A benchmark is not a production environment
Traditional model evaluation asks whether a system can answer a task correctly under controlled conditions. Operational agents introduce a wider set of questions. Did the agent access the appropriate record? Did it call the correct tool? Did it stop at the right point? Could a person reconstruct why it acted? Can a failure be caught before it becomes an operational incident? Those questions cannot be deferred until after deployment, because they are properties of the workflow, not simply of the model.
The control surface expands with agency
The deeper shift is from evaluating output to evaluating action. An agent can coordinate systems, hand off work, request approvals and trigger downstream changes. This creates a control surface across identity, data, tools and escalation paths. The implementation work therefore looks increasingly like operations engineering: observability, test cases based on real tasks, staged releases, exception handling and explicit ownership.
What would make the thesis weaker
The thesis weakens if most organisational value remains in low-risk drafting and retrieval, where a human is already reviewing every step. It also weakens if reliable agent behaviour proves too expensive to test and maintain outside a small number of highly structured workflows. The evidence to watch is not marketing claims about autonomy. It is the gradual appearance of stable operational metrics: task completion, exception rates, intervention rates and the cost of recovery when automation fails.