According to new reporting by VentureBeat, modern AI agents can exchange messages and identify tools or services with one another, but they *still lack the ability to truly understand each other’s goals or reason together toward a shared outcome. Current agent communication protocols — like MCP (Model Context Protocol) and A2A (Agent-to-Agent) — enable basic message passing and discovery, yet they don’t transmit the intent, shared context, or underlying reasoning that would allow agents to collaborate as a cohesive team. This means multi-agent systems often act more like loosely connected task performers rather than coordinated problem-solvers.
In practice, that limitation shows up as a lack of semantic understanding across agent interactions. For example, separate AI agents handling medical scheduling, insurance verification, and pharmacy checks can complete their individual tasks, but they don’t synthesize the full picture of a patient’s needs. Because each agent works within its own narrow scope without shared goals or context, they may recommend incompatible outcomes or fail to optimize for a common objective.
To overcome this, industry researchers and companies like Cisco’s Outshift are proposing new architectural layers — dubbed the “Internet of Cognition” — that would let agents share intent, shared context graphs, and goal states across interactions. This framework involves semantic protocols above simple messaging, distributed shared context memory, and mechanisms for agents to compound insights and enforce safety constraints. The vision is not just connectivity but cooperation where one agent’s learning can benefit others solving related problems.
Experts note that moving beyond superficial communication toward true agent collaboration will require both new standards and industry-wide coordination, since agents need a common understanding of tasks and goals rather than isolated task execution. Only when agents can align their reasoning and maintain a shared context will multi-agent systems be able to work together toward complex goals instead of just passing data around.
