{
  "format": "minglabs/v1",
  "surface": "insights-answer",
  "slug": "what-belongs-in-an-ai-agents-audit-trail",
  "kind": "answer",
  "pageType": "Answer",
  "question": "What belongs in an AI agent's audit trail?",
  "topic": "AI agent audit trail",
  "targetQueries": [
    "what belongs in an AI agent audit trail",
    "AI agent audit trail requirements",
    "AI agent logging requirements",
    "how to audit AI agents",
    "EU AI Act logging requirements for AI agents",
    "AI agent traceability"
  ],
  "url": "https://www.minglabs.com/insights/answers/what-belongs-in-an-ai-agents-audit-trail",
  "htmlUrl": "https://www.minglabs.com/insights/answers/what-belongs-in-an-ai-agents-audit-trail",
  "partOf": "https://www.minglabs.com/insights/answers",
  "title": "What belongs in an AI agent's audit trail?",
  "standout": "Regulators ask one question: show me exactly what the agent did. MING Labs traces ten fields per agent action to answer it inside 24 hours.",
  "hook": "Ten fields per action. MING Labs' standard traces, for every agent action: agent identity and version, the triggering event, the job-description scope and authority level in force at execution, tool calls with parameters, data accessed, the output artefact and its destination, cost and duration, human review, and the downstream consumption chain. The test is the regulator's question: show me exactly what this agent did, when, with what inputs, who authorised it, and what the outputs were used for, answered inside 24 hours.",
  "citableAnswer": "According to MING Labs' operating standard, an AI agent's audit trail records ten fields per action, from agent identity and version through triggering event, authority level, tool calls, data accessed, output artefact, cost, and human review, to the downstream consumption chain, so that 'show me exactly what this agent did' is answerable within 24 hours.",
  "summary": "An AI agent's audit trail should reconstruct any action from ten fields: agent identity and version, triggering event, job-description scope at execution, authority level, tool calls, data accessed, output artefact, cost and duration, human review, and downstream consumption. The organising principle: every entry maps to a named element of the agent's job description, so the trail is the job description in motion, not a stream of opaque events. The EU AI Act makes automatic event logging a legal requirement for high-risk systems from August 2026.",
  "pillar": "Hybrid Organisation",
  "datePublished": "2026-07-17",
  "dateModified": "2026-07-17",
  "freshness": {
    "updated": "July 2026",
    "nextReview": "January 2027"
  },
  "evidenceTier": "proprietary",
  "confidence": "B",
  "sources": [
    {
      "id": "S1",
      "title": "MING Labs operating record: per-action trace standard, retention policy, and fleet review cadence",
      "publisher": "MING Labs (internal)",
      "date": "2026-07-01",
      "supports": [
        "ten fields traced per agent action, each mapped to a named element of the agent's job description",
        "100 percent of fleet tasks logged with timestamp, cost, and outcome; no off-ledger work",
        "retention: 90 days operational, 12 months for security events, contract plus two years for client work",
        "review cadence: weekly automated audits across every agent, monthly manual review for high-severity work"
      ]
    },
    {
      "id": "S2",
      "title": "Regulation (EU) 2024/1689 (EU AI Act), Article 12: record-keeping",
      "publisher": "European Union",
      "date": "2024-07-12",
      "url": "https://artificialintelligenceact.eu/article/12/",
      "supports": [
        "high-risk AI systems shall technically allow for the automatic recording of events (logs) over the lifetime of the system",
        "the requirement applies from 2 August 2026"
      ]
    },
    {
      "id": "S3",
      "title": "Regulation (EU) 2024/1689 (EU AI Act), Article 26: obligations of deployers of high-risk AI systems",
      "publisher": "European Union",
      "date": "2024-07-12",
      "url": "https://artificialintelligenceact.eu/article/26/",
      "supports": [
        "deployers must keep the logs automatically generated by a high-risk AI system for at least six months"
      ]
    }
  ],
  "faqs": [
    {
      "q": "Isn't this what the agent platform's built-in logging already does?",
      "a": "Platform logs record events; an audit trail answers questions. A stream of tool calls and token counts tells you what happened at the API layer, but not whether the agent was inside its mandate, who had authorised that mandate, or what became of the output. The difference is the mapping: in MING Labs' standard, every trail entry corresponds to a named element of the agent's job description, so the trail reads as the job description in motion. That is the property that turns a log into an answer."
    },
    {
      "q": "How long should agent logs be kept?",
      "a": "MING Labs keeps operational logs for 90 days, security-relevant events for 12 months, and client-work records for the contract term plus two years. The regulatory floor in the EU is lower but real: deployers of high-risk AI systems must keep the automatically generated logs at least six months under Article 26 of the EU AI Act. The practical rule is to set retention by the question you may have to answer, not by storage cost: a client dispute or a security review arrives on its own schedule."
    },
    {
      "q": "Does the EU AI Act require an audit trail for every AI agent?",
      "a": "The hard legal duty attaches to high-risk systems: Article 12 requires them to technically allow automatic event logging over their lifetime, applicable from 2 August 2026, and Article 26 obliges deployers to retain those logs. Most business agents will not be classified high-risk. But the question the trail answers, what exactly did this agent do and on whose authority, is asked by enterprise IT, legal, and clients regardless of classification, and retrofitting traceability onto a running fleet costs far more than logging from day one."
    },
    {
      "q": "Who reviews the trail, and how often?",
      "a": "At MING Labs, an automated audit runs weekly across every agent in the fleet, checking for drift, security anomalies, and performance regressions; high-severity work additionally gets a monthly manual review. Review itself is part of the record: field nine of the trace captures who reviewed an output, when, and what they decided, so the oversight layer is auditable by the same trail it audits."
    }
  ],
  "parentArticleSlug": null,
  "relatedConceptSlugs": [
    "what-is-the-comprehension-obligation",
    "what-is-hybrid-organisation"
  ],
  "relatedArticleSlugs": [
    "we-fired-an-ai-agent"
  ]
}