Agent readiness KPIs: what to report each month
Agent readiness work needs a monthly number, or it quietly slides down the priority list. This guide sets out a one-page monthly report built on six measures: where each one comes from, what good looks like, and how to read them together. There are no reliable industry benchmarks for most of these yet, so the comparison that matters is your own trend.
Four rules for the report
- Trend over level. A score of 68 means little on its own. A score that went from 52 to 68 in a quarter, with the reasons, means a lot.
- Keep two questions apart. "Can AI agents use our site?" (readiness, errors, test results) and "Is it turning into business?" (referrals and orders). The first leads; the second follows.
- One owner per measure. Each number needs someone who explains it and acts on it. See who owns agent readiness.
- Note what changed. Releases, bot protection changes, new apps and campaigns. Most movements in these numbers trace back to one of them.
The six measures
| Measure | Question it answers | Where it comes from |
|---|---|---|
| AgentScore | Can agents get in, read and find their way around? | An AgentScore scan |
| Agent visits by type | Who's visiting, and what are they reading? | Server or CDN logs |
| Verification share | How much of that traffic is really who it says it is? | Logs checked against operators' published proof |
| Blocks and errors | Are we turning agents away? | Status codes in your logs |
| Ghost Agent pass rates | Can an agent actually finish the job? | Ghost Agent tests |
| AI referral visits and orders | Is it bringing in customers and revenue? | Your analytics and your store's orders |
In Ghost Agent Labs, the site's Overview page brings several of these together. The sections below name the page that holds each one.
1. AgentScore
Report: the overall score and its band, the scores for Access, Readability and Navigability, and how many findings you fixed and how many are new. The free scan doesn't test Task completion yet, so the score covers the other three categories.
Where: the Readiness page. Every scan is saved, so you can track the score over time, and "All findings" exports to CSV for your developers.
Good looks like: a steady or rising score, no critical findings open for more than a month, and every drop explained. Scan on the same day each month and after major releases, so the numbers compare like with like. The bands are 80 and up Good, 60 to 79 Fair, 40 to 59 Weak and below 40 Poor. See reading your AgentScore report.
2. Agent visits by type
Report: requests by visitor type (search crawlers, AI training crawlers, AI assistants and autonomous agents) compared with last month, the pages agents read most, and any new agents.
Where: the Agent traffic page ("Requests by visitor type", "Agent sessions" and "Pages agents request most"), and "What changed" on the Overview, which lists first-time agents and agents whose traffic rose or fell by half or more.
Good looks like: AI assistant and AI search traffic holding steady or growing, and agents reaching product, pricing and policy pages rather than stalling on the home page or old URLs. Don't celebrate total volume. A busy training crawler isn't a customer. Assistant requests are the closest thing to a customer visit from AI, because each one is usually a person's question. See how to measure AI agent traffic.
3. Verification share
Report: of the requests claiming to be a known agent, the share that were verified, couldn't be verified, and were spoofed.
Where: the Verification & spoofing page.
Good looks like: spoofed traffic close to zero, or falling after you block it, and a verified share that rises as more operators publish ways to check their agents. This measure also keeps the rest of the report honest: never report "ChatGPT visited 40,000 times" from user agent names alone, because impostors use those names too. See how to tell if an AI crawler is real.
4. Blocks and errors
Report: the share of agent requests that were blocked (401, 403 and 429 responses), the 404 and server error rates, and the agents and pages most affected.
Where: "Errors: agents vs people" on the Overview, "What agents got back" on the Agent traffic page, and the status codes on each agent's page.
Good looks like: blocked responses to verified AI assistants near zero, agents getting errors about as often as people do rather than much more, and agent 404s trending down as you redirect old URLs. A sudden rise almost always follows a change to bot protection or rate limits. See finding the errors AI agents hit.
5. Ghost Agent pass rates
Report: for each journey you test, such as checkout, product search or sign-up, whether it's passing, flaky or failing, its pass rate, and the step where failures cluster.
Where: the Ghost Agents page, with a replay of every run.
Good looks like: your money journeys passing, flaky results treated as findings rather than noise, and failures fixed within a sprint. A pass rate below 100% means some customers' assistants would fail too. This is the measure closest to revenue, because it tests whether an agent can do the job, not just whether the page looks right. See how to test your key journeys with AI agents.
6. AI referral visits and orders
Report: visitors who arrived from AI assistants' answers, by assistant, their conversion rate, and orders and revenue from AI.
Where: your analytics, set up as in tracking visits and sales from AI assistants. In Ghost Agent Labs, "Visitors from AI assistants" is on the Overview. Send your store's orders to the site's orders URL and Mission Control shows "Agent-driven revenue": orders referred by an AI answer or placed by an agent, and their share of revenue.
Good looks like: growth from whatever base you start at, and conversion from AI referrals that holds up against your other referral sources. Present it as a floor, not a total: app traffic and copied links arrive as "direct", and many AI answers influence a purchase without a click.
Reading the measures together
Single numbers mislead. These combinations are where the report earns its keep:
- Score steady, blocks up. Something changed in bot protection or rate limits. Check the agent's page for which pages are affected and when it started.
- Visits up, referrals flat. Assistants are reading you but not sending people. Look at product data, prices and policy pages: are they giving assistants what they need to recommend you?
- Score unchanged, pass rate down. A journey broke in a way the scan can't see: a new pop-up, a redesigned size picker, a checkout app. The run replay shows where.
- Referrals up, checkout test failing. AI is sending you customers and losing some of them at the last step. This is the most urgent combination on the page.
- Spoofed share up. Scrapers are using agent names. It doesn't hurt readiness, but it inflates traffic numbers and costs bandwidth.
A one-page template
- Headline. One sentence: better, worse or the same, and why.
- Scorecard. The six measures, this month against last month, with an arrow and a short note each.
- What changed. Site releases, bot protection changes and new agents, linked to the numbers they moved.
- Fixed this month. The findings closed and the journeys repaired.
- Next month. The top three fixes, each with an owner.
- Decisions needed. For example, whether to allow a new AI agent, or budget for platform work.
What to leave out. Total bot requests without a breakdown, scores without a trend, and industry comparisons nobody can source. They invite the wrong questions. If you're presenting upward, see how to explain agent readiness to your board, and for what to fix first, the 90-day agent readiness plan.