Dries Buytaert

Imagine waking up to discover that overnight, AI agents rewrote 500 product descriptions, reorganized 300 pages for SEO, and updated 9,000 alt-text descriptions on your website.

As you review the changes over coffee, you find three product descriptions featuring nonexistent features. If published, customers will order based on false expectations. Then you notice another problem: AI rewrote hundreds of alt-text descriptions, erasing the ones your team crafted for accessibility.

AI-driven content management isn't a distant scenario. Soon, Content Management Systems (CMS) may deploy hundreds of AI agents making bulk edits across thousands of pages.

The challenge? Traditional CMS workflows weren't designed for AI-powered editing at scale. What features should an AI-first CMS include? What safeguards would prevent errors? What workflows would balance efficiency with quality control? I'm outlining some rough ideas to start a conversation and inspire Drupal contributors to help build this future.

1. Smart review queues: scaling human oversight

AI-generated content needs different quality checks than human work. Current editorial workflows aren't optimized to handle its output volume.

I envision "AI review queues" with specialized tools like:

• Spot-checking: Instead of manually reviewing everything, editors can sample AI content strategically. They focus on key areas, like top-selling products or pages flagged by anomaly detection. Reviewing just 5% of the changes could provide confidence; good samples suggest the broader set works well. If issues are found, it signals the need for deeper review.
• Rolled-up approvals: Instead of approving AI edits one by one, CMS platforms could summarize large-scale AI changes into a single reviewable batch.

2. Git-like content versioning: selective control over AI changes

Say an AI translated your site into Spanish with mixed results. Meanwhile, editors updated the English content. Without sophisticated versioning, you face a tough choice: keep poor translations or roll everything back, losing days of human work.

CMS platforms need Git-like branch-based versioning for content. AI contributions should exist in separate branches that teams can merge, modify, or reject independently.

3. Configuration versioning: keeping AI from breaking your CMS

AI isn't just generating content. It is also modifying site configurations, permissions, content models and more. Many CMS platforms don't handle "configuration versioning" well. Changes to settings and site structures are often harder to track and undo.

CMS platforms also need Git-like versioning for configuration changes, allowing humans to track, review, and roll back AI-driven modifications just as easily as content edits. This ensures AI can assist with complex site management tasks without introducing silent, irreversible changes.

4. Enhanced audit trails: understanding AI decisions

Standard CMS audit logs track who made changes and when, but AI operations demand deeper insights. When multiple AI agents modify your site, we need to know which agent made each change, why it acted, and what data influenced its decision. Without these explanations, tracking down and fixing AI errors becomes nearly impossible.

AI audit trails should record confidence scores showing how certain an agent was about its changes (60% vs 95% certainty makes a difference). They need to document reasoning paths explaining how each agent reached its conclusion, track which model versions and parameters were used, and preserve the prompt contexts that guided the AI's decisions. This comprehensive tracking creates accountability in multi-agent environments where dozens of specialized AIs might collaborate on content.

This transparency also supports compliance requirements, ensuring organizations can demonstrate responsible AI oversight.

5. AI guardrails: enforcing governance and quality control

AI needs a governance layer to ensure reliability and compliance. Imagine a healthcare system where AI-generated medical claims must reference approved clinical studies, or a financial institution where AI cannot make investment recommendations without regulatory review.

Without these guardrails, AI could generate misleading or non-compliant content, leading to legal risks, financial penalties, or loss of trust.

Instead of just blocking AI from certain tasks, AI-generated content should be checked for missing citations, regulatory violations, and factual inconsistencies before publication.

Implementing these safeguards likely requires a "rules engine" that intercepts and reviews AI outputs. This could involve pattern matching to detect incorrect content, as well as fact verification against approved databases and trusted sources. For example, a healthcare CMS could automatically verify AI-generated medical claims against clinical research databases. A financial platform might flag investment advice containing unapproved claims for compliance review.

Strategic priorities for modern CMS platforms

I can't predict exactly how these ideas will take shape, but I believe their core principles address real needs in AI-integrated content management. As AI takes on a bigger role in how we manage content, building the right foundation now will pay off regardless of specific implementations. Two key investment areas stand out:

1. Improved version control – AI and human editors will increasingly work in parallel, requiring more sophisticated versioning for both content and configuration. Traditional CMS platforms must evolve to support Git-like branching, precise rollback controls, and configuration tracking, ensuring both content stability and site integrity.
2. AI oversight infrastructure – As AI generates and modifies content at scale, CMS platforms will need structured oversight systems. This includes specialized review queues, audit logs, and governance frameworks.