AI Agents Can Finally Talk to Each Other — But They Still Don’t Understand Each Other

The multi-agent AI revolution is here, but there’s a critical problem that’s holding back true collaboration: agents can exchange messages all day long, but they fundamentally don’t understand each other’s intent or context.

This isn’t just a technical quirk—it’s a massive bottleneck that’s preventing AI agents from working together effectively, costing organizations valuable time and resources while limiting the potential of multi-agent systems.

The Communication Gap That’s Costing You Money

Think about how your current AI agents operate. They can pass messages, identify tools, and complete individual tasks. But when Agent A hands off to Agent B, there’s no shared understanding of what they’re collectively trying to achieve. It’s like having a team where everyone speaks different languages but can only pass notes back and forth.

Consider a real-world scenario: a patient trying to schedule a specialist appointment. With current protocols, you’d have multiple agents working in isolation—a symptom assessment agent, a scheduling agent, an insurance verification agent, and a pharmacy agent. Each completes their task, but they’re not actually collaborating.

The symptom agent might identify potential drug interactions based on the patient’s history, but this crucial information never makes it to the pharmacy agent because “potential drug interactions” wasn’t explicitly in the scope of what needed to be shared. The scheduling agent books the nearest available appointment without knowing the insurance agent found better coverage at a different facility.

They’re connected, but they’re not aligned on the goal: finding the right care for this specific patient’s situation.

Why Current Protocols Aren’t Enough

Existing solutions like MCP (Model Context Protocol), A2A (Agent-to-Agent), and Cisco’s own AGNTCY protocol handle the mechanics of agent communication—they let agents discover tools and exchange messages. But these operate at what Cisco’s Outshift division calls the “connectivity and identification layer.” They handle syntax, not semantics.

This is the crucial distinction: current protocols can tell agents what tools are available and pass data between them, but they can’t convey why an agent is taking a specific action or what broader goal it’s working toward.

The Three Missing Pieces of Agent Collaboration

For agents to move from mere communication to true collaboration, they need to share three critical things, according to Outshift:

Pattern recognition across datasets – Understanding how different pieces of information relate to each other

Causal relationships between actions – Knowing why certain actions lead to specific outcomes

Explicit goal states – Sharing not just what they’re doing, but why they’re doing it and what success looks like

Without these elements, AI agents remain semantically isolated. They might be individually capable, but goals get interpreted differently, coordination burns cycles, and nothing compounds. One agent learns something valuable, but the rest of the multi-agent system still starts from scratch.

Enter the Internet of Cognition

Cisco’s Outshift is proposing a new architectural approach called the “Internet of Cognition” to solve this fundamental problem. It’s not just another protocol—it’s a complete framework for enabling semantic collaboration between AI agents.

The proposed architecture introduces three transformative layers:

Cognition State Protocols: A semantic layer that sits above message-passing protocols. Agents share not just data but intent—what they’re trying to accomplish and why. This lets agents align on goals before acting, rather than clarifying after the fact.

Cognition Fabric: Infrastructure for building and maintaining shared context. Think of it as distributed working memory: context graphs that persist across agent interactions, with policy controls for what gets shared and who can access it. System designers can define what “common understanding” looks like for their use case.

Cognition Engines: Two types of capability. Accelerators let agents pool insights and compound learning—one agent’s discovery becomes available to others solving related problems. Guardrails enforce compliance boundaries so shared reasoning doesn’t violate regulatory or policy constraints.

The Industry-Wide Challenge

Outshift is positioning this framework as a call to action rather than a finished product. The company is working on implementation but emphasized that semantic agent collaboration will require industry-wide coordination—much like early internet protocols needed buy-in to become standards.

“We can send messages, but agents do not understand each other, so there is no grounding, negotiation or coordination or common intent,” Vijoy Pandey, general manager and senior vice president of Outshift, told VentureBeat.

The company is in the process of writing the code, publishing the specifications, and releasing research around the Internet of Cognition. They hope to have a demo of the protocols soon.

Why This Matters for Your Business Right Now

The practical question for teams deploying multi-agent systems today is stark: Are your agents just connected, or are they actually working toward the same goal?

If you’re running multi-agent workflows, you’re likely experiencing the friction firsthand. Your agents might be completing individual tasks competently, but the lack of shared understanding means:

• Duplicated effort as agents independently discover the same information
• Conflicting recommendations because agents interpret goals differently
• Missed opportunities as valuable insights stay siloed within individual agents
• Increased coordination overhead as humans must constantly mediate between agents

The Compounding Value of Shared Cognition

Stanford professor Noah Goodman, co-founder of frontier AI company Humans& and speaking at VentureBeat’s AI Impact event in San Francisco, noted that innovation happens when “other humans figure out which humans to pay attention to.” The same dynamic applies to agent systems: as individual agents learn, the value multiplies when other agents can identify and leverage that knowledge.

This is the key insight: the Internet of Cognition isn’t just about making agents work together better—it’s about creating systems where the whole becomes exponentially more valuable than the sum of its parts.

When agents can truly understand each other’s intent and share context, they can:

• Compound learning across the entire system rather than starting from scratch
• Align on goals before taking action, reducing wasted effort
• Share insights that become available to all relevant agents
• Coordinate naturally without constant human intervention

The Future of Multi-Agent Systems

The Internet of Cognition represents a fundamental shift in how we think about AI agent collaboration. It’s moving beyond the current paradigm of connected but isolated agents to create truly collaborative systems where agents work together with shared understanding and aligned intent.

For businesses investing in AI automation today, this framework offers a glimpse into the future of multi-agent systems—and a roadmap for what’s needed to get there. The question isn’t whether this level of collaboration will become standard, but how quickly the industry can come together to make it a reality.

The agents are talking. Now we need to make sure they’re actually listening and understanding each other.

Tags & Viral Phrases

• AI agents talking to each other
• Internet of Cognition
• Multi-agent collaboration breakthrough
• AI agents don’t understand each other
• Semantic agent collaboration
• Cisco Outshift innovation
• The missing piece in AI agent communication
• Why your AI agents aren’t really working together
• The future of multi-agent systems
• AI agents need shared context
• Breaking down agent silos
• True AI collaboration is coming
• The compounding value of shared AI learning
• From connected to collaborative AI
• The next evolution in agent communication
• AI agents finally understanding intent
• The business cost of agent isolation
• How to make your AI agents actually work together
• The Internet of Cognition explained simply

,