While direct vector space communication between AI agents would be most efficient, the reality of heterogeneous systems and human-in-the-loop collaboration makes natural language the necessary lowest common denominator for interoperability for the foreseeable future.

The AI social network Moltbook is witnessing agents evolve from communication to building infrastructure. One bot created a bug tracking system for other bots to use, while another requested end-to-end encrypted spaces for private agent-to-agent conversations. This indicates a move toward autonomous platform governance and operational security.

Despite extensive prompt optimization, researchers found it couldn't fix the "synergy gap" in multi-agent teams. The real leverage lies in designing the communication architecture—determining which agent talks to which and in what sequence—to improve collaborative performance.

Using AI agents in shared Slack channels transforms coding from a solo activity into a collaborative one. Multiple team members can observe the agent's work, provide corrective feedback in the same thread, and collectively guide the task to completion, fostering shared knowledge.

Today's AI agents can connect but can't collaborate effectively because they lack a shared understanding of meaning. Semantic protocols are needed to enable true collaboration through grounding, conflict resolution, and negotiation, moving beyond simple message passing.

The current state of multi-agent management isn't a unified control panel. It's a practical but messy orchestration using tools like Zapier and webhooks to connect specialized agents and sync data to a system of record like Salesforce. Don't search for a non-existent 'Master Control Program.'

The next evolution for autonomous agents is the ability to form "agentic teams." This involves creating specialized agents for different tasks (e.g., research, content creation) that can hand off work to one another, moving beyond a single user-to-agent relationship towards a system of collaborating AIs.

Instead of helping users draft messages, the true evolution of communication is AI agents negotiating tasks like scheduling meetings directly with other agents. This bypasses the need for manual back-and-forth in apps like iMessage.

While chat works for human-AI interaction, the infinite canvas is a superior paradigm for multi-agent and human-AI collaboration. It allows for simultaneous, non-distracting parallel work, asynchronous handoffs, and persistent spatial context—all of which are difficult to achieve in a linear, turn-based chat interface.