First, the security view. Traditional logging captures request and response, which assumes one human action per logged event. An agent’s unit of work is a chain. Pick a tool, call it, read the result, decide the next step. Twenty steps, some of them writing to production. Instrument every step as a durable audit object, independently queryable. Understand which tool was invoked, what data was accessed, what policy applied, and what the agent reasoned to justify the next step. That’s what Article 14 oversight requires for production.
Second, the business-outcomes view. Audit objects answer the CISO. The chief AI officer asks a different question. Is the agent accomplishing what we deployed it for, or burning compute on a tangent? An agent can run 200 tool calls, generate clean audit logs, and produce nothing. It might be looping on a sub-goal that drifted three steps back. Observe each step against the declared business purpose: on-task ratio, sub-goal coherence, progress markers. Project management telemetry for a non-human worker.
Third, the cost view. The same per-step instrumentation produces cost telemetry: token count per step, model per call, context size per turn, downstream tool-call costs. Without that attribution, the next section’s optimizations are blind.
