TL;DR for operators The paper argues that trustworthy AI agents need more than accurate final answers. Once an agent can retrieve documents, call APIs, write memory, modify databases, send messages, or coordinate with other agents, trust depends on whether the organisation can reconstruct how the output or action happened. The useful mechanism is: ...
AI agents do not need feelings to become difficult to read. That is already enough trouble. A long-running agent can enter a workflow, absorb context, make decisions, and gradually behave as though the situation has a particular “shape.” The system may not merely react to the latest input. It may carry forward a learned orientation: this client is risky, this process is stable, this market regime is noisy, this user wants speed more than precision. In ordinary product language, we call that “context.” In engineering dashboards, we often reduce it to memory, state, embeddings, or hidden activations. In philosophical language, one might be tempted to call it a perspective. ...