EU AI Act Article 12 (record-keeping for high-risk AI systems) and Article 19 (record retention by providers) are operationalised for agentic AI by a 14-field audit-evidence template that captures every agent decision in a regulator-queryable form: deployment ID, agent identity, session ID, ISO timestamp, user prompt, retrieved context with provenance, model output, planned action, action class, approval reference, executed action, tool-call audit chain, output disclosure surface, and policy version. Logs retained for the regulatory minimum (typically 6 months for the EU AI Act baseline, 5 to 7 years for sector-specific overlays like HIPAA and SOX) in a queryable format that supports under-4-business-hour evidence assembly. An enterprise that captures the 14 fields, retains them for the maximum applicable period, and instruments the queryable export has substantially completed Article 12 compliance for the agent layer; the residual work is integrating the agent log stream with the broader audit substrate.
Re-review 10 Jun 2026: regulatory legs verified verbatim against artificialintelligenceact.eu — Article 12(1): 'High-risk AI systems shall technically allow for the automatic recording of events (logs) over the lifetime of the system'; Article 19(1): logs kept 'for a period appropriate to the intended purpose of the high-risk AI system, of at least six months'. The 6-month baseline and the HIPAA (6y) / SOX (7y) overlays in the claim match the verified texts; the 14-field template and the 4-business-hour evidence-assembly window remain house IP layered on those texts. Article 12 audit-evidence template specification. 60-day review cadence given active regulator guidance development. Watches: (1) European AI Office guidance on Article 12 specifically (the Office's first detailed enforcement guidance is expected in Q3 2026 ahead of the August enforcement window), (2) Member-State-level retention period clarifications (Germany BfDI and France CNIL have already issued sector-specific guidance that extends the retention floor in some contexts), (3) ISO/IEC 42001 update that may formalise a parallel record-keeping standard, (4) vendor-platform native support for the 14-field structure (Microsoft, Anthropic, OpenAI, Google all have partial implementations as of April 2026).
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The claim: EU AI Act Article 12 (record-keeping for high-risk AI systems) and Article 19 (record retention by providers) are operationalised for agentic AI by a 14-field audit-evidence template that captures every agent decision in a regulator-queryable form: deployment ID, agent identity, session ID, ISO timestamp, user prompt, retrieved context with provenance, model output, planned action, action class, approval reference, executed action, tool-call audit chain, output disclosure surface, and policy version. Logs retained for the regulatory minimum (typically 6 months for the EU AI Act baseline, 5 to 7 years for sector-specific overlays like HIPAA and SOX) in a queryable format that supports under-4-business-hour evidence assembly. An enterprise that captures the 14 fields, retains them for the maximum applicable period, and instruments the queryable export has substantially completed Article 12 compliance for the agent layer; the residual work is integrating the agent log stream with the broader audit substrate.
About this register
The Reporting register tracks claims published from articles addressed to senior enterprise IT leaders — CIOs, IT directors, heads of platform. Claims are reviewed on a 30–90 day cadence; each review either reaffirms the claim, marks one substantive part as Partial, or marks it Not holding once the underlying evidence has been overtaken.
Recent corrections in Reporting
- AM-132 · Partial · 10 Jun 2026
One of four legs unanchored on re-review. The claim text attributes '12% of deployments clearing 300%+ ROI with 88% at or below break-even at 12-18 months' to the Stanford DEL 2026 Enterprise AI Playbook. Full-text verification on 10 Jun 2026 found no such figure in that source: the playbook (Pereira, Graylin, Brynjolfsson, Apr 2026) studies 51 successful deployments by design and contains no ROI distribution, no 300%-plus cohort, and no break-even measurement point (full finding at AM-029, correction of 10 Jun 2026). The only verified figure carrying the same 12/88 numerals is IDC research with Lenovo (via CIO.com, Mar 2025): roughly 88% of AI proof-of-concepts never reach production and roughly 12% graduate — a pilot-to-production graduation metric, not an ROI distribution. The Gartner 28%, McKinsey 23%/17%, and MIT NANDA 95% legs verify; they support a small high-performing tail and a large struggling body, but none documents the two-peak bimodal shape the claim asserts. Status Up -> Partial.
- AM-129 · Partial · 10 Jun 2026
One of three read-against anchors unanchored on re-review. The claim text cites 'Stanford Digital Economy Lab Enterprise AI Playbook (12/88 bimodal ROI distribution at 12-18 months)' and frames the realistic ROI band around 'the highest-discipline 12% cohort'. Full-text verification on 10 Jun 2026 found the playbook contains no 12/88 distribution, no bimodal ROI shape, and no 12-18-month ROI measurement point (full finding at AM-029, correction of 10 Jun 2026). The claim's core negative finding — no mid-market enterprise has produced a documented +240% ROI in 90 days under audited conditions — is unaffected; the McKinsey State of AI 2025 and MIT NANDA legs verify and continue to support it. The '12% cohort' framing has no verifiable referent. The only verified figure carrying the 12/88 numerals is IDC's pilot-graduation finding (roughly 88% of AI proof-of-concepts never reach production; via CIO.com, Mar 2025), a different metric. Status Up -> Partial.
- AM-201 · Partial · 10 Jun 2026
One of four named datasets unanchored on review. The claim text names 'Stanford DEL's 12% clearing 300%+ ROI vs 88% at or below break-even' as one of four independent datasets. Full-text verification on 10 Jun 2026 found the Stanford DEL Enterprise AI Playbook contains no such distribution — it studies 51 successful deployments by design and carries no ROI-realisation failure data (full finding at AM-029, correction of 10 Jun 2026). The McKinsey (23% scaling, 17% EBIT-attribution), Gartner (28% fully paying off), and MIT NANDA (95% no measurable P&L impact) datasets verify; the claim's spine stands on three datasets rather than four. The only verified figure carrying the 12/88 numerals is IDC's pilot-graduation finding (roughly 88% of AI proof-of-concepts never reach production; via CIO.com, Mar 2025), a different metric from an ROI distribution. Status Up -> Partial.
Reviews coming up in Reporting
- AM-063 · Holding · next +11d (27 Jun 2026)
AI agents executing financial transactions need a four-control bundle (action-approval gates by blast radius, kill-swit…
- AM-061 · Holding · next +11d (27 Jun 2026)
Production agentic-AI costs at scale routinely run multiples of POC projections, and a layered optimisation programme c…
- AM-003 · Partial · next +11d (27 Jun 2026)
GPT-5 Pro's tiered-subscription model forces enterprises to classify problems by computational difficulty — $200/month…