Artificial intelligence is now entering a phase of development at Drupal. Far from simple technical demonstrations, the open source community is building a true ecosystem of interactive intelligent agents designed to enhance the experience of users, publishers, and developers.
These agents are divided into several functional areas: content analysis, editorial optimization (SEO, accessibility, inclusivity), assisted page and media creation, workflow automation, user experience personalization, sales support, and governance.
A pool of agents to structure editorial intelligence
The first series of actions focuses on content and data processing. Agents such as Fact Finder, responsible for verifying the accuracy and consistency of information, and Search Optimizer, dedicated to metadata quality, semantic indexing, and search optimization, work extensively on the reliability, relevance, and structured organization of information.
This moves us away from the simple plain text model: each piece of content becomes an enriched and contextualized resource, supported by intelligent metadata. The internal search engine, for example, can then combine semantic search, user history, and editorial weighting, while retaining Drupal's characteristic modularity.
In the same spirit, tools such as Talk to your Data Agent offer end users a conversational interface that allows them to search their own content using natural language. This type of interface, highly anticipated by business teams, could become a central entry point for document portals or complex extranets.
Continuous optimization: from SEO to inclusion
On the optimization side, the Drupal AI approach offers a range of agents able to act at different stages of the content lifecycle. Whether checking a page's RGAA compliance, suggesting a more accessible version, or automatically proposing a JSON-LD tag better suited to the search intent, these agents become integrated editorial assistants.
One particularly interesting aspect concerns content bias and brand alignment. Thanks to a layer of textual analysis, some agents identify non-inclusive expressions, deviant tone, or poorly tuned vocabulary. A company can thus import its content charter, editorial guide, or internal validation rules and convert them into AI rules that can be automatically exploited.
Content creation and variations: industrialize without blind automation
It is no doubt in content creation that AI has attracted the most attention, and Drupal is no exception. But here again, the approach is more subtle than one might think.
The goal is not to replace copywriters, but to help them adapt their content more quickly to different formats, media, or audiences. An article can be automatically summarized, transformed into a list of frequently asked questions, converted into an SEO meta description, or adapted for a younger audience. Here are some concrete use cases: newsletters, multilingual product sheets, awareness-raising documents, etc.
On the media side, AI is capable of generating image variants, automating asset tagging, and proposing dynamic layouts based on imported content. These functions can be integrated into classic Drupal workflows via modules or third-party integrations (Figma, DAM, design systems, etc.).
Automate, but always manage
One of the major challenges remains control. Drupal therefore offers governance-focused agents capable of tracking AI actions, recording generated versions, triggering an approval process before publication, or limiting certain features according to user roles. These safeguards are essential to enable sensible deployment in demanding contexts: public sector, media, healthcare, associations, etc.
In a multisite or multilingual environment, the benefits are obvious. We can imagine coordinated campaigns whose content is generated semi-automatically, translated into a dozen languages, enriched according to user profiles, all while maintaining an editorial logic specific to each organization.
What we actually test at WebstanZ
For several months now, we have been experimenting with different scenarios in our development environments. These include, for example, a chatbot connected to Drupal data to answer specific business questions, a system for generating contextual meta descriptions, and an AI-enhanced RGAA audit module. We are also testing the generation of alternative images, machine translation with human validation, and automated suggestions for landing page structures based on marketing objectives.
None of these tools are fully stabilized yet—and that's okay. The goal is to test them, understand them, and evaluate them. And above all, to work with you to see what makes sense in your specific contexts.
What now?
The Drupal ecosystem is laying the foundations for a modular, ethical, extensible, and governable AI framework. A framework that does not aim to replace humans, but to augment them—at every stage of your content lifecycle.
At WebstanZ, we don't make miracle promises. But we are convinced that a reasoned and pragmatic approach to AI can save valuable time, open up new possibilities, and enhance the quality of your content.
And if you're wondering — no, it's neither too early nor too late to talk about it. Quite the opposite, in fact.
Want to talk about AI? Get in touch!
KEY POINTS FROM THIS ARTICLE
- Drupal is structuring its AI ecosystem: the open source community is developing a modular framework of intelligent, interoperable, and governable agents—far beyond simple technical demonstrations.
- Agents at the service of content: consistency and accuracy verification (Fact Finder), SEO and semantic optimization (Search Optimizer), conversational interfaces for querying your own data (Talk to your Data Agent).
- Continuous and responsible optimization: accessibility, inclusivity, editorial alignment, tone consistency — each agent becomes a true assistant for content teams.
- Assisted creation and variation: generation of variants, summaries, translations, or dynamic layouts, while keeping human supervision at the heart of the process.
- Integrated governance and traceability: every AI action is recorded, controlled, and validated according to specific roles—essential in sensitive environments (public sector, healthcare, media, etc.).
- WebstanZ at the forefront: we are already experimenting with these tools (chatbots, augmented RGAA auditing, metadata generation, assisted translation, etc.) to understand what makes sense in your real-world contexts.