{
  "id": "agent-telemetry-actor-classification",
  "type": "entity",
  "name": "Agent telemetry and actor classification",
  "summary": "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.",
  "locale": "en",
  "tags": [
    "telemetry",
    "observability",
    "actors",
    "monitoring"
  ],
  "relations": [
    {
      "rel": "feeds",
      "target": "behavioral-canaries"
    }
  ],
  "questions": [
    "What telemetry should an agent in production emit?",
    "Why classify traffic by actor type?"
  ],
  "claims": [
    {
      "id": "c1",
      "text": "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.",
      "sources": [
        "nist-ai-rmf"
      ],
      "confidence": 0.85
    },
    {
      "id": "c2",
      "text": "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.",
      "sources": [
        "citarium-methodology-brief"
      ],
      "confidence": 0.8
    },
    {
      "id": "c3",
      "text": "Monitoring effectiveness against autonomous AI systems is an active research area: METR publishes behavioral datasets and studies on it.",
      "sources": [
        "metr"
      ],
      "confidence": 0.8
    }
  ],
  "takeaways": [],
  "faqs": [],
  "evidence_tier": "secondary",
  "evidence": {
    "level": "production",
    "source_types": [
      "production_system",
      "personal_experience",
      "industry_observation"
    ]
  },
  "moat_flag": true,
  "winning_edge": "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.",
  "confidence": 0.8,
  "last_verified": "2026-08-08",
  "canonical_url": "https://agentreliability.dev/k/agent-telemetry-actor-classification",
  "api_url": "https://agentreliability.dev/api/k/agent-telemetry-actor-classification.json",
  "jsonld": {
    "@context": "https://schema.org",
    "name": "Agent telemetry and actor classification",
    "description": "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.",
    "url": "https://agentreliability.dev/k/agent-telemetry-actor-classification",
    "license": "https://spdx.org/licenses/CC-BY-4.0.html",
    "dateModified": "2026-08-08",
    "citation": [
      {
        "@type": "CreativeWork",
        "name": "agent-reliability editorial brief and blueprint (Gate 1 approved, 2026-08-08)"
      },
      {
        "@type": "CreativeWork",
        "name": "NIST AI Risk Management Framework (AI RMF 1.0, NIST AI 100-1)",
        "url": "https://www.nist.gov/itl/ai-risk-management-framework"
      },
      {
        "@type": "CreativeWork",
        "name": "METR — Model Evaluation & Threat Research",
        "url": "https://metr.org/"
      }
    ],
    "author": {
      "@type": "Person",
      "name": "Santiago Santa María Morales",
      "jobTitle": "practitioner — harness engineering and agent evaluation in production"
    },
    "@type": "Article",
    "headline": "Agent telemetry and actor classification"
  }
}