How an AI agent can produce a page-by-page content audit with evidence, owners, and proposed fixes
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 Adobe Experience Manager 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 produce a page-by-page content audit with evidence, owners, and proposed fixes?
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.
What this AI agent checks
An AEM content audit agent reviews a specific body of pages and Content Fragments for problems that are easy to miss during day-to-day publishing. That can include expired dates, inconsistent product names, missing metadata, unclear ownership, duplicate content, and references to assets or fragments that no longer fit the page.
Define the audit around a real boundary: the /products/analytics section, the French locale for a campaign, or every page changed since the last release. Include the rules that matter to your team, such as required fields, review dates, approved terminology, and who owns each content type. Without those constraints, the agent will produce a long list with no useful priority.
Example starter prompt
Review the AEM pages and Content Fragments under [path or campaign] that changed between [start date] and [end date].
Check for expired or contradictory copy, missing required metadata, broken ownership, and content that is inconsistent across locales. For every finding, include the AEM path, the field or component involved, the evidence you found, and the smallest reasonable correction.
Group the result into: blocks publication, should fix this cycle, and needs an owner decision. Do not edit or publish content. Put anything you cannot verify in a separate questions section.How the agent runs the audit in two passes
First, have the agent inventory the content in scope. The inventory should show page or fragment path, locale, last modified date when available, current owner, and publication status. Review that list before asking for analysis. This catches scope mistakes early, especially on sites with inherited content or several language trees.
In the second pass, ask the agent to apply your editorial and release rules. It should quote or describe the exact field that triggered each finding. A claim such as “copy may be stale” is not enough; the handoff should say which date, offer, product name, or source conflicts with the page.
Questions this workflow answers
Can an AI agent produce a page-by-page content audit with evidence, owners, and proposed fixes?
An agent can inspect a bounded section of an AEM site and build the editorial queue that a broad site search cannot. Start by naming the page roots, locales, templates, and Content Fragment models in scope. Then supply the rules that define a problem for this site: offer end dates must be in the future, product names must match the current terminology list, every page needs an owner, and localized fields cannot fall back to retired English copy. The agent reads the actual component and fragment values, records where each value appears, and compares them with those rules.
The result should distinguish four different jobs. An expired promotion needs a content correction. A fragment referenced by twenty pages needs impact review before anyone edits it. An ownerless page needs routing. A page the agent cannot access needs a permissions check, not a clean bill of health. This separation keeps a 300-row audit from becoming one undifferentiated backlog.
AEM gives the agent page paths, workflow state, component fields, fragment references, locale relationships, and modification context. Ask for those identifiers beside every finding. An editor can open the exact field, verify the conflict, and decide whether the proposed fix is safe. The agent should also group repeated findings by shared cause. Ten pages with the same incorrect disclosure may trace back to one Content Fragment; fixing each page independently would waste time and could leave the source error in place.
Run the workflow first on a section with known issues and compare its findings with an editor’s review. That trial reveals which date formats, inherited values, and exceptions the rules must handle. Once the team trusts the classifications, the same audit can run weekly on changed content while publication remains under normal editorial approval.
Expected handoff
The final audit should be usable as an assignment queue. Each row needs the AEM path, issue type, evidence, severity, proposed correction, owner, and open question. Keep publication and bulk edits behind a separate approval. The agent’s job is to shorten the review, not to make editorial judgment invisible.