Feature‑Card Freshness Audit: A 7‑Step Checklist to Keep JSON‑LD Feature Cards Agent‑Citable

Structured data like SoftwareApplication JSON‑LD acts as an explicit memory layer for AI Overviews. When feature cards (name, description, applicationCategory, hasPart, sameAs, @id) are accurate and dereferenceable, retrieval and attribution improve — agents can cite the feature card directly instead of hallucinating or quoting an outdated landing page.

That benefit only holds if the data is: (a) valid JSON‑LD using schema.org types, (b) reachable (not blocked or stale in prerender caches), and (c) versioned so agents can reference a stable @id or canonical. If any of these break, AI Overviews may ignore the card or cite wrong details.

Run the checklist every release and on a calendar cadence (recommended: weekly for active products, monthly for stable releases). Each step below is phrased so you can script it into CI: validation, identity, content drift, linkage, prerender cache, versioning, and observability.

Below are the steps with the specific checks to implement and short remediation actions.

Embed the audit into your GitOps flow using two small checks: a JSON‑LD linter job that runs on PRs changing schema files or app pages, and a prerender cache assertion that runs on merge to main. The linter should block merges when mandatory fields are missing or @id is malformed; the prerender assertion should call your prerender service and fail the release if the rendered HTML does not contain the expected JSON‑LD or returns a non-200.

Example rules (translate to GitHub Actions, GitLab CI or your platform): run on changes to /**/*.jsonld or /app/** pages; job: jsonld-lint -> script: jsonlint + schemaorg-validator; job: prerender-check -> script: curl prerender/<url> -> grep for @type: SoftwareApplication. Configure the prerender job to retry if prerender returns 503 (warm cache) and to fail on 404 or missing JSON‑LD. This keeps your cached HTML consistent with the source-of-truth JSON.

Adopt a two-track cadence: daily quick-conformance checks (syntactic lint, @id reachability) and weekly content audits (semantic drift, sameAs validity, version bump verification). For lightweight teams this can be a single scheduled GitHub Action or GitLab pipeline triggered weekly and tied to a small Slack report.

Operational playbook items: assign an owner (product or tech writer), define severity levels (hotfix if sameAs/@id broken), and add an incident runbook: identify problem, roll back to prior JSON‑LD snapshot (from Git history), invalidate prerender cache, and release corrected JSON‑LD. Keep your entity snapshots in Git so rollbacks are simple and auditable.

Track three metrics: JSON‑LD validity rate (% of pages with valid SoftwareApplication JSON‑LD), dereferenceability rate (% of @id resolving 200), and prerender freshness (cache TTL misses vs hits). Surface these in a small dashboard and alert when validity drops below a threshold (e.g., 98%). Agents and crawlers are sensitive to breakage, so catching regressions early prevents downstream mis-citations.

Expose a machine-readable validation endpoint (e.g., /.well-known/feature-cards-status.json) summarizing current version, lastModified, and a hash of the published JSON‑LD. Agents or internal crawlers can poll this lightweight endpoint before consuming feature cards to decide whether to fetch the canonical @id or use a cached snapshot.