MCP solved tool calling. A2A solved coordination. What solves transport?
The history of distributed computing is one of protocol proliferation followed by consolidation. Common Object Request Broker Architecture (CORBA), Distributed Component Object Model (DCOM), Java rem…
The history of distributed computing is one of protocol proliferation followed by consolidation. Common Object Request Broker Architecture (CORBA), Di
Read Full Story at VentureBeat →Why This Matters
The breakthrough in Model Context Protocol (MCP) tool calling and Agent-to-Agent (A2A) coordination signals a critical inflection point in AI-native distributed systems. If transport—the movement of data, tasks, and state across heterogeneous systems—remains unsolved, the promise of seamless, interoperable AI agents could stall at the edge of isolated silos. This isn't just about technology; it's about whether the next wave of AI infrastructure will be fragmented or fundamentally unified.
Background Context
The distributed computing landscape has long been plagued by the tension between innovation and fragmentation. Protocols like CORBA and DCOM emerged in the 1990s as ambitious attempts to standardize remote procedure calls, only to succumb to complexity, vendor lock-in, and the rise of simpler, web-native alternatives. Today’s AI systems face a parallel challenge: protocols for tool integration and agent coordination are maturing, but the underlying transport layer—responsible for secure, efficient, and scalable data exchange—lags behind, risking another cycle of isolated ecosystems.
What Happens Next
Expect a wave of consolidation around transport-layer solutions, likely led by industry consortia or open standards bodies, as stakeholders realize that tool calling and coordination are meaningless without reliable transport. Open questions remain: Will proprietary solutions dominate, or will a new generation of lightweight, AI-optimized protocols emerge? Watch for shifts in cloud provider strategies and whether startups or incumbents bridge this gap first with pragmatic, interoperable designs.
Bigger Picture
This moment reflects a broader pattern in computing history: breakthroughs in high-level abstraction (like MCP and A2A) often expose unresolved gaps in foundational infrastructure. Just as the web needed HTTP to unlock the potential of HTML, AI-native systems may require a transport revolution to fully realize their promise. The stakes are high—without solving transport, the AI agent economy risks becoming a patchwork of incompatible fiefdoms, undermining the very interoperability that could define the next decade of innovation.
