seo-for-agents（SEO FOR Agent 搜索）

SEO for Agents

How to make your web presence discoverable by AI agents, not just humans. Traditional SEO optimizes for Google's crawler. Agent SEO optimizes for LLMs, AI search engines, and autonomous agents that need to find and understand your services.

The Core Problem

Agents won't go to your webinar. They won't read your blog post series. They won't watch your YouTube video. They won't click your CTA button.

Agents need:

• Structured, machine-readable information about what you do
• Direct API access to your capabilities
• Clear, unambiguous claims they can evaluate programmatically
• Consistent, up-to-date data at predictable URLs

If your entire web presence is optimized for humans clicking through a funnel, you are invisible to agents.

llms.txt Protocol

What It Is

llms.txt is a file you place at the root of your domain (like robots.txt) that tells LLMs and AI agents what your site is about and how to interact with it.

It's the equivalent of robots.txt for the AI era — except instead of telling crawlers what NOT to index, it tells agents what IS available and how to use it.

File Location

https://yourdomain.com/llms.txt

File Structure

# Your Company Name

> One-line description of what you do.

## About

2-3 sentences about your company, written for an LLM to parse.
Be specific. Be factual. No marketing fluff.

## Services

- [Service Name](https://yourdomain.com/service-page): Brief description
- [Another Service](https://yourdomain.com/another): Brief description

## API

- [API Documentation](https://yourdomain.com/api/docs): Full API reference
- [API Status](https://yourdomain.com/api/status): Current API health

## Contact

- Email: contact@yourdomain.com
- API Support: api-support@yourdomain.com

## Optional

- [Blog](https://yourdomain.com/blog): Latest posts
- [Pricing](https://yourdomain.com/pricing): Current pricing
- [Case Studies](https://yourdomain.com/cases): Example work

Implementation Example

For an AI agent deployment company:

# IAM Solutions

> AI agent deployment and managed automation for small businesses.

## About

IAM Solutions deploys production AI agents on dedicated hardware
(Mac Mini, Linux servers) for small businesses. We handle the full
stack: hardware, software, security, and ongoing management.
Clients own their data and pay for their own API keys.

## Services

- [Agent Deployment](https://iamsolutions.tech/deploy): Full-stack AI agent deployment on dedicated hardware
- [Managed Automation](https://iamsolutions.tech/managed): Ongoing agent management and optimization
- [Security Hardening](https://iamsolutions.tech/security): Production security for AI agent infrastructure

## API

- [Agent Health API](https://iamsolutions.tech/api/health): Check agent deployment status
- [Onboarding API](https://iamsolutions.tech/api/onboard): Start client onboarding process

## Contact

- Email: sam@iamsolutions.tech
- Schedule: https://iamsolutions.tech/schedule

Extended Format: llms-full.txt

For more detailed information, create llms-full.txt with comprehensive content that LLMs can use for deeper understanding:

https://yourdomain.com/llms-full.txt

This file can be longer and include FAQs, detailed service descriptions, pricing details, and technical specifications.

GEO: Generative Engine Optimization

How AI Search Engines Differ from Google

Google ranks pages based on links, authority, and keyword relevance. AI search engines (Perplexity, ChatGPT Search, Google AI Overviews) work differently:

GEO Optimization Strategies

1. Write in claims, not narratives

Bad (human SEO):

"In today's fast-paced business environment, companies are increasingly turning to AI solutions to streamline their operations..."

Good (GEO):

"IAM Solutions deploys AI agents on dedicated Mac Mini hardware for $X/month. Each deployment includes 5-layer security hardening, daily health checks, and a 5-file memory system. Typical client ROI is measurable within 6 weeks."

2. Use Q&A format for key information

## Frequently Asked Questions

### How long does deployment take?
A standard single-agent deployment takes 2-3 business days from
signed agreement to Day 1 onboarding.

### What hardware is required?
Minimum: Apple M1 Mac Mini, 16GB RAM, 256GB SSD.
Recommended: Apple M2 Pro Mac Mini, 32GB RAM, 512GB SSD.

### Who owns the data?
The client owns all data. We never access client data without
explicit permission. All API keys are client-owned and client-paid.

3. Provide structured, citation-friendly data

AI search engines prefer content that can be directly quoted. Make your key claims:

• Specific: "6-week onboarding" not "quick onboarding"
• Verifiable: "5-layer security stack" not "comprehensive security"
• Self-contained: Each claim should make sense without surrounding context
• Consistent: Same numbers and claims across all pages

4. Maintain a facts page

Create a single page with all key facts about your business in a structured format:

# Facts About [Company]

- Founded: [Year]
- Headquarters: [City, State]
- Specialty: [One sentence]
- Clients served: [Number]
- Average deployment time: [Timeframe]
- Hardware platform: [Specific]
- Pricing model: [Description]
- Data ownership: Client owns all data

Structured Data for Agent Discovery

Schema.org Markup

Add structured data to your pages so agents can parse your offerings programmatically:

{
  "@context": "https://schema.org",
  "@type": "Service",
  "name": "AI Agent Deployment",
  "provider": {
    "@type": "Organization",
    "name": "IAM Solutions"
  },
  "description": "Production AI agent deployment on dedicated hardware",
  "areaServed": "United States",
  "serviceType": "AI Infrastructure",
  "offers": {
    "@type": "Offer",
    "priceCurrency": "USD",
    "priceSpecification": {
      "@type": "PriceSpecification",
      "price": "Contact for quote",
      "billingIncrement": "Monthly"
    }
  }
}

Agent-Facing API Endpoints

Beyond your human-facing website, expose endpoints that agents can call directly:

GET /api/services        → List of available services with descriptions
GET /api/services/:id    → Detailed service information
GET /api/availability    → Current availability and lead times
GET /api/capabilities    → What your agents can do
POST /api/inquiry        → Submit an inquiry (structured input)

Example response:

{
  "services": [
    {
      "id": "agent-deploy",
      "name": "Agent Deployment",
      "description": "Full-stack AI agent on dedicated hardware",
      "lead_time_days": 3,
      "includes": [
        "5-layer security stack",
        "5-file memory system",
        "Daily health checks",
        "30-day onboarding support"
      ],
      "requires": {
        "hardware": "Client provides or we source",
        "api_keys": "Client-owned and paid"
      }
    }
  ]
}

Cloudflare /crawl Endpoint

Cloudflare offers a /crawl endpoint that returns clean, agent-friendly content from your site. If you're on Cloudflare:

What It Does

The /crawl endpoint strips navigation, ads, scripts, and styling from your pages, returning clean markdown-like content that agents can easily parse.

How to Use It

If your site is on Cloudflare, agents can access:

https://yourdomain.com/crawl?url=https://yourdomain.com/services

This returns a clean, structured version of the page content without HTML cruft.

Optimization for /crawl

• Ensure your main content is in semantic HTML (<article>, <section>, <main>)
• Use proper heading hierarchy (h1 > h2 > h3)
• Put key information early in the page (agents may truncate)
• Avoid critical information in images, JavaScript-rendered content, or iframes

Content Strategy for LLM Discoverability

The Agent-Discoverable Content Stack

Layer 1: Machine-readable identity (llms.txt, structured data, API)

• This is your "business card" for agents
• Must be maintained and accurate at all times

Layer 2: Claim-dense reference pages

• Service pages written as structured facts, not sales copy
• Pricing pages with actual numbers
• FAQ pages with specific, quotable answers

Layer 3: Demonstrable expertise content

• Technical blog posts that show depth
• Case studies with specific metrics
• Open-source tools and resources

Layer 4: Conversational content (lowest priority)

• Blog posts, newsletters, social media
• Still valuable for human discovery
• Agents may reference but won't navigate to

Content Anti-Patterns for Agent Discovery

Things that make you invisible to agents:

• Gated content: If it requires an email to access, agents can't see it
• PDF-only resources: PDFs are harder for agents to parse
• JavaScript-rendered content: If the content isn't in the HTML source, agents may miss it
• Video/audio-only content: No transcript = invisible to agents
• Vague claims: "Industry-leading" means nothing to an agent
• Inconsistent information: Different prices/specs on different pages destroys trust signals
• Stale content: Outdated information reduces citation confidence

Practical Implementation Checklist

Week 1: Foundation

• [ ] Create and deploy llms.txt at domain root
• [ ] Add Schema.org structured data to service pages
• [ ] Audit all pages for agent-parseable content
• [ ] Create a facts/specs page with structured claims

Week 2: Content Optimization

• [ ] Rewrite service pages in claim-dense format
• [ ] Add Q&A sections to key pages
• [ ] Ensure all content is in semantic HTML
• [ ] Remove or supplement gated content with public summaries

Week 3: API & Discoverability

• [ ] Create /api/services endpoint (even if simple JSON)
• [ ] Set up /api/capabilities endpoint
• [ ] Test site with AI search engines (ask Perplexity about your business)
• [ ] Verify Cloudflare /crawl returns clean content (if applicable)

Week 4: Monitoring & Iteration

• [ ] Monitor AI search engine citations (search for your brand in Perplexity, ChatGPT)
• [ ] Track API endpoint usage
• [ ] Update llms.txt with any new services or changes
• [ ] A/B test claim formats to see what gets cited more

Ongoing

• [ ] Update llms.txt whenever services change
• [ ] Keep structured data in sync with actual offerings
• [ ] Monitor AI search engine results monthly
• [ ] Refresh Q&A content based on actual questions received

Measuring Agent-SEO Success

Traditional SEO measures rankings and clicks. Agent SEO measures:

1. Citation frequency: How often AI search engines cite your content
2. API call volume: How many agents are discovering and using your endpoints
3. llms.txt access logs: How frequently your llms.txt is being fetched
4. Inquiry quality: Are agent-routed inquiries well-qualified?
5. Brand mentions in AI responses: When someone asks an AI about your space, do you come up?

How to Check

# Check if Perplexity knows about you
# Ask: "What companies deploy AI agents on Mac Mini hardware?"

# Check your llms.txt access logs
grep "llms.txt" /var/log/nginx/access.log | wc -l

# Monitor API discovery endpoints
grep "/api/services" /var/log/nginx/access.log | wc -l

The Bottom Line

For humans: Build trust through narrative, social proof, and design.

For agents: Build trust through structured data, consistent claims, and machine-readable endpoints.

You need both. But most companies have zero agent-discoverability. That's the gap. Fill it.