Day 137: Test Whether an AI Agent Can Finish the Buyer’s Next Step

A procurement lead gives an AI agent a bounded task: compare two suppliers against an approved requirement, confirm whether one serves the UK, and prepare a consultation request for review.

The agent finds the supplier. It reads the offer accurately. It locates the contact page and fills in the visible fields.

Then the journey breaks.

The service selector is a styled element with no clear functional name. A required consent choice appears only after another field changes. The submit control becomes active without exposing why. After activation, the page shows a brief animation but no durable confirmation, reference, or clear statement that the request was received.

The supplier was visible. The content was understandable. The next commercial step was still unusable.

This is a generalised failure-mode scenario, not a reported buyer journey or a claim that AI agents routinely submit B2B enquiries today. It exposes a newer question for CMOs, Marketing Directors, and founders: if a person delegates part of a buying task, can the website represent each action and outcome clearly enough for the agent and the person supervising it?

Being understood is not the same as being operable

Most GEO work concentrates on the answer layer. Can an answer-led system find the company, explain the offer, distinguish its fit, and link to a useful source?

Those remain valid questions. A delegated journey adds another layer. The system may need to interact with the destination rather than only summarise it.

Google’s current Search guidance describes AI agents as systems that can perform tasks on behalf of people, including examples such as booking a reservation or comparing product specifications. It also notes that browser agents may inspect visual renderings, the DOM, and the accessibility tree when gathering the information needed to complete a task.[1]

This does not mean every buyer will send an agent through every website, or that every transaction should be automated. It creates a new website test: can a goal-oriented visitor identify the action, its state, and its result without relying on visual implication?

That is not a citation problem. It is an operability problem.

A generalised teardown: the enquiry nobody can confirm

Return to the fictional supplier journey.

The buyer has not asked an agent to make an autonomous purchase. The approved task is narrower: check fit, prepare an enquiry, and stop for human review before submission.

The site creates five avoidable failures:

Journey stage	What the interface shows	What remains ambiguous
Confirm fit	A polished service page with a broad “global” claim	Whether UK delivery is current, conditional, partner-led, or unavailable
Choose an action	Two identical “Start” controls in different cards	Which offer each control belongs to
Complete the form	Labels disappear when the user begins typing	What each field means after values have been entered
Resolve an error	The submit control stays disabled	Which requirement is missing and how to correct it
Confirm completion	A temporary success animation	Whether the request was received, what was sent, and what happens next

None of these requires speculation about rankings or model behaviour. They are interface facts a team can inspect directly.

They also affect humans. Keyboard users, screen-reader users, people switching tabs, buyers working under time pressure, and anyone recovering from a validation error benefit from the same clarity. Agent-journey testing is useful precisely because it exposes whether the page communicates function, state, and outcome beyond visual implication.

The web.dev guidance on agent-friendly sites describes three representations agents may use: screenshots, raw HTML, and the accessibility tree. It explains that agents may combine structural and visual signals to understand interactive elements.[2] Marketing and web teams should therefore ask whether a CTA's function, target, state, consequence, and result survive each representation.

The commercial unit is a completed, reviewable task

A click is a weak finish line.

For a delegated journey, the useful unit is a task that reaches a clear outcome without erasing buyer control. That might be:

• a comparison assembled with stated conditions;
• a consultation request prepared for human approval;
• an appointment selected but not confirmed until review;
• a no-fit result that directs the user to a safer alternative.

Each task needs an observable completion state. “The page changed” is not enough. The interface should make clear what happened, what did not happen, whether the action can be reversed, and what the user should expect next.

For a consultation request, that may mean a persistent confirmation, a summary of what was sent, an honest response window, and a reference where appropriate. A prepared-but-not-submitted request needs an unmistakable review boundary before data leaves the user’s control.

This is where agent readiness becomes a conversion and service-design issue rather than a technical novelty. The business is defining what successful progression actually means.

Do not remove safeguards in the name of agent readiness

An agent-friendly journey should be clearer, not more permissive.

Do not remove authentication, consent, fraud controls, rate limits, approval steps, accessibility protections, or legal disclosures merely to reduce interaction friction. Do not make a consequential purchase, booking, cancellation, or data submission look like an ordinary navigation click. Do not treat a hidden action as acceptable because an automated visitor might find it faster.

The safer design makes boundaries explicit:

• distinguish information gathering from an action that changes state;
• show the user what data will be sent and to whom;
• require human confirmation where the consequence warrants it;
• expose validation errors in text, not only colour or motion;
• preserve price, scope, date, quantity, and cancellation conditions at review;
• return a durable success or failure state;
• offer a recovery route when the task cannot continue safely.

The goal is reliable delegation under visible controls. A blocked journey can be correct: if eligibility cannot be confirmed publicly, report the limitation rather than inventing it; if the request needs sensitive information, route the user into an authenticated or human-supported channel.

Run one delegated-journey test

Do not begin with a site-wide “agent optimisation” programme.

Choose one buyer task with a clear commercial consequence and a safe test environment. Write the goal in buyer language. Define where human review must occur. Then inspect the journey through the rendered page, DOM, and accessibility tree.

Record six things:

1. Goal: What has the user delegated?
2. Boundary: Which steps may the agent prepare, and which require human confirmation?
3. Controls: Do task-critical elements expose a stable role, name, target, and state?
4. Conditions: Are fit, price, geography, availability, consent, and other material limits present at the point they matter?
5. Recovery: Can the user or agent identify and correct an error without guessing?
6. Outcome: Is success, failure, or safe refusal explicit and durable?

Make the smallest defensible correction: a proper label, semantic button, stable layout, textual validation message, review screen, clearer availability state, or durable confirmation. It is not automatically a redesign or a new AI-specific file.

Keep the platform promise bounded

Google’s official guidance says its generative AI Search features remain rooted in core Search ranking and quality systems. It says llms.txt, new AI text files, special markup, arbitrary content chunking, and special schema are not required for visibility in Google Search.[1]

The same guidance discusses agentic website experiences separately. Improve semantic interaction for a better, more accessible journey—not as a guaranteed ranking or citation switch.

No interface change can guarantee discovery, selection, task completion, leads, or revenue. Agents may use different tools, permissions, representations, and safety policies.

The claim worth making is smaller: a business can inspect whether its own website exposes task-critical controls and outcomes clearly. It can preserve approval and security boundaries. It can remove ambiguity that already harms human users.

Ask whether the next step survives delegation

A visibility report may show that the company is present and accurately described. That is useful. It does not show whether a delegated buyer journey can proceed safely from understanding to action.

Pick one high-value task. Let the agent prepare what it is allowed to prepare. Require the person to approve what they should approve. Inspect every role, name, state, condition, error, and confirmation along the way.

If the task fails, do not default to more content or another prompt set. Fix the interaction that made the outcome ambiguous.

The next generation of website visitors may include systems acting under a person’s instructions. The commercial standard should not be that they can click something.

It should be that the buyer remains in control, the action remains legible, and both can tell whether the task actually finished.

Sources

[1] https://developers.google.com/search/docs/fundamentals/ai-optimization-guide — Google Search Central: Optimizing your website for generative AI features on Google Search

[2] https://web.dev/articles/ai-agent-site-ux — web.dev: Build agent-friendly websites