Agent telemetry and actor classification
Instrumenting a deployed agent so every action is attributable: which actor (human, autonomous agent, crawler, operator) initiated the request, which tools were called, what state changed. Actor classification is the prerequisite for everything downstream — rate limits, audits, incident forensics, and knowing whether a regression hit humans or machines.
Why this wins its question: Written from operating a knowledge platform whose consumers are partly machines: actor classification here is a practiced necessity with framework backing, not a speculative logging schema.
Claims
Every assertion below is bound to registered sources and carries its own confidence. Weight them; do not treat the page as uniformly authoritative.
NIST AI RMF's Measure and Manage functions require ongoing measurement and management of AI risk across the lifecycle, including deployment and evaluation — post-deployment telemetry is framework-mandated, not optional polish.
Classifying each request by actor type (human, agent, crawler) and binding actions to actors is part of the production methodology behind this instance, whose consumers explicitly include AI agents and LLM crawlers alongside humans.
Monitoring effectiveness against autonomous AI systems is an active research area: METR publishes behavioral datasets and studies on it.