In 2026, organizations are deploying AI assistants and agents at scale on top of their existing documentation: manuals, knowledge bases, registries, process descriptions, and protocols. This sudden shift brings a dormant problem to the surface. Documentation typically describes the reality of the moment it was written, not the reality of today.
A human reading an outdated manual can usually tell from the context. An AI assistant, however, reads it as absolute truth and reproduces the text fluently and convincingly. Research into knowledge conflicts (Xie et al., 2023, arXiv 2305.13300) shows that large language models heavily follow the provided context, even if it contradicts what the model inherently knows from its training. The result? An AI system does not correct outdated documentation; it amplifies it. Keeping documentation up-to-date for AI is therefore no longer simple administrative hygiene, but a strict prerequisite for responsible deployment.
Information Alignment vs. Information Architecture
In our previous article on one fact, one place, we discussed the information architecture for organizations where AI agents operate. That covered the question of where information should reside. Information alignment is the necessary follow-up: it addresses whether the stored information is still accurate, and how you organize the process to maintain it.
Information alignment is the continuous synchronization of the documented description with the actual status (what exists, what applies, who owns it, and until when). Without rigorous alignment, documentation inevitably drifts from reality in four ways:
- Status drift: The project is completed, but the intranet page still says 'in progress'.
- World drift: The law, supplier, or purchase price changes, but the internal text does not.
- Duplicate drift: Two copies of a protocol exist; one gets updated, while the other (read by the AI) is forgotten.
- Ownership drift: No one feels responsible for the document anymore, meaning no one verifies the facts.
Compliance Demands: From the AI Act to Base Registries
Simultaneously, laws and regulations increasingly demand that documentation accurately reflects reality. The AI Act (EU 2024/1689) requires that the technical documentation of high-risk AI systems is kept demonstrably up-to-date (Article 11). Similarly, the ISO/IEC 27001:2022 standard sets clear requirements for the continuous control of documented information (Clause 7.5).
We see this pattern across various sectors. In the legal profession, model deeds and office manuals require a date of last review—an AI presenting an outdated clause will do so with utmost confidence. In construction, handover dossiers (as-built) must reflect the physical building exactly. In healthcare, norms like NEN 7510 require strict review cycles on medical protocols. We can also look at the public sector for inspiration: the Dutch government uses a system of base registries with a legal obligation to report back. Anyone who doubts the accuracy of a registered data point must report it to the source for correction.
Operationalizing Information Alignment
For processes managed by AI agents, organizations should adopt the following six principles for continuous alignment:
- Expiration dates for facts: Every recorded fact about the outside world (like current legislation or vendor prices) receives an expiration date and a verification method.
- Verify before use: Once a fact's expiration date passes, it must be verified at the source before being used again.
- A single fact registry: External claims originate from a single registry containing the source and expiration date, backed by a periodic drift check (e.g., weekly).
- 'Done' means verified: An update is only truly finished when the documentation is updated and the change is verified post-delivery.
- Strict ownership: Every status carries an owner and an expiration date, after which it automatically returns to the agenda for verification.
- Docs-as-code: Treat documentation like software (following Write the Docs principles). Documentation lives next to the system it describes and is updated in the same change cycle.
Crucially, this requires a feedback loop. Every user, human or AI, must have a clear escalation route to report an error to the owner. Outdated knowledge is more dangerous than no knowledge, because no one checks it anymore.
Starting in Five Steps
- Inventory: Identify exactly which documentation the AI assistant will read.
- Assign ownership: Add an owner and a 'last verified on' date to every critical document.
- Determine shelf life: Give vital facts about the outside world a fixed expiration date.
- Plan a drift cadence: Choose a fixed review cycle (e.g., weekly samples, quarterly deep dives).
- Enable reporting: Set up a straightforward feedback route for users and AI agents to report discrepancies.
Sources
- NORA - Reporting back (Terugmelden): the Dutch government standard for reporting a suspected error back to the data owner.
- Digitale Overheid - System of base registries: how the Dutch system designates a single source per data item and anchors the obligation to report back.
- Xie et al. (2023) - Adaptive Chameleon or Stubborn Sloth: research into knowledge conflicts, on how language models handle context that contradicts their own parametric knowledge.
- Write the Docs - Docs as Code: managing documentation with the same version control, reviews and release cycle as source code.
- NORA - Basic principles: the foundational principles for information management and data quality in Dutch government architecture.
- EUR-Lex - AI Act (EU) 2024/1689: the consolidated text of the European AI Act, including Article 11 on keeping technical documentation up to date.
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