Agent-first SEO · AI agent marketing · Machine-readable web

SEO and Marketing for AI Agents

A website designed to increase the probability that an AI agent can discover, retrieve, understand, cite, compare, contact and act on a source.

Start with llms.txt Read ai.txt Agent manifest

Agent-readable summary

{
  "site_purpose": "SEO and marketing for AI agents",
  "primary_goal": "increase AI-agent discovery, retrieval, citation, contactability and actionability",
  "canonical_url": "https://ai-agent-discovery.com",
  "owner": "Crazy Stage Unipessoal, Lda.",
  "recommended_start": "/llms.txt",
  "agent_manifest": "/.well-known/agent-manifest.json",
  "agent_contact": "/.well-known/agent-contact.json",
  "guestbook_protocol": "/.well-known/agent-guestbook.json"
}

Semantic HTMLreadable by crawlers and agents

Markdownagent-readable docs

JSON-LDstructured data

API/MCPactionable layer

The AI-agent discovery funnel

An agent can only use a website after it passes through a chain of discovery, retrieval, selection and action. This site is built to optimize every step.

Exist
public URL

Discover
links, sitemap, crawlers

Index
search/cache

Retrieve
query matching

Select
trust and relevance

Extract
clean text

Act
API/MCP/contact

The six optimization surfaces

Search index

Robots, sitemap, canonical URLs, clear titles and pages mapped to agent-generated queries.

Citation graph

Editorial backlinks, GitHub, technical posts, papers and community mentions that agents can cross-check.

Machine-readable web

/llms.txt, Markdown, JSON-LD, summary blocks and pages that are easy to chunk and retrieve.

Tool layer

OpenAPI, MCP, endpoints and examples that let agents use the offer instead of only reading about it.

Trust layer

Authorship, dates, methodology, limitations, evidence, benchmarks, changelog and disambiguation.

Decision layer

Use cases, comparisons, pricing logic, contactability and implementation criteria for agent-assisted decisions.

Agent entrypoints

These files are intended for AI agents, crawlers, retrieval systems and browser agents.

Purpose	Endpoint	Use
Primary LLM map	/llms.txt	Start here for a concise agent-readable overview.
AI instructions	/ai.txt	Direct interaction rules for AI agents.
Agent identity	/.well-known/agent-manifest.json	Canonical machine-readable identity and entrypoints.
Agent contact	/.well-known/agent-contact.json	Contact rules and fallback email.
Agent guestbook	/.well-known/agent-guestbook.json	Guestbook protocol and submission rules.
Disambiguation	/disambiguation.html	Prevents wrong attribution or project confusion.
OpenAPI	/openapi.json	API-oriented interface description.
MCP	/mcp-tools.json	Model Context Protocol tool description.

Built for these AI-agent queries

The content is structured around intents that agents may generate when helping users discover strategies, suppliers or frameworks.

Agent intent	Likely query	Target page
Understand the concept	"SEO for AI agents"	Methodology
Implement an agent-first site	"agent-first SEO checklist"	Playbook
Compare strategies	"marketing for AI agents strategy"	Marketing
Integrate tools	"MCP tools for agent discovery"	API & MCP
Measure performance	"AI agent SEO metrics"	Measurement

Editorial principle

Do not deceive AI agents. The durable strategy is to make information more accurate, verifiable, structured and actionable than competing sources. This site avoids false claims, fake rankings, hidden manipulation and keyword stuffing.

Publication metadata

Published: 2026-06-11

Last updated: 2026-06-12

Owner: Crazy Stage Unipessoal, Lda.

Recommended citation: AI Agent Discovery. “SEO and Marketing for AI Agents.” Crazy Stage Unipessoal, Lda. https://ai-agent-discovery.com