> ## Documentation Index
> Fetch the complete documentation index at: https://docs.roboticks.io/llms.txt
> Use this file to discover all available pages before exploring further.
# EU AI Act (Regulation 2024/1689)
> For AI-component robotics — high-risk classification triggers, conformity assessment, what Roboticks supports today and what is on the roadmap.
# EU AI Act (Regulation 2024/1689)
Regulation (EU) 2024/1689, the **EU AI Act**, came into force August 1, 2024 with phased application dates extending into 2027. It establishes a risk-based regime for AI systems placed on the EU market, with the strongest obligations on **high-risk AI systems** — a category that includes much of the AI used in robotics.
For robotics customers, the AI Act and [EU MR 2023/1230](/standards/eu-mr-2023-1230) interact: machinery containing AI safety components is regulated under both. Conformity assessment under the Machinery Regulation cross-references AI Act obligations for the AI parts.
**Roboticks is audit-readiness tooling, not a certified toolchain.** We assemble the evidence your notified body, certification body, or QA process ingests. We do not replace tool qualification (DO-178C, ISO 26262-8 TCL) and we do not issue conformity assessments. Verify the regulatory interpretations on this page against the standard text and your accredited assessor.
## Application timeline
| Date | What applies |
| --------------- | --------------------------------------------------------------------------------------------------------- |
| 1 August 2024 | Regulation in force |
| 2 February 2025 | Prohibited AI practices, AI literacy obligations |
| 2 August 2025 | General-purpose AI model obligations, governance bodies |
| 2 August 2026 | High-risk AI system obligations under Annex III |
| 2 August 2027 | High-risk AI system obligations under Annex I (safety components of regulated products — incl. machinery) |
For machinery vendors, **2 August 2027** is the critical date. AI safety components in machinery covered by EU MR 2023/1230 fall under Annex I and become subject to AI Act high-risk obligations from that date — roughly seven months after the Machinery Regulation itself applies (20 January 2027).
## High-risk classification
An AI system is **high-risk** if it falls into one of the categories in Article 6:
1. **Article 6(1)** — AI systems intended to be used as a safety component of a product covered by the Union harmonisation legislation listed in Annex I (incl. EU MR 2023/1230). Includes safety-related AI in machinery.
2. **Article 6(2)** — AI systems falling under the use cases listed in Annex III (biometric ID, critical infrastructure, education, employment, etc.). Most robotics applications are Article 6(1), not Article 6(2).
A machinery vendor with safety-related AI components is typically operating under Article 6(1) and must satisfy:
* Risk management system (Article 9).
* Data and data governance (Article 10) — including dataset documentation.
* Technical documentation (Article 11, Annex IV) — substantial overlap with the Machinery Regulation technical file.
* Record-keeping (Article 12).
* Transparency and provision of information to deployers (Article 13).
* Human oversight (Article 14).
* Accuracy, robustness, and cybersecurity (Article 15).
## What Roboticks supports today
* **Clause-level derivation** from the AI Act, particularly Articles 9 (risk management), 12 (logging), 14 (human oversight), 15 (accuracy/robustness/cybersecurity).
* **Cross-derivation with EU MR 2023/1230** — a requirement covering AI behaviour in machinery can derive from both Regulations simultaneously.
* **Test evidence for AI behaviour** — reproducible sim scenarios, MCAP capture of model inputs and outputs, deterministic regression tests across AI model versions.
* **Accuracy/robustness evidence** — JUnit aggregation of AI evaluation results; the same evidence-pack workflow applies.
* **Logging evidence** — Article 12 requires automatic logging; the platform's MCAP capture of model inferences satisfies the logging requirement when the test set covers the operational scope.
* **Cybersecurity evidence** — SBOM, SARIF, vulnerability scanning — applies equally to AI-containing safety components.
## What is out of scope today (Year 2 roadmap)
* **Dataset cards** — Article 10 expects structured documentation of training, validation, and test datasets. Roboticks does not currently author dataset cards. On the roadmap; integration with HuggingFace `datasets` metadata is the planned approach.
* **Bias evaluations** — Article 10 expects examination of training data for biases. Out of scope today; expected via connector to third-party bias-evaluation tools (e.g., Fairlearn, Aequitas) on the roadmap.
* **Model cards** — structured documentation of model purpose, performance, limitations. Roboticks can attach model cards to requirements as supplementary artefacts; native authoring is roadmap.
* **Notified-body interaction for AI** — we do not act as a notified body for AI Act conformity assessment, the same as for the Machinery Regulation.
The roadmap items are honest gaps. If they are blocking for your conformity claim today, plan to fill them with adjacent tooling and attach the outputs to the evidence pack as supplementary artefacts.
## Example AI-component requirement
```yaml theme={null}
- id: REQ-AI-007
title: Perception model robustness to adversarial lighting
type: safety
asil_pl: PLd
derives_from:
- standard: eu-ai-act-2024-1689
clause: "Article 15 Accuracy, robustness, and cybersecurity"
edition: "2024-08-01"
- standard: eu-mr-2023-1230
clause: "Annex III, EHSR 1.1.6 AI components"
edition: "2023-06-14"
text: |
The cobot perception model shall detect operator presence in
the workspace under the lighting variation set defined in
`scenarios/lighting_robustness.yaml`, with no false-negative
rate exceeding 0.001% across the full evaluation set.
The evaluation set covers nominal, low-light, high-glare,
backlit, and adversarially-illuminated conditions.
acceptance:
- test: tests/ai/test_perception_robustness.py::test_no_false_negatives_under_lighting_variation
```
## Suggested test patterns
| Clause | Pattern |
| -------------------------- | ----------------------------------------------------------------------------- |
| Article 9 Risk management | Hazard-driven test scenarios per the risk assessment; MCAP capture |
| Article 12 Logging | All test runs capture MCAP of model inputs and outputs |
| Article 14 Human oversight | Tests for handover-to-operator behaviours; verify intervention paths |
| Article 15 Accuracy | Per-class accuracy assertions; coverage of the operational design domain |
| Article 15 Robustness | Adversarial-input tests; perturbed-input tests; out-of-distribution detection |
| Article 15 Cybersecurity | Adversarial-attack tests; input-validation tests; SARIF coverage |
## Interaction with EU MR 2023/1230
For AI safety components in machinery:
* **Conformity assessment** under the Machinery Regulation may incorporate AI Act conformity assessment, depending on the route chosen and the notified body's scope.
* **Technical file** content overlaps substantially (Annex IV of the Machinery Regulation vs Annex IV of the AI Act).
* **Substantial-modification triggers** under both Regulations apply; a change that modifies the AI model in a safety-relevant way is a substantial modification under both.
The recommended posture: pin both Regulations, author requirements with cross-derivation where the obligations overlap, and produce one evidence pack covering both.
## Pinning
```bash theme={null}
rbtk standard pin eu-ai-act-2024-1689 --project acme-cobot/firmware
```
For AI-containing robotics, pinning typically accompanies pinning the relevant machinery standards (`industrial-robot-eu` or `cobot-eu` templates).
## Next steps
The Machinery Regulation with which the AI Act co-applies.
What we don't do — dataset cards, bias eval, notified-body services.