The fundamental problem holding back AI progress is that agents can connect, but they can't think together. Vijoy Pandey, GM and SVP of Outshift by Cisco, explains why the missing piece of AI is horizontal scaling.

1. First things first: Why does AI feel stuck right now?

Pandey: Scaling up models will move us toward superintelligence, but progress is slowing. Key technical experts and researchers alluding to this trend are calling out for an "age of research," and the need for the next algorithmic breakthroughs.

Here's what could accelerate the timeline: scaling out, not just up. The collective is exponentially more powerful than the individual. We've been building smarter individual agents while ignoring how they could think together.

• The limitation isn't computing, data or parameters; it's that insights stay trapped inside isolated systems and do not compound.

2. Okay, but: Why does this shift from isolated to collective intelligence matter to leaders today?

Pandey: Because the world's most complex problems — from drug discovery to global supply chain optimization — cannot be solved by a single model or agent. It needs teams of agents, from different vendors and with different expertise, working towards common goals.

• Right now, when one agent figures something out, that knowledge only lives within that agent. Every other agent starts from scratch.

Without infrastructure for collective intelligence, you're deploying isolated experts that can't build on each other's work and cannot leverage each other's expertise.

3. The details: What does "thinking together" mean?

Pandey: The industry thinks connecting APIs or message passing between agents solves this. It doesn't. That's syntactic communication — agents can pass data but not meaning. The payload of these messages is not well understood between agents.

• Real coordination requires semantic understanding, meaning they align on objectives, reconcile conflicts, coordinate and negotiate on the objective, and build shared context that persists across all agents institution-wide, regardless of where these agents come from.

Most multi-agent systems in enterprises just execute predefined workflows in silos with their own understanding of what the goals could be. They're not doing collective reasoning or solving problems that require cooperative innovation.

4. Looking ahead: As we move toward this collaborative model, what are the implications for trust and governance?

Pandey: The implications are profound. Agents might appear online like humans but operate at machine speeds and scale. It requires going back to first principles and rethinking identity and access control, basing it off the units of work: accessing a tool or performing a task, and not based off roles.

• That requires semantically understanding what tasks are being performed by agents. Moreover, trust can no longer be centralized — it must be distributed and verifiable across the entire system.

We need new cognitive guardrails, embedded within the framework itself, to ensure agent collaborations adhere to ethical policies, privacy laws and security protocols. Without this, scaling distributed intelligence becomes an unacceptable risk.

5. Here's what else: How should leaders prepare for a future of distributed intelligence?

Pandey: Start by mapping where cognitive and meaningful collaboration breaks down. Where do teams solve the same problems repeatedly because insights don't transfer between systems? Where do agents exist but can't coordinate?

Then build three capabilities that together enable an agentic cognitive evolution:

• Protocols for semantic coordination beyond APIs.
• Fabric for institution-wide shared memory and knowledge.
• Reasoning engines with built-in governance.

The "Scaling Out Superintelligence" white paper has the full architecture. Start pilot projects with design partners now — test these principles in real-world workflows.

6. The takeaway: If there's one idea leaders should take away from this, what is it?

Pandey: The journey to artificial superintelligence will not be achieved only through smarter individual AI brains. It requires empowering every entity — teams of AI agents and individuals — across your organization to meaningfully collaborate on shared objectives and reach their highest potential together.

• This has parallels to the Cognitive Revolution in humans from 70,000 years ago. Humans didn't build civilizations through individual geniuses. We did it through the advent of language — through shared intent, cumulative knowledge and collective innovation.

The most valuable companies of the next decade will be those that master the science of collective intelligence.

Discover the blueprint for moving from isolated agents to collective intelligence.