While projects like Agency and A2A solve crucial communication and identity problems for AI agents, these are foundational. The larger, unsolved challenge preventing distributed superintelligence is the semantic layer: enabling agents to establish shared meaning and intent.
To enable shared knowledge, a "cognitive memory fabric" is needed. This architecture combines exploratory, probabilistic AI agents with formal, deterministic representations of the world (like digital twins), providing a powerful yet safe environment for innovation.
While direct vector space communication between AI agents would be most efficient, the reality of heterogeneous systems and human-in-the-loop collaboration makes natural language the necessary lowest common denominator for interoperability for the foreseeable future.
Moving beyond isolated AI agents requires a framework mirroring human collaboration. This involves agents establishing common goals (shared intent), building a collective knowledge base (shared knowledge), and creating novel solutions together (shared innovation).
Today's AI agents can connect but can't collaborate effectively because they lack a shared understanding of meaning. Semantic protocols are needed to enable true collaboration through grounding, conflict resolution, and negotiation, moving beyond simple message passing.
To foster shared innovation among AI agents, "cognitive engines" are required. These serve two functions: accelerators to speed up specific tasks (e.g., complex calculations) and guardrails to ensure creative exploration remains within safe, realistic, and compliant boundaries.
Cisco's OutShift incubator focuses on enabling distributed systems rather than building monolithic ones. Their strategy for both AI and quantum computing is not to create the most powerful single agent or computer, but to build the network fabric that connects them all.
Current AI development focuses on "vertical scaling" (bigger models), akin to early humans getting smarter individually. The real breakthrough, like humanity's invention of language, will come from "horizontal scaling"—enabling AI agents to share knowledge and collaborate.
Dr. Vijoy Pandey defines ASI with two concrete benchmarks: 1) an AI system performing 100% of a human task autonomously (economic viability), and 2) an AI inventing novel ideas beyond its training data without human help (technical viability).