Agentic Engine Optimization (AEO): Preparing for AI Agents in 2025

The Third Era of Web Optimization

The internet is undergoing its most significant transformation since the mobile revolution. We're witnessing the emergence of the Agentic Web—a paradigm where AI agents don't just read websites to answer questions, but actively interact with them to complete tasks on behalf of users.

Why this changes everything: SEO helped humans find your content. GEO helped AI cite your content. AEO enables AI to use your services. When a user says "order me running shoes under $100," the agent doesn't just recommend products—it browses, compares, adds to cart, and checks out. Sites that agents can easily interact with will capture this traffic; sites that agents struggle with will be bypassed entirely.

The "Google for agents" moment: Just as SEO became essential when Google became the web's front door, AEO becomes essential as AI agents become the web's primary users. The difference: when Google couldn't index your site, you lost visibility. When agents can't use your site, you lose transactions.

This shift demands a new optimization discipline: Agentic Engine Optimization (AEO).

Three Eras of Web Optimization

Era 1: SEO (1990s-Present)

Search Engine Optimization emerged when search engines became the primary gateway to the web.

Goal: Rank higher in Google's list of blue links Optimization focus: Keywords, backlinks, technical crawlability Success metric: Rankings, organic traffic, click-through rates User behavior: Human searches → Scans results → Clicks link → Reads page

Era 2: GEO (2023-Present)

Generative Engine Optimization emerged when AI systems began synthesizing answers instead of listing links.

Goal: Get cited in AI-generated responses Optimization focus: Answerability, authority signals, structured content Success metric: AI citations, referral traffic from AI platforms User behavior: Human asks AI → AI retrieves and synthesizes → Human reads answer

Era 3: AEO (2025-Future)

Agentic Engine Optimization emerges as AI agents gain the ability to take actions, not just provide information.

Goal: Enable AI agents to interact with your services directly Optimization focus: Actionability, machine-readable interfaces, task completion Success metric: Agent interactions, automated transactions, task success rates User behavior: Human delegates task to agent → Agent discovers and uses services → Task completed

What is Agentic Engine Optimization?

AEO is the practice of designing websites, applications, and digital services to be discoverable, understandable, and usable by autonomous AI agents.

The fundamental distinction:

When a user tells an AI agent "Book me a table at an Italian restaurant downtown for Saturday at 7pm," the agent needs to:

1. Discover restaurant services that accept reservations
2. Understand how to interact with those services
3. Execute the booking on behalf of the user
4. Confirm completion and handle edge cases

Websites optimized for AEO make each step possible. Websites not optimized force agents to attempt brittle workarounds—or fail entirely.

AEO vs Answer Engine Optimization

The acronym "AEO" is used for two concepts:

Answer Engine Optimization: Optimizing content to appear in AI-generated answers (essentially synonymous with GEO).

Agentic Engine Optimization: Preparing for AI agents that take actions—not just answer questions.

Key distinction:

• Answer Engine Optimization = Getting cited in AI answers (passive)
• Agentic Engine Optimization = Enabling AI agents to use your services (active)

This guide focuses on Agentic Engine Optimization—preparing for AI agents that act, not just answer.

The Rise of Agentic Browsers

The catalyst for AEO is the emergence of agentic browsers—AI-powered browsing environments designed for autonomous task completion.

OpenAI Atlas

Launched October 21, 2025, Atlas represents OpenAI's vision of an AI-first browser.

Key capabilities:

• Agent Mode for autonomous web task completion
• Research and purchase execution
• Multi-step workflow automation
• Integration with ChatGPT's reasoning capabilities

From OpenAI: "Atlas is the first web browser built from the ground up around artificial intelligence."

Demo capabilities:

• Opening recipe pages, identifying ingredients, adding to Instacart cart
• Navigating e-commerce, comparing listings, checking delivery options
• Executing multi-step workflows with user consent

Safety guardrails:

• Cannot run code in browser, download files, or install extensions
• Cannot access other apps or file system
• Pauses for confirmation on sensitive sites (financial institutions)

ChatGPT Agent (Operator Integration)

OpenAI's journey in agentic AI:

Operator (January 2025): Standalone AI agent for browser-based tasks—filling forms, placing orders, scheduling appointments. Powered by Computer-Using Agent (CUA), combining GPT-4o's vision with reinforcement learning.

ChatGPT Agent (July 2025): Operator's capabilities integrated into ChatGPT—visual browser, text-based browser, terminal access, API connections (Gmail, GitHub, SharePoint).

Performance: 41.6% accuracy on Humanity's Last Exam, 68.9% on BrowseComp benchmark.

Perplexity Comet

Released globally October 2, 2025, Comet brings Perplexity's search synthesis into a full browser.

Key capabilities:

• Real-time web information with transparent citations
• Shopping cart management and checkout completion
• Email composition and sending
• LinkedIn connection handling
• Intelligent tab organization

Comet excels at research-to-action workflows.

Other Agentic Browsers

Open-Source: Browser Use

While commercial browsers capture headlines, Browser Use has become the foundational open-source infrastructure:

From Browser Use: "The most popular open-source solution for AI browser automation, with 23,300+ Discord members."

December 2025 milestone: Released 30B parameter model (3B active), capable of 200 tasks per $1.

Core architecture:

• Built on Microsoft Playwright
• Works with any LLM (OpenAI, Claude, Gemini, local models)
• Vision capabilities (screenshots, not just HTML)
• Structured action space: click, type, scroll, navigate, extract

Why it matters for AEO: Browser Use reveals how agents interact with sites that haven't implemented MCP:

• Agents parse HTML structure—clean, semantic markup works better
• When DOM parsing fails, agents use screenshot analysis—good visual hierarchy helps
• Agents guess which elements to interact with—clear labels and ARIA reduce confusion

Growth Numbers

The adoption of agentic browsing is accelerating:

• 6,900% increase in AI agent requests since July 2025
• 144.7% surge in agent traffic during Black Friday/Cyber Monday 2025
• 27.7% of enterprises have employees using ChatGPT Atlas
• 67% adoption in technology sector

This isn't future trend—it's happening now.

Why Traditional Websites Fail Agents

Most websites are built for humans with mice and keyboards. This creates fundamental problems for AI agents.

The DOM Fragility Problem

When an agent tries to add an item to cart, it must locate specific HTML elements:

<button class="btn-primary add-to-cart" data-product-id="12345">

But HTML structures are:

• Inconsistent: Every site uses different class names
• Fragile: Design updates change identifiers overnight
• Opaque: Button purposes aren't clear from markup alone
• Dynamic: JavaScript-rendered content may not exist when agent inspects

The Authentication Barrier

Most valuable actions require authentication:

• How does an agent log in on behalf of a user?
• How are session tokens securely passed?
• How do CAPTCHAs distinguish legitimate agents from malicious ones?

The Context Gap

Humans understand that "Add to Bag" means "Add to Cart." Agents lack this contextual fluency unless explicitly provided.

The Action Discovery Problem

There's no standard way for agents to discover what actions are possible. A human glances at a page and understands options. An agent must parse HTML, guess at functionality, and hope assumptions are correct.

The AEO Technology Stack

Model Context Protocol (MCP)

MCP is the foundational standard for agent-service communication. Originally developed by Anthropic and donated to the Linux Foundation's Agentic AI Foundation in December 2025.

From Anthropic: "In one year, MCP has become one of the fastest-growing open-source projects in AI, with over 97 million monthly SDK downloads, 10,000 active servers."

MCP Architecture:

Adoption: ChatGPT, Claude, Gemini, Microsoft Copilot, VS Code, backed by Anthropic, OpenAI, Google, Microsoft, AWS, Cloudflare, Bloomberg.

MCP is the "USB standard" for AI—a universal interface allowing any agent to connect to any service.

Agent-to-Agent Protocol (A2A)

While MCP handles agent-to-service communication, Google's A2A Protocol addresses agent-to-agent communication.

From A2A specification: "A2A enables independent AI agents to discover each other, negotiate communication formats, and collaborate without exposing private code or data."

Key concepts:

• Agent discovery: Every agent publishes JSON at /.well-known/agent.json
• Capability negotiation: Agents declare what they can do
• Secure collaboration: Agents work together without exposing internals

Support: Over 100 companies including AWS, Cisco, Google, Microsoft, Salesforce, SAP, ServiceNow.

MCP and A2A are complementary: MCP connects agents to tools; A2A connects agents to each other.

llms.txt Standard

Proposed by Jeremy Howard (Answer.AI), llms.txt provides AI systems with structured site information:

llms.txt (index):

# Site Name
> Brief description

## Key Resources
- [Guide](/guide.md): Comprehensive guide
- [FAQ](/faq.md): Common questions

## Context
Specializes in [topic]. Written by [credentials].

llms-full.txt (comprehensive): Contains all documentation in one place—larger but faster for agents.

Adoption: 844,000+ implementations as of October 2025, accelerated when Mintlify rolled out across thousands of sites including Anthropic and Cursor.

Semantic HTML and Accessibility

Critical insight: Sites optimized for screen readers work remarkably well with AI agents. Both rely on DOM structure rather than visual cues.

Agentic systems leverage accessibility infrastructure:

• ARIA labels: Help agents understand element purposes
• Semantic HTML5: Provides structural context (nav, main, article)
• Form labels: Connect inputs to purposes
• Alt text: Describes images and icons

A button with only an icon is opaque to agents. A button with proper markup is immediately understandable:

<button aria-label="Add iPhone 15 to shopping cart" role="button">

Accessibility investments now serve double duty—improving experience for users with disabilities AND preparing for agent interaction.

Schema.org Actions

Schema markup gains expanded importance for AEO:

The Actions vocabulary specifically describes actionable items—critical for AEO.

The SEO-GEO-AEO Framework

These disciplines are cumulative—each builds on the previous.

Layer 1: SEO Foundation

Purpose: Ensure content is discoverable and indexable

• Technical crawlability (robots.txt, sitemaps)
• On-page optimization (titles, headers, content)
• Authority signals (backlinks, domain trust)
• User experience (Core Web Vitals, mobile)

Layer 2: GEO Enhancement

Purpose: Ensure content is citable and authoritative

• Answerable content structure
• E-E-A-T signals
• AI crawler accessibility
• Citation-worthy original insights

Layer 3: AEO Extension

Purpose: Ensure services are actionable and reliable

• Machine-readable interfaces (MCP, APIs)
• Agent authentication pathways
• Action discovery mechanisms
• Transaction reliability and error handling

Build from foundation up. A site with poor SEO struggles with GEO. A site with poor GEO struggles with AEO.

Implementing AEO: Practical Framework

Phase 1: Audit and Assessment

Agent Accessibility Audit:

• Can AI crawlers access content? (Check robots.txt for GPTBot, ClaudeBot, PerplexityBot)
• Is site structure semantic and accessible?
• Are interactive elements properly labeled?
• Do you have structured data describing services?

Action Inventory:

• What actions can users perform?
• Which actions have highest automation value?
• What authentication is required?
• What are failure modes and edge cases?

Competitive Analysis:

• Are competitors exposing APIs or MCP interfaces?
• What actions are agents attempting on your site?
• Where are agents failing in your industry?

Phase 2: Foundation Optimization

Semantic Structure:

• Implement comprehensive ARIA labels on interactive elements
• Use semantic HTML5 (nav, main, article, section)
• Ensure form fields have associated labels
• Provide clear, descriptive text for buttons and links

Metadata Enhancement:

• Create llms.txt describing site and services
• Implement comprehensive schema, especially Actions
• Ensure meta descriptions reflect page capabilities
• Add machine-readable service descriptions

Agent Crawler Access:

• Explicitly allow AI crawlers in robots.txt
• Ensure dynamic content accessible without JavaScript
• Provide XML sitemaps with comprehensive coverage
• Implement proper caching headers

Phase 3: Interface Exposure

API Development:

• Identify high-value actions for API exposure
• Design RESTful or GraphQL endpoints with clear documentation
• Implement proper authentication (OAuth 2.0)
• Create comprehensive API documentation

MCP Server Implementation:

• Evaluate which services to expose via MCP
• Implement MCP server with appropriate tools
• Write clear, detailed tool descriptions
• Test with Claude Desktop and ChatGPT

# Example MCP Server (Python)
from mcp.server import Server
from mcp.types import Tool, TextContent

server = Server("my-store")

@server.tool()
async def search_products(query: str, category: str = None) -> list[TextContent]:
    """Search products by query and optional category."""
    results = await product_db.search(query, category)
    return [TextContent(type="text", text=format_results(results))]

@server.tool()
async def add_to_cart(product_id: str, quantity: int = 1) -> list[TextContent]:
    """Add a product to the shopping cart."""
    result = await cart_service.add(product_id, quantity)
    return [TextContent(type="text", text=f"Added {quantity}x {product_id}")]

Phase 4: Authentication and Security

Agent Authentication Strategy:

• Define how agents authenticate on behalf of users
• Implement OAuth flows compatible with agent use
• Consider API keys for trusted agent platforms
• Plan session handoff from browser to agent

Security Considerations:

• Implement rate limiting for agent traffic
• Distinguish legitimate agents from malicious bots
• Protect against prompt injection attacks
• Audit agent actions for anomalous behavior

User Control:

• Provide visibility into agent actions
• Implement approval workflows for high-risk operations
• Enable users to revoke agent access
• Maintain audit logs

Phase 5: Testing and Iteration

Agent Testing:

• Test interfaces with multiple agentic systems
• Simulate common user requests and verify completion
• Identify failure modes and edge cases
• Measure success rates and completion times

Monitoring:

• Track agent traffic separately from human traffic
• Monitor API and MCP endpoint usage
• Measure task completion rates
• Identify and address failure patterns

Industry-Specific AEO Strategies

E-Commerce

High-value agent actions:

• Product search and filtering
• Price comparison
• Cart management
• Checkout and payment
• Order tracking

Implementation priorities:

• Product schema with comprehensive attributes
• Search API with rich filtering
• Cart API with full CRUD operations
• Secure payment integration

Travel and Hospitality

High-value agent actions:

• Availability search
• Price comparison and fare rules
• Booking creation
• Itinerary modification
• Cancellation handling

Implementation priorities:

• Real-time availability APIs
• Complex search with flexible parameters
• Booking workflow automation
• Calendar integration

Financial Services

High-value agent actions:

• Account information retrieval
• Transaction initiation
• Bill payment scheduling
• Investment operations

Implementation priorities:

• Strong authentication (regulatory requirement)
• Read-only vs. transactional access tiers
• Comprehensive audit logging
• Risk-appropriate approval workflows

SaaS and Enterprise Software

High-value agent actions:

• Data retrieval and reporting
• Configuration changes
• User management
• Workflow automation

Implementation priorities:

• Comprehensive API coverage
• Granular permission systems
• Webhook and event systems
• Documentation and examples

Measuring AEO Success

Agent Traffic Metrics

Identification:

• User agent strings for known AI agents
• API and MCP endpoint traffic
• Traffic patterns characteristic of automation

Volume metrics:

• Agent sessions and requests
• Actions attempted vs. completed
• Unique agents interacting with services
• Growth trends over time

Task Completion Metrics

Success rates:

• What percentage of agent-initiated tasks complete?
• Where do agents most commonly fail?
• How do completion rates compare across platforms?

Efficiency metrics:

• How many steps do agents take?
• What is average task completion time?
• How often do agents retry failed operations?

Business Impact Metrics

Transaction metrics:

• Revenue through agent-initiated transactions
• Average order value (agent vs. human)
• Customer acquisition through agent recommendations

Quality metrics:

• Do agents represent services correctly?
• Are agent transactions error-free?
• Do agent transactions generate support tickets?

Security Considerations

Known Risks

Prompt injection attacks: From security research: "Indirect prompt injection is a systemic challenge facing the entire category of AI-powered browsers." Malicious content can manipulate agent behavior.

Authentication vulnerabilities: Early research found instances where agentic browsers bypassed encryption, exposing private authentication data.

Data exposure: Agents operating on behalf of users may access and transmit sensitive information in unexpected ways.

Mitigation Strategies

• Implement strong authentication for all agent operations
• Add rate limiting appropriate for agent traffic
• Validate agent identity before executing actions
• Log and audit all agent actions
• Test for prompt injection vulnerabilities
• Define and enforce permission scopes
• Start with read-only operations before enabling sensitive actions

Common AEO Mistakes

Strategic Mistakes

• Waiting for "perfect" standards (MCP is mature enough now)
• Building for single agent platform instead of open standards
• Ignoring AEO because current traffic is low (6,900% annual growth)
• Over-engineering before validating demand
• Treating AEO as separate from SEO/GEO

Technical Mistakes

• Exposing sensitive operations via MCP without authentication
• Creating MCP tools with vague descriptions
• Not implementing rate limiting
• Forgetting graceful error handling
• Building brittle integrations that break with UI changes

Security Mistakes

• Allowing any agent to access authenticated endpoints
• Not validating agent identity
• Exposing internal systems through permissive MCP servers
• Ignoring prompt injection vulnerabilities
• Failing to audit agent actions

AEO Implementation Checklist

Phase 1: Foundation (Month 1)

Assessment:

• Audit current agent accessibility
• Inventory actions users might delegate
• Identify high-value, low-risk operations to expose first
• Review competitor agent readiness
• Assess current API coverage

Quick Wins:

• Allow AI crawlers in robots.txt
• Implement ARIA labels on interactive elements
• Add schema.org Action markup
• Create llms.txt with service descriptions
• Ensure semantic HTML structure

Phase 2: Interface Development (Month 2-3)

API Preparation:

• Document existing APIs comprehensively
• Identify gaps between website and API coverage
• Design new endpoints for agent operations
• Implement OAuth 2.0 authentication
• Create sandbox environments

MCP Implementation:

• Implement MCP server for initial scope
• Write clear tool descriptions
• Test with Claude Desktop and ChatGPT
• Document integration patterns

Phase 3: Expansion (Month 4-6)

Capability Growth:

• Add write operations (cart, forms)
• Implement transaction support
• Build error handling and recovery
• Create agent-specific analytics
• Develop approval workflows for sensitive operations

Security Hardening:

• Implement comprehensive authentication
• Add rate limiting and abuse detection
• Create audit logging
• Test for prompt injection
• Define permission scopes

Success Criteria

• Agent traffic growing month-over-month
• Task completion rate > 80% for supported operations
• Error rate < 5% for agent transactions
• Positive user feedback on agent-completed tasks
• Integration by multiple agent platforms

The Future of the Agentic Web

Standardization

MCP's donation to the Linux Foundation signals industry commitment:

• Broader adoption across platforms
• Industry-specific extension specifications
• Security and authentication standards
• Interoperability certification

Agent Capability Expansion

Current agents are limited. Coming improvements:

• Better multi-step workflow handling
• Improved error recovery
• Enhanced visual interface understanding
• Stronger reasoning about appropriate actions

Regulatory Attention

As agents handle more transactions:

• Consumer protection for agent-initiated transactions
• Liability frameworks for agent errors
• Privacy requirements for agent data access
• Disclosure requirements for agent involvement

Conclusion

Agentic Engine Optimization represents the next evolution in web optimization:

1. SEO → GEO → AEO: Each layer builds on the previous
2. MCP and A2A: The standards enabling agent interaction
3. Accessibility = Agent-readiness: Screen reader optimization benefits agents
4. Start now: 6,900% growth means early movers win

Organizations that implement AEO today will capture interactions that competitors lose to agent failures tomorrow.