AI agents can't understand each other — and nobody's fixing the meaning layer

Right now, AI agents communicating with each other get the grammar right but miss context constantly. Most agent protocols focus on reliable message delivery and data formats, but leave the hard part — making sure both sides actually mean the same thing — to whoever writes the code.

As systems deploy more AI agents that need to coordinate with each other, semantic misalignment becomes a silent failure mode. The protocol layer isn't catching it. Instead, the cost of making sure agents actually understand each other gets pushed into application code, orchestration logic, and prompt engineering — places where it's expensive to fix, easy to miss, and invisible until something breaks. This creates maintenance debt that scales with complexity.