First, the safety view. Conventional logging captures request and response, which assumes one human motion per logged occasion. An agent’s unit of labor is a series. Choose a device, name it, learn the outcome, resolve the following step. Twenty steps, a few of them writing to manufacturing. Instrument each step as a sturdy audit object, independently queryable. Perceive which device was invoked, what knowledge was accessed, what coverage utilized, and what the agent reasoned to justify the following step. That’s what Article 14 oversight requires for manufacturing.
Second, the business-outcomes view. Audit objects reply the CISO. The chief AI officer asks a unique query. Is the agent undertaking what we deployed it for, or burning compute on a tangent? An agent can run 200 device calls, generate clear audit logs, and produce nothing. It may be looping on a sub-goal that drifted three steps again. Observe every step in opposition to the declared enterprise goal: on-task ratio, sub-goal coherence, progress markers. Mission administration telemetry for a non-human employee.
Third, the fee view. The identical per-step instrumentation produces value telemetry: token rely per step, mannequin per name, context measurement per flip, downstream tool-call prices. With out that attribution, the following part’s optimizations are blind.
