Teaching the orchestra to tune, next to play together ..

The recent emergence of A2A (Agent-to-Agent) by Google and the MCP (Model Context Protocol) by Anthropic gives us a promising foundation for technical interoperability among AI agents. It standardizes how they exchange data, invoke functions, and cooperate across organizational boundaries. These protocols represent a change from silos into modular, plug-and-play AI ecosystems.

However, A2A and MCP basically address the mechanics of communication. It is about how agents talk, but not about the meaning of their conversation, why they talk. In agent collaboration, and especially in dynamic multi-agent environments, being able to convey intent, recognize purpose, and perceive other agents goals is essential. This is even more important if you think of all different contexts in which agents may be cooperative, competitive, or even neutral toward each other.

For example, a scheduling agent might share data with another in a cooperative setup, assuming sharedgoals. But in a competitive scenario, like bidding for resources or negotiating outcomes, understanding the intent behind each actionbecomes critical. Merely exchanging structured tasks or OpenAPI requests (as MCP does), or triggering cross-agent workflows (as A2A enables), does not yet support reasoning about purpose, trust, or strategic alignment.

Even more complexity arises from the systemic level: often, agents operate within a larger context (a company, platform, or goal space). In such systems, it is vital for every agent to understand not just its local objective, but also the shared purpose driving the whole system. Conveying this higher-order intent in a machine-readable and interpretable way remains a largely unsolved problem. A2A and MCP provide lanes, but not yet the traffic signs, rules, or destination logic.

Right now, we are building telephones for agents; clear voice, stable lines, international dialing. A2A is like the switchboard, while MCP is the phonebook and the wiring. But we haven’t taught the agents language. We have not yet tackled the poetry of conversation.

We’re at the stage where agents can say: “I received your message.” But not yet: “I understand why you sent it and I trust your motive.”

Until that layer of intent modeling is developed, AI agents may function together, but not align.

This is not the first time the AI community has attempted to structure communication between intelligent agents. In fact, we might have a renewed look at one of the most relevant historical efforts and learn from it. The DARPA-funded initiative KQML (Knowledge Query and Manipulation Language) from the 1990s aimed to enable complex agent communication. Another important standard is ACL (Agent Communication Language) by FIPA indeed aiming at giving physical agents a common way to talk to each other, so they can work together, even if they are built by different people or companies. Based on Speech Act Theory, (J.L. Austin), it helps agents understand each other's goals and intents, so they can act and collaborate smoothly.

Unlike today’s protocols, KQML didn’t just focus on transmission of data or execution of functions. It was explicitly concerned with semantics and modeling communication acts, such as asking, informing, recommending, or requesting an action. Each message carried a performative that reflected not only the data but the intent behind the message.

While KQML faced adoption issues due to ambiguity in interpretation and lack of technical maturity, its central idea remains deeply relevant: communication among intelligent agents must eventually go beyond requests and responsesand move toward a shared understanding of mental states. And that must include intentions, beliefs, goals, and trust.

A2A and MCP are strong foundations, but they are largely syntactic and procedural, not semantic. KQML, for all its limitations, at least attempted to codify the mental model of one agent about another. ACL has it´s own variant of that too. This is something today’s protocols sidestep, but it will shown to be necessary to tackle. As we build increasingly autonomous and context-sensitive agent networks, the ability to model and perceive intent will show to be not just useful, but essential!