Your Policies Are Out of Date: How I Built a Sanity AI Agent to Catch Fact Drift

What happened

The article outlines the development of Fact Ledger, an AI-assisted system that detects and resolves fact drift in content management systems. It describes how the system uses a deterministic scanner with five rules to identify inconsistencies, an AI agent to draft patches, and a human review process to ensure accurate updates. The system ensures atomic updates across all affected pages and includes a live dashboard for monitoring and managing the process.

Why it matters

Fact Ledger is significant for AI developers and content management system engineers because it provides a robust solution to a common problem in content management: fact drift. By ensuring that all content referencing a fact is updated automatically and consistently, it reduces the risk of errors and inconsistencies, improving the reliability and accuracy of information across an organization.

• Fact Ledger addresses the problem of fact drift by ensuring all content referencing a fact is updated automatically.
• The system uses a deterministic scanner with five rules to detect inconsistencies efficiently.
• An AI agent drafts patches for content updates, which are then reviewed and approved by a human.
• The system ensures atomic updates across all affected pages, preventing partial or inconsistent changes.
• The solution includes a live dashboard for monitoring and managing the remediation process.

The bigger picture

The practical impact of Fact Ledger is significant for organizations managing large volumes of content. It ensures that all content referencing a fact is updated automatically, reducing the risk of errors and inconsistencies. This leads to improved reliability, accuracy, and compliance, particularly in industries where policy changes can have legal or financial implications.

Fact Ledger is a robust solution to the problem of fact drift in content management systems. By combining deterministic scanning, AI-assisted patch generation, and human review, it ensures that all content referencing a fact is updated consistently and accurately, reducing the risk of errors and inconsistencies.

Technical details

• Fact Ledger uses Sanity Content Lake as its data model.
• The system employs a deterministic scanner with five rules (R1–R5) to detect inconsistencies.
• The AI agent generates patch mutations for content updates.
• The system uses a custom document action in Sanity Studio for human approval.
• The solution includes a live dashboard with interactive visualizations and real-time updates.

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