# Practical guides to agent-readiness

Understand what AI clients can discover, read and use on a website or API. Start with the buying decision, improve a technical surface or explore how AI visibility is measured.

Every guide here is re-read against its primary sources at least once a month, and the two families that move fastest, agent commerce and MCP discovery, more often than that. Specifications move, and a sentence that was true the day it shipped can stop being true without anything on this site changing. A layout change is not a technical re-check, so each guide's own check date is the one that counts.

## Start here

- [What an agent-readiness audit covers](/guides/agent-readiness-audit)
- [How to choose an audit](/guides/choosing-an-agent-readiness-audit)
- [How technical readiness differs from AI visibility](/guides/agent-readiness-aeo-geo)

## Audit, visibility and priorities

What an audit measures, how to choose one, and how technical readiness differs from AI visibility.

- [What a website and API agent-readiness audit covers](https://turva.dev/guides/agent-readiness-audit)
- [How to choose an agent-readiness audit](https://turva.dev/guides/choosing-an-agent-readiness-audit)
- [Make your website easier for AI assistants to find and cite](https://turva.dev/guides/get-cited-by-ai-assistants)
- [SEO and agent-readiness: overlap and differences](https://turva.dev/guides/seo-vs-agent-readiness)
- [Agent-readiness, AEO and GEO](https://turva.dev/guides/agent-readiness-aeo-geo)
- [Measure agent-readiness with evidence](https://turva.dev/guides/measurement-led-agent-readiness)
- [Common agent-readiness gaps in a measured sample](https://turva.dev/guides/agent-readiness-gaps)

## Content and crawl access

What a text-based client can read on a site, and what the files at the root tell a crawler.

- [llms.txt explained](https://turva.dev/guides/llms-txt)
- [Serving Markdown to AI clients](https://turva.dev/guides/markdown-for-agents)
- [Open Knowledge Format explained](https://turva.dev/guides/open-knowledge-format)
- [Sitemaps, robots.txt and AI crawler access](https://turva.dev/guides/sitemaps-and-robots-for-agents)
- [Response headers for AI clients](https://turva.dev/guides/response-headers-for-agents)
- [When AI clients cannot read rendered pages](https://turva.dev/guides/prerendering-for-agents)
- [JSON-LD and structured data for AI clients](https://turva.dev/guides/json-ld-structured-data)

## Discovery and authentication

How a client finds an interface, and how the service decides what it may access.

- [MCP server cards and discovery](https://turva.dev/guides/mcp-server-card)
- [What agents.json describes](https://turva.dev/guides/agents-json)
- [The /.well-known directory for agent discovery](https://turva.dev/guides/well-known-for-agents)
- [Agentic Resource Discovery and resource catalogs](https://turva.dev/guides/agentic-resource-discovery)
- [Authentication and authorisation for AI agents](https://turva.dev/guides/agent-authentication)

## Commerce and agent operations

Payment flows, commerce discovery, and the limits an agent operates within.

- [x402 and HTTP payment flows](https://turva.dev/guides/x402-agent-payments)
- [Agent commerce discovery: A2A, AP2, ACP and UCP](https://turva.dev/guides/agent-commerce-discovery)
- [Agentic commerce readiness](https://turva.dev/guides/agentic-commerce-readiness)
- [Define what an agent may do with your data](https://turva.dev/guides/letting-agents-act-on-data)
- [AI agent use cases and their operating limits](https://turva.dev/guides/ai-agent-use-cases)

## Frequently asked

**What is an agent-readiness audit?**

An agent-readiness audit measures how well an AI agent can discover, read, and act on a website or an API. It combines an independent scanner's per-check readings, a manual review of the agent-facing surfaces and observed answers from AI assistants to a documented question set, rather than a self-assessment.

**Do I need llms.txt on my site?**

llms.txt gives a curated map of what matters to the clients that fetch it, and no assistant is obliged to be one of them. It does not replace robots.txt or a sitemap, it complements them.

**How do I get my site cited by AI assistants?**

A model cites content it can read cleanly and corroborate. That means machine-readable surfaces such as llms.txt and structured data, a markdown form that does not exhaust the token budget, and being indexed where the assistant searches. None of that guarantees a citation.

**What is an MCP server card?**

An MCP server card is a JSON file that lets an agent discover a site's Model Context Protocol server, its endpoint and its transport, so the agent can connect without a human wiring up the connection. Deployed cards commonly sit at /.well-known/mcp/server-card.json.

**Is agent-readiness the same as SEO?**

No. SEO makes a site rank for a person to click. Agent-readiness makes a site legible and usable by an agent that reads and acts. A site can rank well and still be opaque to agents.

**How is agent-readiness measured?**

By an independent scanner that reads the live site and reports its checks one by one, beside a manual review and observed AI answers. A follow-up repeats the relevant checks and records the result beside the baseline, with the date and any change in the measurement method. No particular score or AI mention is guaranteed.

For an audit, contact info@turva.dev.