The HTML Moment for AI

When Tim Berners-Lee created HTML, he wasn’t just inventing a way to format documents. He was creating a universal language. A language that let millions of independent systems communicate, collaborate, and grow into something far beyond any single creator’s imagination. Today, AI is standing at a similar crossroads.

Right now, AI agents are powerful but limited. They perform impressively in narrow tasks like screening resumes, answering customer inquiries, or processing medical images, but their reliability plummets as their scope broadens. An agent trained to sift through legal contracts won’t suddenly excel at scheduling meetings. Attempting generalization dilutes their effectiveness, leading to frequent failures and unpredictable results.

As a consequence, we have built isolated "AI silos": brilliant specialists locked behind walls of data and specialization, rarely cooperating. Each agent is an island. And islands are difficult places to build complex civilizations.

So, how do we fix this?

The answer isn't to create ever more generalized AI, which would be expensive, slow, and error-prone, but rather to establish shared communication protocols that bridge these islands. Much as HTML allowed disparate web servers and browsers to communicate seamlessly, today’s companies are racing to develop standardized frameworks that let different AI agents talk to each other, understand each other's strengths, share relevant data, and coordinate complex tasks securely and reliably. 

Imagine a recruiting workflow: A hiring agent scans the web for qualified candidates; an HR agent independently verifies credentials; and a scheduling agent coordinates interviews. Currently, each of these tasks might rely on human mediation or fragile custom integrations. With standardized protocols, these separate AI agents could autonomously discover each other, share precise, context-aware information, and seamlessly coordinate workflows, all without human intervention.

This isn’t theoretical, this is happening now. In fact, Google announced Agent2Agent Protocol (A2A) a few days ago, Anthropic introduced the Model Context Protocol (MCP) earlier late last year, Megagon Labs published a research paper last week proposing a framework for AI Agents orchestration, all are confirming the direction towards building new communication standards. These standards ensure that each agent knows exactly what information it can trust, what it can share, and how it fits within a broader ecosystem. This collective choreography transforms isolated specialists into coordinated teams capable of reliably accomplishing complex, multi-step tasks at scale.

Yet, the significance goes beyond technical convenience. Just as standardized web languages made possible previously unimaginable products and services (e-commerce, streaming platforms, social networks, etc.) standardized AI communication protocols could launch entirely new categories of automation and cooperation. Healthcare providers could rapidly assemble agent teams specialized in diagnosis, treatment planning, and patient monitoring. Logistics firms could dynamically pair agents managing inventory with those handling real-time delivery scheduling, achieving unprecedented efficiency. Financial institutions could connect specialized compliance, analysis, and forecasting agents into agile, intelligent financial services platforms.

The evolution of these standards hints at something profound: the dawn of an "internet of agents." Just as the web let documents and data flow across the globe, standardization may soon enable intelligent tasks, workflows, and collaborations to flow seamlessly between diverse AI systems. We could soon see agents dynamically forming connections not just within one organization, but across entire industries, assembling as needed to tackle sophisticated challenges rapidly, economically, and safely.

But achieving this won’t happen automatically. Standards only succeed through consensus and careful design. Much like HTML, today’s emerging agent protocols require industry collaboration, thoughtful governance, and an open commitment to interoperability and responsible design. Without careful stewardship, we risk fragmentation— not just of standards, but of trust, safety, and control — with hundreds of incompatible systems competing rather than cooperating.. 

The stakes here are enormous. The companies, engineers, and organizations who successfully build, support, and champion these AI communication standards today will shape tomorrow’s economy. They will decide whether AI remains fragmented or becomes integrated; whether it remains an island chain or becomes a networked continent — and whether that network is one we can trust.

Just as we cannot imagine the modern internet without HTML, one day we may find it unimaginable to recall how we once allowed intelligent agents to remain isolated behind walls of data. The era of siloed AI is ending. It’s time to build the bridges.