How an AI agent can create a page-level content maintenance plan
This workflow gives an AI agent a defined job, a bounded set of records, and a result a person can review. The agent reads the relevant WordPress.com context, applies the rules in the prompt, and keeps the source behind every recommendation. It returns a proposed handoff rather than taking consequential actions on its own.
Can an AI agent create a page-level content maintenance plan?
Yes. Start with the scope, date range, decision rules, and fields that identify the right records. The agent can collect the evidence, compare states or sources, mark conflicts and missing data, and organize the result around the outcome above. A reviewer then checks the matches and judgment calls before approving messages, record updates, bookings, purchases, publishing, or other write actions. The guide below shows the records, boundaries, prompt, and handoff needed for this specific workflow.
Inventory the section before judging it
Choose a category, URL prefix, post type, author queue, locale, or explicit list. Ask the agent to inventory title, URL, status, author, modified date, taxonomy, and other fields needed for the review. Confirm that inventory before applying content rules so drafts, private pages, and unrelated sections do not enter the audit.
Provide current terminology, expiration rules, required metadata, ownership mapping, duplicate criteria, and any pages that control a shared claim. The agent should quote or identify the exact field and content behind each finding. Age is a review signal, not proof that a page is wrong.
Questions this workflow answers
Which pages should our content team update first, and which can safely wait?
An agent can answer that only after separating publication risk from simple age. A five-year-old help page may still be correct and heavily used, while a product page edited last month may contain an expired offer or an ownerless legal claim. The audit begins with a bounded inventory and applies rules the editorial team recognizes: time-sensitive statements, required metadata, broken ownership, obsolete terminology, likely duplication, missing locale updates, or links to a superseded source. Every flag retains the URL, status, relevant passage or field, and the rule it triggered.
Priority comes from consequence and confidence. A verified wrong price on a public conversion page belongs above an old author bio that may still be acceptable. A likely duplicate should not be merged merely because two titles look alike; the agent compares audience, search intent, canonical role, and meaningful content differences. Items with unclear evidence go to an editor’s question queue rather than being padded with an invented recommendation. Grouping related findings can also reveal a systemic fix, such as a missing form field or shared block that needs one correction instead of dozens of page edits.
The final maintenance plan gives each page a proposed disposition—correct, review, consolidate, redirect, archive, or leave alone—with rationale, owner, and publication risk. It can also identify a small representative batch for the first review. Editors see the high-impact changes early while retaining control over nuance and live publishing. On later runs, the same rules and section boundary make it possible to compare progress without turning every old timestamp into a new backlog item.
Example starter prompt
Audit WordPress.com [site and section] for [date range or URL boundary]. Include only [statuses, locales, or post types].
Check for [expired claims], missing required metadata, broken ownership, inconsistent terminology, and likely duplication under these rules: [rules]. For each finding, include URL, status, author, field or passage, evidence, proposed correction, and confidence.
Do not edit, delete, or publish content. Return an inventory, prioritized maintenance queue, and questions that need an editor.Review claims before bulk changes
Check high-impact and low-confidence findings first. Confirm that duplicate candidates serve the same audience and intent rather than sharing a title. Check links and shared claims against the named source instead of substituting general knowledge.
The final queue should include page, issue, evidence, owner, correction, and publication risk. Editors can approve changes in batches after inspecting representative pages.