From Connected Agents to Collective Intelligence: The Next AI Revolution

The AI landscape is at a critical inflection point. While today’s agents can connect, they fundamentally cannot think together—a limitation that’s holding back the next wave of artificial intelligence. At a recent VentureBeat AI Impact Series event, industry leaders Vijoy Pandey of Cisco’s Outshift and Stanford’s Noah Goodman of Humans& revealed why achieving true collective intelligence represents the most important frontier in AI development.

The Missing Link: Beyond Coordination to Collaboration

The problem isn’t technical plumbing anymore. Protocols like MCP, A2A, and AGNTCY have solved the equivalent of giving agents phones to call each other. But as Pandey starkly put it, “agents today can connect together, but they can’t really think together.”

This distinction matters enormously. Coordination means executing pre-programmed instructions in parallel. Collaboration means negotiating shared goals, building common understanding, and innovating together—capabilities that remain stubbornly out of reach for multi-agent systems.

Learning from 70,000 Years of Human Evolution

Pandey and Goodman both point to a fascinating historical parallel: humans achieved individual intelligence approximately 300,000 years ago, but collective intelligence didn’t emerge until roughly 70,000 years ago with sophisticated language development. This breakthrough enabled three revolutionary capabilities that multi-agent AI desperately needs:

Shared intent – The ability to align on common goals across different agents and organizations. Without this, agents operate in silos, duplicating effort or working at cross-purposes.

Shared knowledge – A collective memory system where agents can build upon each other’s discoveries, creating emergent understanding that exceeds individual capabilities.

Collective innovation – The capacity to evolve context and generate novel solutions through interaction, not just execute predetermined tasks.

“Language is this encoding that requires understanding context, intention, and how the world affects what people will say,” Goodman explained. This sophisticated understanding scaffolds human collaboration and cumulative cultural evolution—exactly what’s missing from agent-to-agent interaction today.

The Internet of Cognition: A Three-Layer Architecture

To bridge this gap, Pandey envisions what he calls the “Internet of Cognition”—a revolutionary three-layer architecture:

Protocol Layer: Moving beyond basic connectivity to enable deep understanding between heterogeneous agents. These protocols handle intent sharing, coordination, negotiation, and discovery across different vendors and organizational boundaries.

Fabric Layer: A shared memory system that allows agents to build and evolve collective context. The magic happens in the emergent properties that arise when diverse agents interact within this shared space.

Cognition Engine Layer: Accelerators and guardrails that help agents think faster while operating within necessary constraints around compliance, security, and cost.

The challenge is particularly acute when organizations need to build collective intelligence across boundaries. “Think about shared memory in a heterogeneous way,” Pandey said. “We have agents from different parties coming together. So how do you evolve that memory and have emergent properties?”

Rethinking Foundation Model Training

While Pandey focuses on infrastructure, Goodman’s Humans& is taking a radically different approach—fundamentally changing how foundation models are trained. Rather than optimizing for longer autonomy, they’re centering training on extremely long-horizon interactions between humans and multiple agents, and between agents and multiple humans.

“By changing the training that we give to the foundation models and centering the training over extremely long horizon interactions, they’ll come to understand how interactions should proceed in order to achieve the right long-term outcomes,” Goodman explained.

This represents a deliberate divergence from the path pursued by many large AI labs. “Our goal is not longer and longer autonomy. It’s better and better collaboration,” Goodman emphasized. Humans& is building agents with deep social understanding—entities that know who knows what, can foster collaboration, and put the right specialists in touch at the right time.

The Guardrail Paradox: Rules vs. Judgment

As multi-functional agents touch every part of organizational systems, guardrails become both essential and problematic. Organizations need strict, rule-like boundaries, but humans don’t actually work that way. Instead, people operate on principles of minimal harm and contextual judgment.

“How do we provide the guardrails in a way which is rule-like, but also supports the outcome-based cognition when the models get smart enough for that?” Goodman asked.

Pandey extended this thinking to innovation teams that need to apply rules with judgment, not just follow them mechanically. “Figuring out what’s open to interpretation is a very collaborative task,” he said. “And you don’t figure that out through a set of predicates. You don’t figure that out through a document. You figure that out through common understanding and grounding and discovery and negotiation.”

Distributed Intelligence: The Path to Superintelligence

Both leaders agree that true superintelligence won’t come from increasingly powerful individual models, but from distributed systems. “While we build better and better models, and better and better agents, eventually we feel that true super intelligence will happen through distributed systems,” Pandey stated.

Intelligence will scale along two axes simultaneously: vertical (better individual agents) and horizontal (more collaborative networks), mirroring traditional distributed computing but with far more sophisticated interaction patterns.

However, Goodman cautions against a common misconception: “We can’t move towards a future where the AIs go off and work by themselves. We have to move towards a future where there’s an integrated ecosystem, a distributed ecosystem that seamlessly merges humans and AI together.”

The Race to Collective Intelligence

The implications are profound. Organizations that master collective intelligence will gain unprecedented competitive advantages—agents that truly collaborate can solve problems that isolated systems cannot touch. But achieving this requires rethinking everything from protocol design to foundation model training to organizational structures.

The question isn’t whether collective intelligence will arrive—it’s which organizations will build it first, and whether they’ll do so in ways that enhance rather than replace human capability. As Pandey and Goodman’s conversation reveals, we’re not just building better tools; we’re creating new forms of distributed cognition that could reshape how humans and machines work together.

The agents are learning to speak. The question is whether they’ll learn to think together before the opportunity passes to those who master this critical capability.

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