EU AI Act Evidence Package Structure: Per-Article Folders Explained
An AIAgentree evidence package is the ZIP archive a regulator or auditor receives in response to an EU AI Act evidence request. It is organized as per-article folders: data governance evidence (Article 10), record-keeping with the audit log export and tamper-evidence verification proof (Article 12), transparency with sampled decision packets each accompanied by its per-decision Decision Record PDF (Article 13), human oversight with approval-queue configuration, override log, escalation history and reviewer assignments (Article 14), quality management metrics (Article 17), post-market monitoring evidence (Article 72), and generated technical documentation with per-step model provenance (Article 11 / Annex IV). The package includes a README, metadata with checksums, and RENDER-FAILURES.txt — an honest manifest listing anything that failed to render, so nothing is silently dropped. Evidence packages support EU AI Act compliance responses; they do not by themselves make an organization compliant.
An evidence package is the audit answered in advance: one ZIP, organized the way the regulator reads — by article — with per-decision explanation documents beside the machine evidence.
- •Per-article folders — Articles 10, 12, 13, 14, 17, 72, plus technical documentation. The reviewer navigates by obligation, not by your system's schema.
- •Records beside packets — every sampled decision ships as a pair: the signed JSON packet and its human-readable Decision Record PDF.
- •Integrity travels along — the tamper-evidence verification proof is in the archive; checksums cover the archive itself.
- •RENDER-FAILURES.txt — anything that failed to render is listed, never silently dropped. Honesty as a feature.
Traceability tells you what an agent did. This cluster is about the next step: records that stand up — under the EU AI Act, NIST AI RMF and ISO/IEC 42001.
- 1.Defensible AI Decisions: The Complete Guide to Records an Auditor Can Act On
- 2.They Made Agents Traceable. We Make Their Decisions Defensible.
- 3.The Decision Record: the PDF an Auditor Actually Opens
- 4.Inside the Signed Decision Packet (the JSON)
- 5.Inside an EU AI Act Evidence Package (Folder by Folder)You are here
- 6.Human Oversight You Can Prove (Because Machines Can't Hold the Permission)
- 7.Article 86: What Affected Persons Can Demand From Your AI
- 8.From First Trace to Evidence Package: an EU AI Act Flow Walk-Through
- 9.A NIST AI RMF Self-Assessment You Can Actually Finish
- 10.ISO/IEC 42001 Readiness: 38 Controls, One Honest Score
- 11.Agent-Native Onboarding: Tracing in 10 Lines (and Agents Can Sign Themselves Up)
There is a moment in every audit where trust is decided, and it is not the moment you expect.
It is when the reviewer finds the file that says: "two artifacts failed to render; here is which ones."
An archive that admits its gaps is an archive whose completeness claims mean something. Let's open the ZIP.
Disclaimer: Informational content, not legal advice. Evidence packages support EU AI Act compliance responses; they do not by themselves make an organization compliant. Regulation text current as of August 2026.
Why the Unit Is a Package, Not a Portal
An evidence request from a market-surveillance authority is a document-shaped event: a scope (this system, this period), a deadline, and an expectation of something the authority can file, share internally, and cite. The wrong answer is "here are read-only credentials to our dashboard" — for all the reasons we covered in the Decision Record walk-through, portal access pushes verification cost onto the reviewer and disappears when the contract does.
The right answer is a self-contained archive, generated on demand: evidence-package-<agent>-<date>.zip. At its root sit three orientation files — a README describing the package, a metadata file with checksums and generation info, and the failure manifest we will get to. Below them, the folders follow the regulation's own structure, because the person opening the archive thinks in articles, not in your database schema. The pipeline that fills those folders is the five-station flow; this post is about what lands where.
The Per-Article Folders
Folder by folder, top to bottom of the archive:
| Folder | What is inside | The question it answers |
|---|---|---|
| article-10-data-governance/ | Data-governance evidence for the system's training and input data practices | Was the data behind decisions governed? (Art. 10) |
| article-12-record-keeping/ | The full audit log export, the tamper-evidence verification proof, retention policy configuration, event statistics | Were events automatically recorded, and is the record intact? (Art. 12) |
| article-13-transparency/ | Sampled decision packets — each beside its per-decision Decision Record PDF — plus deliberation-tree examples and rationale-coverage summaries | Can the system's outputs be interpreted? (Art. 13) |
| article-14-human-oversight/ | Approval-queue configuration, the override log, escalation history, reviewer assignments, response-time metrics | Was oversight real, and who exercised it? (Art. 14) |
| article-17-quality-management/ | Quality-score distributions, validation summaries, drift-detection results, decision-pattern analysis | Is there a working quality system around the AI? (Art. 17) |
| article-72-post-market/ | Post-market monitoring evidence | Is the system watched after deployment? (Art. 72) |
| technical-documentation/ | Generated technical documentation, system architecture, model provenance per deliberation step | The Annex IV question: what is this system, technically? (Art. 11) |
Two folders reward a closer look. The record-keeping folder does not just contain the logs — it contains the verification proof: the result of running tamper-evidence checks over the trail, included as an artifact. The reviewer does not have to take the archive's integrity on faith; the archive argues for itself. The obligations behind it are unpacked in the Article 12 guide and the Article 17 guide.
The Transparency Folder: Pairs, Not Piles
The transparency folder embodies the package's core design decision. Every sampled decision appears as a pair: packet_001.json — the signed machine packet with the deliberation, alternatives and evidence references — and packet_001.pdf — its per-decision Decision Record, the human-readable account with the embedded packet and verification result.
Why pairs matter: an audit team is never one person. The compliance lead reads the PDFs; the technical assessor verifies the JSON signatures; a legal reviewer may quote the narrative. Because both halves derive from the same sealed record — the PDF literally embeds the packet, as covered in the packet deep-dive — the three of them can never be reading diverging accounts. Explanation and evidence, per decision, in one place.
RENDER-FAILURES.txt: Honesty as a Feature
At the root of every package sits a plain-text file most vendors would never ship: RENDER-FAILURES.txt. If any artifact in the package failed to render — a PDF that errored, an export that timed out — it is listed there, by name. Nothing is silently dropped.
The naive view is that this file is an embarrassment. The reviewer's view is the opposite. An archive with no failure manifest offers no way to distinguish "complete" from "quietly incomplete" — and auditors are professionally obligated to assume the worst about silence. A manifest converts unknown unknowns into known, bounded, re-requestable items. In the best case it is empty, and the empty file is itself evidence: the generation process checks, and reports, its own completeness.
This is the same honesty discipline that runs through the whole cluster — the draft flag on the Record's narrative, the readiness score that can say not ready in the ISO/IEC 42001 walkthrough. Artifacts that admit their limits are artifacts whose positive claims can be believed.
The Dry-Run Technique
Generate a package for one agent, one period
Scope it small: your highest-stakes decision family, last 30 days.
Review it as the regulator
Open each folder cold. Check the pairs in transparency, the verification proof in record-keeping, and read the failure manifest first — that is what a reviewer does.
Fix the emptiest folder, regenerate
Empty folders map one-to-one to missing instrumentation. Close the gap, regenerate, compare. Repeat quarterly.
Sources & Further Reading
- •Regulation (EU) 2024/1689 (EU AI Act) — Articles 10, 11, 12, 13, 14, 17, 72; Annex IV — EUR-Lex (2024)
- •EU Artificial Intelligence Act — resource site — article texts and implementation tracking
Frequently Asked Questions
What is an EU AI Act evidence package?
A self-contained ZIP archive generated in response to a regulator's or auditor's evidence request. It is organized as per-article folders — data governance, record-keeping, transparency, human oversight, quality management, post-market monitoring, and technical documentation — containing the exports, sampled decision records, and verification proofs relevant to each obligation, plus a README, checksummed metadata, and a manifest of anything that failed to render.
How is the evidence package organized?
By article, because that is how the reviewer reads: article-10-data-governance, article-12-record-keeping (audit log export, tamper-evidence proof, retention policy), article-13-transparency (packet/Record pairs per sampled decision), article-14-human-oversight (approval config, override log, escalations, reviewer assignments), article-17-quality-management, article-72-post-market, and technical-documentation with per-step model provenance.
What is RENDER-FAILURES.txt?
The package's honest manifest: a plain-text file at the archive root listing any artifact that failed to render during generation, by name — nothing is silently dropped. It converts silent gaps into bounded, re-requestable items, and when it is empty, that emptiness is itself evidence that the generation process verifies its own completeness.
Why does every sampled decision appear twice in the transparency folder?
Because each decision ships as a pair: the signed machine-readable packet (JSON) and its per-decision Decision Record (PDF) with the packet embedded inside. Compliance readers use the PDFs, technical assessors verify the JSON signatures, and because both derive from the same sealed record, the two audiences can never be reading diverging accounts.
How long does it take to produce an evidence package?
It is generated, not assembled — every artifact derives from records the agents already produced while deciding. The practical constraint is upstream: the package is only as complete as the traces, packets and oversight events underneath it, which is why generating a dry-run package before any audit is the fastest way to find instrumentation gaps.
Does producing an evidence package demonstrate EU AI Act compliance?
It demonstrates the record-keeping, transparency, oversight and documentation evidence for the scoped system and period — a substantial part of an audit response, but not the whole of compliance, which also covers risk management, conformity assessment and organizational measures. The package supports the response; the claim deliberately stops there.
Related Topics
Related Articles
Defensible AI Decisions: The Complete Guide
The hub for this cluster: every artifact, every framework, and where to go deeper.
From First Trace to Evidence Package: an EU AI Act Flow Walk-Through
The pipeline that fills these folders, station by station.
The Decision Record: the PDF an Auditor Actually Opens
The PDF half of every pair in the transparency folder.
AIAgentree Team
AI Compliance
The AIAgentree team is building decision tracing infrastructure for AI agents. Our mission is to make AI reasoning visible, auditable, and improvable.
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