Artificial superintelligence, in this view, is not only a question of building larger models. Outshift by Cisco argues that the next important shift is horizontal: connecting AI agents across systems, companies, and platforms so they can work as a coordinated team.
The idea starts with a simple problem. Many agents can already handle narrow tasks, but coordination still often depends on a human deciding what should happen next.
Why Single Agents Are Not Enough
The source article describes a healthcare system made up of separate AI agents for symptom assessment, scheduling, insurance, and pharmacy. Each agent may be strong within its own domain. The problem is that exchanging data is not the same as coordinating care.
Vijoy Pandey, senior vice president and general manager of Outshift by Cisco, frames the missing piece as shared coordination. In his explanation, today’s agents can have intelligence, but they lack the connective layer that lets them operate as one team.
For years, AI progress has been driven by vertical scaling: larger models, more data, and more compute. That has helped produce agents that can perceive, reason, and act in digital environments. But Outshift’s argument is that cross-domain problem solving needs another axis of scale.
That second axis is horizontal scaling. Instead of only making individual agents more capable, it focuses on how different agents discover each other, align around goals, and combine what they know.
The Internet Of Agents And The Internet Of Cognition
Outshift describes two related layers. The first is the Internet of Agents, a connectivity layer that lets autonomous agents find one another, prove identity, and exchange messages across domains.
The second is the Internet of Cognition, a semantic layer that sits above that connectivity. Its purpose is to let agents share intent, context, and reasoning, so they can do more than pass tasks from one system to another.
According to the source, Outshift has built AGNTCY as a connectivity layer. AGNTCY is described as an open-source project now under the Linux Foundation. It is meant to support open, standardized protocols for agents operating across different systems, companies, and platforms.
The Internet of Cognition thesis goes further. It is intended to help agents align goals, share institutional knowledge, build compound memory, and make collective trade-offs. In practical terms, that means a multi-agent system would not simply route a request. It would have a way to coordinate around what the request is trying to achieve.
Three Layers For Multi-Agent Coordination
The proposed stack rests on three pillars: shared intent, shared context, and shared reasoning. Each addresses a different weakness in basic multi-agent setups.
Shared Intent
Shared intent depends on cognition state protocols. These protocols act as a semantic handshake, allowing agents to agree on a goal before they act and negotiate toward it as the task develops.
Outshift has created Mycelium, an open-source coordination layer that organizations can clone and use with their own agents. In internal testing cited by Pandey, unstructured groups reached a decision about a third of the time across 14 scenarios. A coordination protocol that required agents to declare a goal, identify missing information, and resolve conflicts before acting raised that result to 93%.
Shared Context
Shared context comes from a cognition fabric. The source describes this as a shared institutional memory and communication mesh. Its purpose is to let agent insight compound over time instead of disappearing at the end of each session.
Pandey calls the problem “organizational amnesia.” A policy-governed context layer is presented as a way to ensure that the system’s baseline intelligence keeps improving rather than repeatedly starting from the same point.
Shared Reasoning
Shared reasoning involves cognitive amplifiers and guardrail technologies. Cognitive amplifiers are described as tools for speeding up shared reasoning and modeling. Guardrail technologies create frameworks for security, cost, and compliance.
Humans remain part of this layer. The article describes them as active contributors who make judgment calls routed to them by the system, rather than only reviewing outputs after the fact.
Why Guardrails Matter As Agents Coordinate
More coordination creates more capability, but it also creates new risk. The source names several examples: unintended delegations, malicious prompt injections or memory poisoning, and over-privileged agents with access to permissions and data beyond what their tasks require.
That is why environment-specific controls are part of the architecture. An agent that can act across tools and domains needs controls that follow the task, not only the user role or session.
Outshift’s Continuous Agent Semantic Authorization, or CASA, is described as an open-source reference implementation and a guardrail technology. It works by reading what an agent is trying to accomplish, then checking each tool request against that task through continuous authorization.
The healthcare example makes the risk concrete. If an agent is authorized to summarize a patient record but begins by querying a whole database, CASA could deny the call because the request no longer matches the task it was authorized to perform.
Where Enterprises Can Begin
Pandey advises businesses to start with one cross-functional workflow that spans three or four teams and currently requires a human to authorize handoffs. The recommendation is to build a small multi-agent system on open, interoperable infrastructure and measure it against a baseline.
The important measurement is not only whether one agent completes a task. It is whether one agent’s insight makes another agent better. That is the signal Outshift points to as evidence that horizontal scaling is working.
The broader claim is that artificial superintelligence may depend as much on coordination as on model size. Bigger models may still matter, but the path described here adds a layer for collective action: agents sharing intent, carrying forward context, and reasoning together under controls that keep their actions aligned with the original goal.