The current evolution of Artificial Intelligence is hitting a structural wall. While we have become remarkably good at building powerful individual models and “stitching” them together into workflows, a fundamental gap remains: AI agents can connect, but they cannot yet think together.
Vijoy Pandey, SVP and GM of Outshift by Cisco, argues that the next great frontier in AI isn’t just about making models smarter—it is about moving from simple connectivity to shared cognition.
The Problem: Connection vs. Cognition
Currently, most multi-agent systems operate in silos. You can plug several agents into a single “supervisor” model, but they lack semantic alignment and shared context. Each time an agent performs a task, it essentially starts from scratch, unaware of the nuanced “thought process” or the specific context held by the agent that preceded it.
Pandey draws a parallel to human evolution. Humans didn’t just become intelligent in isolation; we unlocked a “cognitive revolution” through communication. By developing language and shared intent, we moved from individual intelligence to collective intelligence, allowing us to coordinate, negotiate, and innovate as a group.
To achieve this in silicon, AI requires more than just high-speed data transfer; it requires a way to transfer understanding.
The Solution: New Protocols for Distributed Intelligence
To solve this “horizontal distributed assistance problem,” Pandey’s team is working toward a concept called the “Internet of Cognition.” The goal is to move away from simple data exchange and toward a system where agents can share their internal reasoning and context through a new layer of infrastructure.
They are developing three specific protocols to facilitate this:
- Semantic State Transfer Protocol (SSTP): Operates at the language level, allowing systems to analyze semantic communication so they can accurately infer the correct tools or tasks required.
- Latent Space Transfer Protocol (LSTP): A more advanced method that transfers the “entire latent space” (the internal mathematical representation of information) from one agent to another. This avoids the “tax” of converting data into natural language and back again, making communication much more efficient.
- Compressed State Transfer Protocol (CSTP): Focuses on efficiency by compressing data and grounding only the most relevant variants. This is critical for edge computing, where bandwidth is limited but high-accuracy state transfer is necessary.
By combining these protocols with “cognition engines” (which provide guardrails) and a new infrastructure fabric, the team aims to create distributed super intelligence.
Real-World Impact: Efficiency in the Trenches
While the “Internet of Cognition” is a future-facing vision, Cisco is already seeing the benefits of agentic workflows in its current operations.
The company’s Site Reliability Engineering (SRE) team faced a classic scaling problem: code production was increasing, but the team size remained static. By deploying over 20 AI agents—some internal and some third-party—to manage complex workflows like CI/CD pipelines and Kubernetes deployments, Cisco achieved significant results:
- Speed: Deployment times dropped from “hours and hours to seconds.”
- Reliability: Agents reduced 80% of the issues previously encountered within Kubernetes workflows.
- Integration: These agents utilize frameworks like the Model Context Protocol (MCP) to access over 100 different tools and security platforms.
The Pragmatic View: AI as a Tool, Not a Replacement
Despite these advancements, Pandey maintains a grounded perspective on the role of AI. He warns against the temptation to use AI simply because it is available, noting that “you don’t just go around looking for nails because you have a new hammer.”
The most effective systems will not rely solely on the non-deterministic nature of AI (where outcomes can vary), but will instead marry AI with deterministic code —the reliable, rule-based logic that has underpinned computing for decades.
The ultimate goal is to move beyond mere connectivity to a state of shared intent and collective innovation, turning isolated models into a synchronized, intelligent fabric.
