The Web Wasn’t Built for This
HTTP, TCP/IP, HTML — the protocols holding the internet together were designed for a person clicking links and filling out forms. The splinternet of agents is what happens when that assumption stops holding. Most machine-to-machine traffic still runs on formats shaped for humans: JSON APIs built around predictable requests, pages laid out for human eyes first.
That’s starting to change. AI agents plan and carry out tasks on their own. They coordinate with other AI systems constantly, at a volume no human conversation ever generated. So a real question is emerging. Do agents keep using protocols built for people? Or do they build something of their own?
What Is the Splinternet of Agents?
The splinternet of agents describes a separate, faster communication layer built specifically for AI systems talking to each other — distinct from the human-oriented web. The term borrows from “splinternet,” which usually describes the internet breaking apart along geopolitical lines. Applied to AI, it captures a similar risk: machine traffic splitting off into something optimized for efficiency, not human readability.
Why Old Protocols Don’t Fit Agent Traffic
Standard web protocols carry overhead that exists purely for humans — visual formatting, readable prose, descriptive metadata. None of that helps when the reader is another model, not a person. Engineers see this mismatch as pure waste in agent-to-agent traffic. That waste creates real pressure to build something leaner, which is exactly what’s fueling the rise of the splinternet of agents.
New Protocols Powering the Splinternet of Agents
The industry noticed the gap. It started filling it fast:
- Model Context Protocol (MCP) — an open standard that lets AI models plug into external tools and data sources consistently. No more hand-building a custom integration every time.
- Agent-to-agent (A2A) standards — competing efforts from different companies and open-source groups. They let agents built on different models find each other, talk, and collaborate without custom glue code.
Both are young. But they’re the first real infrastructure built around how machines need to communicate, not how humans do.
Agents Inventing Their Own Shorthand
There’s a stranger, less deliberate side to the splinternet of agents. In some experimental research setups, agents trained to work together drift toward compressed signaling. It no longer resembles the human language they started with. It’s faster for the task at hand, but no longer legible to a person reading over their shoulder. Researchers call this agents developing their own “protocol” or “language” — though it’s emergent, not designed, unlike MCP or A2A.
The Risks Behind the Splinternet of Agents
Three questions dominate the debate right now:
- Interoperability — will competing standards fragment the landscape, echoing the early, messy days of internet protocol development?
- Transparency — if agents default to compressed formats, how do regulators and security researchers track what’s actually being decided?
- Security — new protocols haven’t been battle-tested like HTTP and TCP/IP have, after decades of attacks and patches. That gap is a real liability.
This Isn’t Hypothetical — It’s Already Live
- Standardized tool-calling formats let models invoke APIs predictably. This is early scaffolding for more complex agent-to-agent exchanges.
- Discovery protocols let an agent advertise what it can do. Other agents then know who to hand a task to.
- Machine-first data formats skip the overhead of parsing human-oriented HTML entirely.
Why the Splinternet of Agents Matters Right Now
We may be watching a new layer of the internet take shape. It’s not built for people — it’s built for a growing population of AI agents exchanging information at a scale nothing human-built was ever meant to handle. The engineers who wrote the original internet protocols never had to ask what “the internet” means when most of its traffic isn’t meant for a human to read at all.
How to Get Involved
- Read the actual specs — MCP, the various A2A efforts — instead of relying on secondhand summaries. This field moves fast.
- Learn distributed systems and protocol design fundamentals. The same principles apply whether you’re building for humans or machines.
- Follow multi-agent AI research, since that’s where the emergent-communication side is being studied.
- Join the governance conversations. Standards bodies and open-source groups still have the interoperability and oversight questions wide open.
Bottom Line
The splinternet of agents points at something real: a communication layer built for machines, evolving its own protocols far faster than the original web ever did. Whether it ends up open and well-governed, or fragmented and opaque, depends on decisions being made right now — mostly out of public view.