Background: The Challenge of AI Context and Data Silos

The evolution of Artificial Intelligence is rapidly approaching an inflection point, moving beyond single-purpose models towards a vibrant autonomous agent economy. In this future, decentralized agents will execute complex tasks, manage assets, and deliver sophisticated services. As this complexity grows, so does the need for standardized methods of interaction. This has given rise to frameworks like Model Context Protocols (MCPs) and Agent to Agent (A2A)—rules and standards designed to govern how AI models, agents, and data sources securely and coherently communicate.

However, MCP or A2A are insufficient to handle all the data necessary for good AI outcomes. . The core challenge remains: the Context Barrier. This barrier is a two-fold problem of architecture and infrastructure:

• The Architectural Limit of the Context: AI models operate with transient context. Their understanding is confined to the data within their immediate operational window. This prevents the accumulation of persistent knowledge and renders them contextually unaware from one task to the next, unable to maintain state or learn from long-term interaction. Proprietary applications incorporate proprietary memory models to retain the context.

• The Structural Problem of Data Silos: The very data needed to build persistent context—a user's or agent's long-term experiences and history—is fragmented across centralized, proprietary platforms. This data is non-portable, its value captured exclusively by the platform. Any AI system, agent, or MCP attempting to operate across these silos would face friction.