four Pillars for the Next Web

While the current internet is vast, its structure presents significant hurdles for AI. To fully leverage AI's capabilities online, the next evolution likely needs to excel in four key areas:

1. Complete Context Access: AI agents require comprehensive information to make optimal decisions. Today's internet, largely composed of siloed applications and data stores, hinders this.Breaking down these silos is paramount.

2. Universal Tool Use: To perform complex, multi-step tasks efficiently, agents need seamless access to the vast array of tools and services available online – from booking flights to executing code or querying specialized databases.

3. Native AI Interaction: Humans excel with graphical user interfaces (GUIs). AI, however, thrives on structured data, APIs, and communication protocols.Forcing AI to mimic human interaction with GUIs is inherently inefficient. The future web needs interfaces designed for machine intelligence.

4. Efficient Agent Collaboration: The true power of AI may lie not just in individual agents, but in networks of collaborating agents. The next web must facilitate low-friction, high-efficiency communication and coordination between diverse AI entities.

Bridging the Gap: Current Approaches and Their Limitations

We're seeing initial attempts to bridge AI with the current internet, but they highlight the underlying friction:

• UI Automation ("Computer Use"): Technologies allowing AI to control GUIs are essentially workarounds. They force powerful AI to painstakingly mimic human clicks and navigation within interfaces not designed for them. While offering short-term utility, this isn't a scalable or efficient long-term solution for native AI interaction.

• On-Device AI ("AI Phones"): Integrating AI at the operating system level to orchestrate apps via their APIs (like Apple Intelligence) is a significant step forward, allowing AI to work closer to the data layer. However, it creates complex dynamics – apps may be hesitant to fully open their data and functionality, fearing disintermediation by the OS-level AI. Furthermore, as AI personal assistants become more capable, the need for many traditional app UIs might diminish altogether.

The Path Forward: APIs, Protocols, and Open Interoperability

A more fundamental and efficient path involves building an internet where AI can interact natively. This means prioritizing:

• API-Centric Design: Services and data sources exposing clean, structured APIs that AI agents can readily understand and utilize.

• Standardized Protocols: Developing open standards – conceptually similar to what the Model Context Protocol (MCP) aims for – that define how agents discover, connect to, and utilize diverse tools and data sources. Such standards are crucial for breaking down silos and fostering an open ecosystem where anyone can contribute tools usable by any compliant agent.

This approach plays to AI's strengths in processing structured information, rather than forcing it through the bottleneck of human-centric interfaces.

Beyond Human Interaction: Enabling the Agent Network

Furthermore, the future internet needs to facilitate efficient agent-to-agent collaboration. Imagine AI agents representing individuals or businesses negotiating services directly. This could involve using natural language capabilities for initial discovery and agreement, followed by execution via standardized protocols. Such a network could potentially operate with far greater speed and lower transaction costs than human-mediated processes, potentially reducing reliance on today's large, centralized platforms.

Vision: Characteristics of an "Agentic Web"

While the exact name and timeline are uncertain, an internet optimized for AI might exhibit these characteristics:

• Agents as Gateways: Highly personalized AI assistants become the primary interface through which humans interact with the digital world.

  

• Ubiquitous Service Agents: Countless specialized agents representing businesses, services (hotels, banks), and data sources operate behind the scenes, interacting primarily with personal assistants or other service agents.

• Radical Interoperability: Agents from different developers, companies, and platforms can communicate and transact seamlessly using open standards.

• Flatter, Decentralized Structure: Increased direct agent-to-agent interaction could lessen the dominance of massive central platforms, leading to a more distributed network.

• Self-Organizing Collaboration: Networks of agents could potentially self-organize and negotiate complex workflows dynamically to achieve goals.

Conclusion: Building the Internet for Intelligence

Technological shifts drive internet evolution by enabling new capabilities. AI is undoubtedly the current driver. While the final form of the "Agentic Web" remains to be seen, its core direction seems clear: it must be an internet designed not just for human eyeballs, but for machine intelligence. The critical challenge and immense opportunity lie in building an open, interconnected, and standardized digital fabric that allows AI agents to access context, utilize tools, interact natively, and collaborate efficiently, truly unlocking the transformative potential of artificial intelligence.