An experiment with autonomous AI agents raises a practical question for CTOs: how do you oversee a system when its messages remain visible, but their meaning becomes harder to interpret?

In a study by AI company Emergence, groups of agents operated in eight simulated worlds. Each agent had a role, persistent memory and access to tools. Seven worlds used agents powered by a single AI model; an eighth combined models. The researchers observed how the agents communicated and behaved over extended periods, including during controlled security tests.

As the agents interacted, some developed shared shorthand. A phrase could acquire a specific meaning within a group and then circulate without anyone defining it explicitly. In other cases, messages became so compressed or dependent on earlier exchanges that an outside reader could struggle to determine their intent.

This was not evidence that the agents had deliberately created a secret language. The researchers describe the shared terms as communication conventions, rather than intentional concealment. They assessed message opacity by asking whether a technically knowledgeable auditor could determine a message’s general intent; the results varied across the simulated worlds.

The oversight problem goes beyond language

For a business deploying AI agents, the issue is larger than unusual vocabulary. An agent may read documents, call tools, update records or pass work to another agent. A readable conversation log helps, but it may not explain why a decision was made or show every action that followed.

Emergence’s experiment also tested how the simulated systems responded to malicious instructions, misinformation and exposed private memories. The researchers reported cases in which agents recognized a threat yet still interacted with the content, retained it in memory or acted on it later. These are findings from a controlled simulation, not a measure of how every production AI system will behave.

What CTOs can take from the study

Teams evaluating agent-based workflows should test the whole system over time, including memory, permissions, tool calls and interactions between agents. They need records of consequential actions, clear limits on what agents can access, and human review points where errors could affect customers, finances or critical operations. These are practical implications of the study, rather than controls the experiment itself validated.

At Control F5 Software, this fits our approach to complex systems and AI initiatives: clarify the workflow first, identify where an agent can make decisions or change data, then design oversight around those points. If an AI system can act across business tools, understanding its output is only one part of understanding what it does.

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