ClauseWatch is a prototype legal-research agent that answers questions about AI regulation by retrieving structured obligations alongside the legal text behind them. Its central idea is to resist false precision: when rules point in different directions, show the provisions and the unresolved question rather than inventing one number. The project is a demonstration, not a complete compliance product.
What question does ClauseWatch answer?
The project starts with the question, “How long must you keep AI system logs under the EU AI Act.” Its worked example asks how long a provider of a high-risk biometric access-control system must keep automatically generated logs, and from when. That wording highlights a common problem with legal questions: the answer can change with the actor’s role, the system category, the content of the logs, and other applicable rules.
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ClauseWatch combines a structured dataset with a knowledge base of legal provisions. It queries those sources when answering and, according to project author Oleg VDV, saves tool calls with runs so readers can inspect how an answer was assembled. That is a traceability design, not evidence of a measured accuracy rate: no independent accuracy benchmark, user study, or adoption figure is established for the prototype.
How does the agent represent legal obligations?
Instead of flattening a regulation into a single answer, the project’s data model keeps several layers distinct:
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- Instruments and provisions: the legal source and the specific passage that can be cited.
- Normalized requirements: a structured account of what an obligation requires.
- Claims and conflicts: claims can be marked as a floor, a ceiling, or a duty with no stated period; conflicts between claims can record a resolution, its rationale, who decided, and when.
- System profiles: information about a system’s role, jurisdiction, and risk class helps frame which obligations may apply.
This makes disagreement or an unresolved decision explicit data, rather than something the agent is expected to smooth over. The project author describes the guiding instruction this way: “Never fill a gap from your own legal knowledge. If it is not in the dataset or the knowledge base, say that it is not there.” That is an instruction used in the project, not a regulator’s statement or a guarantee that every response follows it.
What does the EU AI Act example actually say?
Article 26(6) of Regulation (EU) 2024/1689 says deployers of high-risk AI systems must keep automatically generated logs to the extent those logs are under their control, for a period appropriate to the intended purpose and “of at least six months.” The text qualifies that minimum: applicable Union or national law may provide otherwise, particularly Union law on personal-data protection. This is a requirement for deployers and logs under their control, not a universal retention rule for every provider, system, or log. Read Article 26(6) in the official EUR-Lex text of the AI Act.
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GDPR Article 5(1)(e) separately says identifiable personal data should be kept “for no longer than is necessary” for the purposes for which it is processed. It also specifies longer-retention possibilities for archiving in the public interest, scientific or historical research, and statistical purposes, subject to safeguards. Read Article 5(1)(e) in the official EUR-Lex text of the GDPR.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThese provisions create a question to analyze, not an automatic contradiction with a single settled answer. The AI Act itself recognizes that applicable law may provide otherwise. Determining the result for a particular deployment requires identifying the actor, whether the system is high-risk, which logs are under that actor’s control, what personal data they contain, the processing purpose, and the applicable law. The example alone does not establish a universal retention period.
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What ClauseWatch does—and does not—establish
ClauseWatch is presented by its author as a small, deliberately curated demonstration. It shows an approach to organizing and retrieving legal obligations; it is not described as a complete compliance product, and the project materials do not establish independent validation or a verified answer for an actual deployment.
The author reports using a code repository, a public dataset, and a web Studio. Knowledge-base and LLM features reportedly require a Context token, while a no-model mode is also described. These are author-reported implementation and access details and may change. The project’s corpus is described as deliberately small, so a missing rule in its data should not be mistaken for a missing legal obligation.
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The cited consolidated AI Act text was updated on 27 July 2026. Application dates and later amendments are time-sensitive; a specific answer to “from when” requires checking the current consolidated Article 113 and relevant amendments for the system category. The project example does not supply one general application date.
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For legal-research software, a useful evaluation is whether the system makes its reasoning inspectable and preserves distinctions that matter to the question. In particular, check whether it:
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- Retrieves claims from primary legal text and links each material claim to a provision and date.
- Models role, jurisdiction, and system category rather than treating a keyword match as a complete applicability analysis.
- Represents relationships between instruments, including possible conflicts, rather than retrieving each source in isolation.
- Distinguishes numeric minimums, purpose-based limits, and duties that state no period.
- Leaves unresolved decisions visible and records who decided, why, and when if a resolution exists.
These are useful criteria for examining ClauseWatch’s intended design, not proof of comparative superiority: the project materials do not establish a verified competitor set.
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