SERP→Agent Feature Specs: Turn Answer Boxes into Contractor‑Ready, Agent‑Citable Features

Start with query enumeration focused on high‑value intent buckets (task completion, comparison, how‑to, tool request). Pull the SERP types that appear for those queries: featured snippets, People Also Ask (PAA), AI Overviews, and related entity panels. Each SERP feature encodes different user expectations — a paragraph snippet usually signals a single factual answer, while PAA indicates a follow‑on flow of micro‑tasks or clarifying questions.

Capture the concrete signals from each result: the question phrasing, the exact extractable answer (40–120 words), whether the page supplies step lists, tables, or code blocks, and the landing page’s structural signals (H2s, FAQ schema, HowTo schema). These signals drive the job‑to‑be‑done statement you will draft into the feature spec.

Translate raw SERP evidence into a prioritized feature backlog using three lenses: frequency (how often the question/format appears), commercial intent (transactional vs. informational), and agentability (can an AI agent automate or complete this task?). Weight each dimension and produce a short list of candidate features with clear success metrics (task completion rate, agent success rate, search CTR uplift).

For agentability, prefer tasks that are repeatable, have clearly structured inputs/outputs, and map to small, testable actions. Use schema.org Action and SoftwareApplication patterns to assess whether a feature can be represented as an agent‑callable action or as a discoverable capability.

The spec must be single‑page, actionable, and structured so a contractor or engineer can implement it without ambiguity. Use the following sections: Job‑to‑be‑done (one sentence), Acceptance tests (machine‑executable examples), JSON‑LD contract (schema.org action, inputs, expected outputs), Rollout plan (canary + telemetry), and Search/Agent discoverability checklist.

Example acceptance tests are imperative: provide 3–5 example user inputs mapped to exact outputs (text, JSON, or UI). The JSON‑LD should include @type: Action or SoftwareApplication with explicit input and output properties (use featureList, entryPoint, or the Action -output annotations where appropriate). That JSON‑LD becomes the canonical agent contract.

Instrument telemetry that measures both human search behavior and agent interactions: SERP CTR and impressions for landing pages, query‑to‑task conversion, agent invocation rate, agent success/failure, and downstream retention events. Capture sample inputs that produced success/failure and store them for acceptance/regression test suites.

On the SEO side, ensure the implementation surfaces the same extractable answer formats that triggered the SERP feature: clear H2 questions, concise first‑paragraph answers, structured HowTo or FAQ schema where appropriate, and an embedded JSON‑LD Action contract so agents can discover the capability. Measure changes in featured snippet ownership, PAA inclusion, and AI Overview citations.

Agents discover capabilities through a mix of structured data and strong extractable signals. Publish JSON‑LD describing an Action (or SoftwareApplication featureList + EntryPoint) near the actionable content. Use concise, canonical answers in HTML so search extractors can cite the page. Consider cross‑linking to an agent manifest on your site or to a well‑known discovery endpoint that lists agent‑callable actions.

Finally, treat agent discoverability and SERP visibility as a single product requirement — the same acceptance tests that verify agent contract correctness should also verify that the same queries produce the intended snippet or structured answer on the page. That alignment closes the loop between demand (search), automation (agents), and product delivery.