The path to artificial superintelligence

Imagine a healthcare system made up of multiple AI agents: one that manages symptom assessment, another scheduling, a third insurance, and a fourth pharmacy. Each is an expert in its domain. But they all have their own distinct knowledge and objectives. Today they can exchange data, but they are not yet able to actually coordinate patient care without a human making the decisions. “The intelligence is already there. What is missing is the connective tissue that turns four strangers into one team,” explains Vijoy Pandey, senior vice president and general manager of Outshift by Cisco. This “connective tissue” comes from adding a semantic layer—what Outshift calls the “Internet of Cognition”—that enables agents across domains to work together and, critically, “think” together through shared intent, context, and reasoning. This semantic layer relies on a connectivity layer beneath it called the “Internet of Agents,” which allows autonomous agents to discover one another, prove identity, and exchange messages across domains. When used together, they enable “the next step on the road to distributed artificial superintelligence,” says Pandey. From solo silicon savants to the ‘Internet of Cognition’ For years, the AI industry has been focused on growth. Scaling vertically has led to bigger models, trained on more data with more compute. This has produced the reasoning capabilities that can be like a “brain” for AI agents, which can perceive, reason, and act in digital environments. While vertical scaling can produce more capable agents perpetually, to enable agentic problem solving across different systems, companies, and platforms the next axis of scale must be horizontal, says Pandey. Multi-agent systems are already being explored in areas like software engineering, drug discovery, and scientific simulations, but their performances so far have been underwhelming. One study finds a failure rate of between 41% and around 87% when evaluating seven open-source multi-agent systems. “Connected agents handle coordinated action well; taking a task whose shape they have seen, divided and passed around,” Pandey explains. “What they cannot do is hold a goal in common and reason toward something none of them was trained to solve.” “The gap is architectural, not a prompting problem,” Pandey adds. “Without the right coordination layer, naive multi-agent setups can perform worse than a single agent. The step change is that team of agents converging on its own, on a new problem, with no human stitching the seams.” To reach this goal, Pandey says Outshift has built a connectivity layer called AGNTCY, an open-source project now under the Linux Foundation. AGNTCY allows agents across different systems, companies, and platforms to find each other, prove identity, and exchange messages through open, standardized protocols. And, as Pandey explains, this allows the Internet of Cognition thesis to take a step further. It creates a semantic layer that allows agents to align goals (share intent), pool institutional knowledge and compound memory (share context), and make collective trade-offs (share reasoning). Pandey likens this progression to that of humans: “For hundreds of thousands of years humans got individually smarter, and the gains died with each person who made them,” he explains. “Around 70,000 years ago that changed, when humans learned to share intent, build cumulative knowledge, and reason collectively. That is when scattered individuals became civilization. “Agents are at the same threshold. We have built the silicon geniuses and given them agency. What they lack is the layer that let humans go collective,” he says. First steps to distributed superintelligence Enabling agents to work collectively rests on three pillars in the tech stack: Shared intent through cognition state protocols: Cognition state protocols are the semantic handshake that allow agents to agree on a goal before they act and then negotiate toward it. Outshift has created an open-source coordination layer called Mycelium, which organizations can clone and use against their own agents. “We found that unstructured groups reached a decision about a third of the time across 14 scenarios,” says Pandey, speaking about internal testing. “A coordination protocol that makes agents declare a goal, surface missing information, and resolve conflicts before acting raised that to 93%.” Shared context through cognition fabric: A cognition fabric is a shared institutional memory and communication mesh that allows agent insight to compound over time rather than resetting each session. This policy-governed context layer solves the problem of “organizational amnesia,” says Pandey, by ensuring the baseline intelligence of the systems only ever goes up. Shared reasoning through cognitive amplifiers and guardrail technologies: Two kinds of cognition engine can be used together to enable shared reasoning. Cognitive amplifiers speed up shared reasoning and modeling, and guardrail technologies (GATs) create security, cost, and compliance frameworks. Humans are active contributors to this layer, making judgment calls the system routes to them (rather than reviewing outputs after the fact). Cognition sharing in multi-agent systems can create new risks, including unintended delegations, malicious prompt injections or memory poisoning, or over-privileged agents with access to permissions and data far beyond what their tasks require. Environment-specific controls are therefore needed to protect against unintended actions or consequences. “Agents have human-like attributes but operate at machine speed and scale,” says Pandey. “Everything we built for twenty years—access control, identity, compliance—was built for humans or machines, not both.” Continuous Agent Semantic Authorization (CASA)—an open-source reference implementation developed by Outshift—is a GAT that works to ensure agent actions remain securely aligned with the user’s original goal through a process of continuous authorization. It does this by reading what the agent is trying to accomplish then checking each tool request against that task. In the case of a healthcare system, for example, an agent told to summarize a patient record may start by querying a whole database. This could lead to CASA denying the call, because the request no longer matches the task it was authorized for. “Today’s controls are scoped to a role or a session not to the task so an agent granted a tool can use it for anything,” explains Pandey. “Roughly 90% of the time, an agent has no way to confirm it is even cleared for the job it was handed.” Experimentation for cross-domain innovation When horizontally scaling intelligence in the enterprise, businesses should begin by experimenting with one cross-functional workflow that spans three or four teams and currently needs a human authorizing the handoffs, Pandey advises. “Stand it up as a small multi-agent system on open, interoperable infrastructure, with a measurable baseline,” he says. “Keep building bigger models, add the horizontal axis on top of them, and change what you measure. Track where one agent’s insight made another agent better—that is the signal the horizontal axis is working.” By starting to experiment now with intent, context, and reasoning layers, organizations can get ahead of the curve. “The problems are open, and the infrastructure is still being written,” says Pandey. “This is the moment to build it.” For more information on the Internet of Cognition, visit Outshift.com . This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.