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Agent-Readiness Assessment

A fixed-scope engagement that turns "are we agent-ready?" into a measured baseline and a sequenced plan — score your API surface against the open Agent Readiness framework, map it onto the standards onramp, and walk away with a prioritized roadmap and the descriptor that proves it.

A human developer can paper over a lot of API friction — an ambiguous error, an undocumented idempotency convention, a prose-only auth description, an HTML-only changelog. An autonomous agent cannot. Every implicit convention a human silently absorbs is a place an agent gets stuck, retries blindly, double-charges a card, or hallucinates a payload. Agent readiness is the discipline of removing those implicit conventions and replacing them with machine-readable signals.

Most teams know that matters, but they have no canonical definition of “agent-ready,” no sequenced path to get there, and no way to check hundreds of repositories against it. So “are we agent-ready?” stays an anxious, unbounded question. This engagement makes it a measured baseline and a plan — a fixed-scope assessment in four moves.

The engagement, in four moves

  1. Scan. I score your public API surface against my open Agent Readiness framework — a twelve-dimension model covering spec presence, auth-model clarity, idempotency, error semantics, rate-limit headers, dry-run, examples, MCP, and event contracts, plus the forward-looking signals of /.well-known/api-catalog, machine-readable consent, and identified agent traffic. Each dimension is scored 0–3 from a real evidence URL and rolled up into an overall posture.
  2. Map. I place the result on the Agent-Era API Standards Onramp — the sequenced ladder from OpenAPI through JSON Schema, Arazzo, discovery, MCP, agent skills, async, and agent-payment standards. This produces your canonical definition of agent-ready: where you are, what the next rung is, and what it unlocks.
  3. Gap. A prioritized findings list — minimum operational docs, an AGENTS.md with explicit boundaries, AI-scrapable delivery, task-oriented MCP tools — highest-leverage fixes first, each backed by a specific evidence URL.
  4. Onramp. A sequenced roadmap plus an API Onboarding Descriptor for your flagship APIs, so you leave with a repeatable pattern rather than a one-off cleanup.

Because the framework is public and I use it the same way across every provider on APIs.io, the number is neutral — not a badge I sell, but an honest outside read your teams and stakeholders can trust. It works as a one-time baseline, and it works even better as a before-and-after: score today, ship your improvements, and I re-score against the same rubric so you can prove exactly how far you moved.

What you walk away with

  • A scored scorecard across all twelve dimensions, each backed by a specific evidence URL
  • Your position on the standards onramp — the canonical, sequenced definition of agent-ready for your operation
  • A prioritized remediation roadmap — what to fix, in what order, to move each dimension up
  • An API Onboarding Descriptor for your flagship APIs, and a benchmark against reference and peer providers
  • A re-assessment pass that quantifies your improvement as a defensible delta

Related reading

Start with the research

Everything I know about what agent-ready actually means and how it is measured is already written down, priced, and yours to read tonight — no call, no scoping, no proposal. Start there.

Free first: your Kin Score and Agent Readiness are already published on APIs.io. Look yourself up before you buy anything — the score costs nothing and it is the same rubric every report on this page is built from.

Browse all research →

If you would rather have this done with you than do it yourself, I take a small number of engagements a year — [email protected].