Agent readiness1 min read
An agent-friendly website: Muse, Instinct, and every agent welcome
We want Agent Friendly to be the friendliest site for agents. Muse, Instinct, ChatGPT, Claude Code, and agents we have never met are welcome here.
THE AGENT FRIENDLY JOURNAL
Agent readiness
Test real journeys through product pages, local services, forms, and bookings. Each guide asks what a visitor needs to know and what evidence shows that the next step worked.
Agent readiness
14 guides
Agent readiness1 min read
AI agent website checklist: test a real customer journey
Start with one real customer task: find an item, understand its terms, and take the next permitted step. Test the whole path.
Agent readiness1 min read
Agent-readiness rankings vs customer reviews: what each measures
A customer review describes a person’s experience. An agent-readiness assessment asks whether software can retrieve facts and navigate a particular task.
Agent readiness1 min read
What an Agent Friendly score measures—and what it misses
Agent Friendly’s public score is a scoped website-readability signal. It is not proof that an agent completed checkout or that a merchant will be recommended.
Agent readiness1 min read
AI agent access: public information does not authorize a purchase
Reading a public page and authorizing a purchase are different actions. A crawler policy cannot express the buyer’s consent to spend money.
Agent readiness1 min read
Add-to-cart feedback: show agents which item actually changed
A usable control explains what it does and exposes its state. Clear browser interactions help people and provide better evidence for an agent following a task.
Agent readiness1 min read
agents.txt for businesses: what to publish and what to leave out
Agent Friendly’s agents.txt setup describes a business and points agents toward useful public routes. Treat it as an optional discovery aid, not universal adoption or purchase authorization.
Agent readiness1 min read
AI agent readiness for local service businesses
A service business needs readable scope, coverage, and booking information. An agent cannot judge a suitable provider from a town name and a contact button alone.
Agent readiness1 min read
Restaurant menus for AI agents: publish readable choices
Publish menu items, prices, and ordering conditions as readable text. A menu photograph can be useful visually, but it should not be the only way to learn what a restaurant offers.
Agent readiness1 min read
Booking forms: distinguish a request from a reservation
A booking flow should make clear whether the user has requested availability or secured a confirmed reservation. The success message must describe the state the system actually created.
Agent readiness1 min read
Contact pages that give agents a useful handoff
A useful contact page tells an agent where a question belongs and what information to provide. It should offer a clear fallback when self-service content cannot resolve the customer’s task.
Agent readiness1 min read
Accessible buttons help browser agents understand actions
A button should expose a meaningful name and perform an observable action. Clear controls help people using assistive technology and give browser agents better information about what a click will do.
Agent readiness1 min read
Form validation errors an AI agent can recover from
A useful validation error identifies the field, explains the problem, and preserves the user’s other work. A generic “Something went wrong” does not tell an agent how to repair the input.
Agent readiness1 min read
When a bot challenge blocks a legitimate agent visit
A challenge page can prevent a legitimate automated visit from reaching public content. Diagnose the exact block before changing access rules, and preserve the protections required for private or sensitive actions.
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How we write these guides
Agent Friendly publishes these guides with AI assistance, using the product’s documented behavior and linked primary sources. Practical checks are our editorial suggestions. Examples are illustrative, not customer case studies. Each guide links its evidence and shows its publication and revision dates.
We distinguish discovery, citations, task completion, and payments. We do not claim measured search demand, ranking gains, or AI citations without a recorded baseline. Found a mistake? Send the exact page and correction.