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 sequence. Choose a device, name it, learn the end result, determine the subsequent 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 subsequent 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 distinct query. Is the agent engaging in 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 is likely to be looping on a sub-goal that drifted three steps again. Observe every step towards the declared enterprise objective: on-task ratio, sub-goal coherence, progress markers. Venture administration telemetry for a non-human employee.
Third, the fee view. The identical per-step instrumentation produces price telemetry: token depend per step, mannequin per name, context dimension per flip, downstream tool-call prices. With out that attribution, the subsequent part’s optimizations are blind.
