Content Versioning

Implement content hashing that generates a unique version identifier for every published state of every page - include this hash in your structured data (dateModified + version) so agents can detect changes without re-parsing the full content.

Add Last-Modified and ETag HTTP headers to all content responses - agents use conditional requests (If-Modified-Since, If-None-Match) to efficiently check for updates without downloading unchanged content.

Publish a change feed (RSS, Atom, or JSON Feed) that lists recently modified content with summaries of what changed - agents monitoring your content for updates can poll a single feed rather than crawling your entire site.

Implement content versioning URLs (/article/v2, /article?version=2024-03-07) that allow agents to reference specific historical versions - this prevents citation invalidation when you update content that agents have already cited.

Add a version history section to long-form content that documents significant changes with dates and descriptions - agents assessing content reliability use update history as a signal of maintenance quality.

Implement WebSub (formerly PubSubHubbub) for real-time content update notifications - agents that subscribe to your WebSub hub receive push notifications within seconds of content changes, eliminating polling latency.

Add a sitemap lastmod field that accurately reflects the last significant content change, not the last technical deployment - agents use sitemap timestamps to prioritise re-crawling, and inflated timestamps waste their crawl budget.

Publish a machine-readable changelog at a well-known URL (/.well-known/changelog.json) that lists content changes with semantic versioning - major changes (new conclusions), minor changes (added evidence), and patches (typo fixes).

Implement HTTP 301 redirects with a Sunset header for deprecated content - agents following links to removed content need to know both where to go instead and when the original content was deprecated.

Consider implementing a content subscription API that allows agents to register for notifications about specific topics or pages - this inverts the crawling model and gives you visibility into which agents consume which content.

Maintain URL stability as a core principle - every URL that has been published, cited, or indexed should continue to resolve, either to the original content or to a redirect that explains the change.

Version your structured data schemas and maintain backward compatibility for at least two major versions - agents that parse your JSON-LD will break if you rename properties or change types without a transition period.

Implement content negotiation that serves different formats based on Accept headers - agents requesting application/json should receive structured data, while agents requesting text/html should receive the rendered page.

Add deprecation notices to content that will be removed or significantly changed - include a machine-readable sunset date and a link to the replacement content so agents can update their references proactively.

Test backward compatibility by maintaining a suite of agent-perspective integration tests that verify your content structure, URL patterns, and structured data output remain stable across deployments.

Classify content changes using a semantic taxonomy: correction (factual error fixed), update (new information added), expansion (more detail on existing topic), restructure (same content, different organisation), and deprecation (content no longer current).

Include diff summaries in your change feed that describe what changed in human-readable and machine-parseable format - agents that have cited specific claims need to know whether those claims were affected by the update.

Implement editorial notes (similar to Wikipedia's edit summaries) that explain the rationale for significant changes - agents assessing content reliability use editorial transparency as a trust signal.

Add a correction policy page that describes how you handle errors, updates, and retractions - agents and the humans who configure them use correction policies to assess the trustworthiness of a content source.

Publish a content stability score for each page based on its change frequency and the nature of changes - pages that change frequently with corrections score lower than pages that change infrequently with expansions, and agents can use this signal to calibrate citation confidence.