AI agents are already selecting brands, booking services, and completing transactions on behalf of real customers. Most businesses have no idea this is happening, and fewer still have done anything to influence which brand gets chosen.
Agent Preference Optimization (APO) is the discipline of making your brand the one agents select. Not through backlinks or domain authority. Through structured signals, entity clarity, and machine-readable capability data that agents actually use when making decisions.
This playbook, developed by Booked Solid Digital, defines APO as a discipline, explains how agents build brand preference, and gives you a concrete framework for achieving Agentic Brand Preference (ABP) at scale.
The Shift That Changes Everything
The urgency is real.
A July 2025 survey of 750 U.S. consumers by Kearney found that 60% of shoppers expect to use agentic AI to make purchases within the next 12 months.
Separate research found that two-thirds of Gen Z consumers and more than half of Millennials already use large language models to research products before buying.
The brands that build agent-readable infrastructure now will have a head start that compounds.
Agents Don’t Search. They Decide.
When a human searches, they scan results, compare options, click through, and decide. The entire SEO discipline exists to influence that human decision-making process.
Agents skip every step of that process.
An AI agent receives a task (“Book a facial at a highly rated med spa in Austin for next Tuesday”) and immediately begins evaluating candidates.
It pulls structured data, checks availability via APIs, confirms service areas, reads pricing signals, and selects the business that best satisfies all conditions.
The human never sees a search results page. They get a confirmation.
No click. No browsing. No brand impression. Just a selection.
Why SEO and GEO Don’t Move the Needle Here
Your domain rating doesn’t appear in an agent’s evaluation criteria. Neither do your backlinks, your click-through rate, or your time-on-page metrics.
GEO got your brand cited in AI-generated answers. That mattered when a human was reading those answers.
Agents don’t read answers. They parse structured signals.
An agent evaluating two med spas in Austin doesn’t care which one ranks first on Google or which one gets cited more often by ChatGPT.
It cares which one has machine-readable availability data, a clear service schema, a confirmed booking endpoint, and unambiguous entity signals. That’s the brand it selects.
This is the gap APO fills.
The Evolution: SEO to GEO to Agentic SEO to APO
Phase 1: SEO (Human to Search Engine)
- The original model
- A human types a keyword
- A search engine like Google returns ranked links
- Success was measured by position, clicks, and traffic
Phase 2: GEO/AEO (Human to Generative Engine)
- A human asks a question in natural language
- A generative AI, for example ChatGPT produces a synthesized answer, often citing sources
- The human reads, may follow a source link, and decides
- Success is measured by citations and Share of Model (SoM)
Phase 3: Agentic SEO (Human to Agent)
- A human delegates a task to an AI agent like Manus, Suna or Perplexity
- The agent plans, researches, compares, and executes
- The human may never interact with the web at all
- Success shifts to whether the agent selects your business to complete the task
Phase 4: Agent to Agent (The APO Era)
- The full agentic model
- A customer’s personal agent negotiates directly with a business’s agent to confirm availability, agree on terms, and complete a transaction
- No human visits your site. No human reads your content
- Success is determined entirely by whether your brand has the signals agents need to select and trust you
This is the era APO is built for.
What Is Agent Preference Optimization (APO)?
Defining APO as a Discipline
Agent Preference Optimization (APO) is the practice of structuring your brand’s digital presence so that AI agents consistently evaluate, select, and act on your behalf over competing options.
APO sits at the intersection of structured data, API architecture, entity clarity, and brand signal management. It has nothing to do with link building, content volume, or keyword density. Those are human-facing signals. APO works entirely at the machine layer.
Agent Preference Optimization (APO) is making your brand the preferred selection for AI agents when they evaluate options on a user’s behalf.
It works through structured data, API availability, entity clarity, and machine-readable capability signals rather than traditional search ranking factors like backlinks or domain authority.
What Agents Actually Evaluate When Selecting a Brand
Agents run through a decision sequence when evaluating any brand for a given task. Understanding that sequence is the foundation of APO.
| Evaluation Signal | What the Agent Checks | Where It Comes From |
|---|---|---|
| Entity clarity | Can I unambiguously identify this brand? | Knowledge graph, Schema.org, sameAs |
| Capability match | Can this brand do what the user needs? | Agent Manifest, potentialAction |
| Service area | Does it serve the user’s location? | areaServed in Schema |
| Availability | Is it available at the requested time? | /api/availability endpoint |
| Pricing | What does it cost? | priceSpecification in Schema |
| Ratings | What do verified sources say? | aggregateRating |
| Execution path | Can I complete the transaction automatically? | /api/book endpoint |
Every signal in this table is something you can control. None of them involve traditional SEO metrics.
Agentic Brand Preference (ABP): The Outcome APO Drives
Agentic Brand Preference (ABP) is the measurable outcome of a successful APO strategy. It answers one question: across all agent-driven queries for your service category, how often is your brand the one agents choose?
High ABP means agents consistently select your brand. Low ABP means you’re invisible to agent-driven demand, even if you rank well in traditional search.
ABP is quantified through Agent Share of Model (ASoM), a metric defined and tracked by Booked Solid Digital.
The APO Framework: How Agents Build Brand Preference
Entity Clarity: Can Agents Unambiguously Identify Your Brand?
Agents need to know exactly who you are before they can select you.
Entity clarity means your brand exists as a distinct, well-defined entity in the knowledge graph, with consistent signals across your website, structured data, and third-party sources.
Checklist for entity clarity:
- Business name matches exactly across your website, Google Business Profile, Schema markup, and all major directories
- sameAs properties in your Schema link to authoritative profiles (Google, Wikidata, LinkedIn, industry directories)
- Your NAP (name, address, phone) is consistent everywhere agents might retrieve it
- Your brand has a clear @type in Schema.org (LocalBusiness, MedicalBusiness, etc.)
- You use unique identifiers such as a Google Knowledge Panel, ISBN, or DUNS number where applicable
Without entity clarity, an agent may fail to identify your brand as a valid candidate entirely. This is the most common and most fixable APO problem.
Capability Signals: Can You Execute What the Agent Needs?
Once an agent identifies your brand, it checks whether you can actually fulfill the task. Capability signals tell agents what your brand can do, not just what it sells.
The primary vehicle for capability signals is the Agent Manifest, a machine-readable file at /agent-manifest.json that explicitly declares your brand’s executable capabilities.
{
"name": "Radiance Med Spa",
"version": "1.0",
"capabilities": [
"book facial",
"book massage",
"provide pricing",
"check availability"
],
"endpoints": {
"availability": "/api/availability",
"booking": "/api/book",
"pricing": "/api/pricing"
},
"policies": {
"cancellation": "24 hours notice",
"payment": "deposit required"
}
}
Secondary capability signals come from potentialAction in your Schema.org markup, which links specific services to executable booking or purchase endpoints.
Trust Signals: What Data Points Confirm You’re the Safer Choice?
Agents operating on behalf of humans carry implicit accountability. They select businesses they can justify to the user. Trust signals give them that justification.
Key trust signals agents evaluate:
- aggregateRating in Schema (verified review count and score)
- Years in operation (foundingDate in Schema)
- License and certification data where applicable
- Clear cancellation and refund policies in machine-readable format
- Verified business status on Google, Yelp, and category-specific platforms
Note that these are not the same as E-E-A-T signals for human-facing content. An agent isn’t reading your about page to assess expertise. It’s parsing structured fields that confirm your legitimacy quickly and unambiguously.
Consumer sentiment reinforces why this matters.
A Salesforce survey found that 72% of consumers demand clear disclosure about whether they are interacting with AI or a human.
Brands that structure their agent-facing data with transparent capability declarations and honest policy documentation build the kind of machine-readable credibility that agents can verify and act on.
Structured Availability: Are Your Services Machine-Readable?
An agent can’t book an appointment at a business that doesn’t expose availability data. This is where many businesses hit a hard wall.
Structured availability requires:
- A live API endpoint that returns real-time open slots (/api/availability)
- A booking endpoint that accepts and confirms reservations (/api/book)
- priceSpecification in your Schema that reflects current pricing
- areaServed data that confirms you serve the user’s location
Businesses that don’t expose this data are invisible to scheduling agents regardless of how well they rank in traditional search.
The competitive gap here is real because most businesses have not yet built this infrastructure.
Technical Foundations of APO
API-First Architecture: Your Brand’s Interface With the Agent Layer
Your website serves humans. Your APIs serve agents. These are two separate channels, and both need to be built and maintained deliberately.
At minimum, your brand needs three API endpoints:
| Endpoint | Method | Function |
|---|---|---|
| /api/availability | GET | Returns open time slots by date and service type |
| /api/book | POST | Accepts booking requests and returns confirmation |
| /api/pricing | GET | Returns current pricing by service |
Document all endpoints with OpenAPI/Swagger specifications. Agents use these specifications to understand how to interact with your API correctly.
Without documentation, even a functioning API may be ignored.
The Agent Manifest: Declaring Your Brand’s Capabilities
The Agent Manifest is the APO equivalent of a robots.txt file, except instead of telling crawlers what not to access, it tells agents exactly what your brand can do and how to do it.
Place your manifest at /agent-manifest.json and include it in your sitemap. Update it whenever you add or change capabilities. This file becomes an agent’s first stop when evaluating whether your brand is a candidate for a given task.
Schema Markup for Agent Selection
Schema.org markup is the structured data layer agents read to evaluate your brand. For APO, the most critical Schema properties are:
Service-level Schema with potentialAction:
{
"@context": "https://schema.org",
"@type": "Service",
"name": "HydraFacial",
"provider": {
"@type": "LocalBusiness",
"name": "Radiance Med Spa",
"sameAs": "https://radiancemedspa.com"
},
"areaServed": {
"@type": "City",
"name": "Austin"
},
"potentialAction": {
"@type": "BookAction",
"target": {
"@type": "EntryPoint",
"url": "https://radiancemedspa.com/api/book",
"actionPlatform": "https://schema.org/DesktopWebPlatform",
"encodingType": "application/json"
}
}
}
This tells an agent your business exists, where it operates, and exactly how to initiate a booking transaction.
Priority Schema properties for APO:
- potentialAction (links services to booking/purchase endpoints)
- areaServed (confirms geographic coverage)
- priceSpecification (exposes pricing in structured form)
- aggregateRating (provides trust signals)
- openingHoursSpecification (exposes operating hours)
- sameAs (connects your entity to authoritative profiles)
llms.txt and Agent Discovery
llms.txt is an emerging convention that helps AI systems navigate your site.
Think of it as a human-readable summary of your site’s most important content, written for language models rather than search engine crawlers.
Early adoption data suggests this is worth prioritizing.
Brands including Cloudflare, HubSpot, and Stripe have already published llms.txt files, with some reporting a 12% increase in AI-generated traffic within two weeks of implementation and a 25% rise in organic traffic.
For APO purposes, your llms.txt should reference your Agent Manifest, your key API endpoints, and your primary service Schema.
This creates a discovery chain: an agent finds your llms.txt, identifies your manifest, reads your capabilities, and begins evaluation.
Measuring Agentic Brand Preference (ABP)
Agent Share of Model (ASoM): Are Agents Finding You?
Agent Share of Model (ASoM) measures the percentage of agent-driven queries for your service category that result in your brand being evaluated as a candidate.
How to track ASoM:
- Monitor API logs for agent user-agents including GPTBot, ClaudeBot, PerplexityBot, and emerging agent identifiers
- Track the volume of requests to your /api/availability and /api/pricing endpoints from non-human sources
- Compare inbound agent traffic against estimated total category query volume
Rising ASoM means more agents are finding and evaluating your brand. This is a leading indicator of ABP growth.
Execution Rate: Are They Selecting You?
Execution Rate measures the percentage of agent evaluations that result in a completed transaction through your API.
A high ASoM with a low Execution Rate signals that agents find you but something in your capability signals or API reliability causes them to select a competitor instead.
Common Execution Rate problems:
- API downtime or slow response times
- Incomplete availability data (agents can’t confirm a slot)
- Missing or outdated pricing information
- Schema errors that create mismatches between declared capabilities and actual API behavior
Treat Execution Rate as your APO conversion rate.
Improving it follows the same diagnostic logic as traditional conversion rate optimization, applied to machine-to-machine interactions.
Attribution in Agent-Driven Journeys
This is where traditional analytics breaks down completely.
When an agent books a service on a customer’s behalf, the customer may never visit your website.
No session appears in Google Analytics. No referral source gets logged. The booking simply appears in your system.
Build attribution for agent-driven bookings through:
- Unique booking ID prefixes that identify agent-initiated transactions (e.g., AGT-XXXXX)
- API call logs with user-agent strings that identify the originating agent platform
- Customer surveys that ask how the booking was made
- Webhook notifications from agent platforms that support booking attribution headers
How Consumers Prompt Agents Affects Which Brands Get Selected
One measurement consideration most brands overlook entirely: the phrasing a consumer uses when instructing an agent changes which businesses the agent evaluates.
Research from Carnegie Mellon University found that using synonyms to alter basic search prompts, with no change in meaning, shifted brand recommendation likelihood by as much as 78.3%.
This has direct implications for APO. Your structured data and content should reflect the full range of ways customers describe your service, not just the terms your marketing team prefers.
Practical steps:
- Review customer service logs and booking inquiry language to identify how customers actually describe what they want
- Map those natural language patterns to your Schema service names and capability declarations in your Agent Manifest
- Test how your brand appears across different prompt phrasings using agent simulators and tools like Perplexity’s reasoning model, which surfaces the decision criteria agents apply in real time
- Update your manifest and Schema when you identify gaps between how customers ask and how agents categorize your services
The APO Playbook: 10 Steps to Agentic Brand Preference
| Step | Action | Priority |
|---|---|---|
| 1 | Audit every task a customer might delegate to an agent for your business category | High |
| 2 | Build and publish an Agent Manifest at /agent-manifest.json | High |
| 3 | Expose a minimum viable API set: availability (GET), booking (POST), pricing (GET) | High |
| 4 | Implement Service Schema with potentialAction linking to your booking endpoint | High |
| 5 | Add areaServed, priceSpecification, and aggregateRating to all service Schema | High |
| 6 | Document your APIs with OpenAPI/Swagger specifications | Medium |
| 7 | Publish an llms.txt file referencing your manifest and key endpoints | Medium |
| 8 | Set up API log monitoring for agent user-agents to begin tracking ASoM | Medium |
| 9 | Define unique booking ID conventions that identify agent-initiated transactions | Medium |
| 10 | Assign ownership: who monitors API performance, updates the manifest, and tracks ABP | Low |
Key Terms
| Term | Definition |
|---|---|
| Agent Preference Optimization (APO) | The practice of structuring your brand’s digital presence so AI agents consistently evaluate, select, and act on your behalf over competing options. Coined by Booked Solid Digital. |
| Agentic Brand Preference (ABP) | The measurable outcome of APO. The degree to which AI agents prefer and select your brand across agent-driven queries in your service category. |
| Agent Share of Model (ASoM) | The percentage of agent-driven queries for your service category that result in your brand being evaluated as a candidate. |
| Execution Rate | The percentage of agent evaluations that result in a completed transaction through your API. |
| Agent Manifest | A machine-readable file at /agent-manifest.json that declares your brand’s agent-accessible capabilities and API endpoints. |
| potentialAction | A Schema.org property that links a service to an executable API endpoint, such as a BookAction or BuyAction. |
| Agentic SEO | The broader discipline of optimizing your digital presence for agent-driven discovery and task execution. APO operates within the Agentic SEO framework. |
Your Questions, Answered by Zoltan
Is APO only relevant for businesses with booking or transaction capabilities?
No. Research agents evaluate informational businesses too, and their selection criteria still include entity clarity, structured data quality, and content reliability signals. Every business benefits from the entity clarity and Schema foundations of APO, even without a booking API.
How do I know if agents are already evaluating my brand?
Check your server logs for user-agents from GPTBot, ClaudeBot, PerplexityBot, and similar identifiers. Watch for spikes in API traffic that don’t correlate with human session volume. If you’re seeing these without a manifest or structured capability data in place, you’re being evaluated and probably losing to competitors who have better signals.
Does APO replace SEO and GEO?
No. Human-initiated search still drives high volumes of traffic and will for years. SEO and GEO remain relevant for that channel. APO addresses a separate and growing channel: agent-driven transactions where no human ever touches a search results page. You need all three.
What’s the single highest-impact action most businesses can take right now?
Implement Service Schema with potentialAction and areaServed. This is the signal agents use first to determine whether your brand is even a candidate for a given task. Without it, you don’t make the evaluation shortlist regardless of what else you’ve done.
How long does it take to see measurable ABP improvement?
Based on client data at Booked Solid Digital, businesses that implement the full foundation (manifest, APIs, Schema) typically see measurable ASoM growth within three to six months as major agent platforms discover and index their capability signals. Execution Rate improvements follow as API reliability matures.
Let Booked Solid Digital Build Your Agent Preference Optimization Strategy
APO requires a specific blend of technical architecture, structured data expertise, and brand strategy. Most SEO and digital marketing agencies are not equipped to build it because it sits outside the traditional SEO skillset.
Our team, led by Zoltan Bedocs, has been building agent-ready digital infrastructure since before the discipline had a name. We developed the APO framework, coined Agentic Brand Preference as a measurable outcome, and track ASoM for clients across multiple service categories.
If your competitors are still optimizing for humans alone while agents increasingly make the buying decisions, the gap between you and them is widening every month.
Contact Booked Solid Digital today and start building the brand preference that the agent layer recognizes.
Sources
- Acar, Oguz A. and David A. Schweidel. “Preparing Your Brand for Agentic AI.” Harvard Business Review, March-April 2026. https://hbr.org/2026/03/preparing-your-brand-for-agentic-ai
- Booked Solid Digital. “Share of Model (SoM).” 2026. bookedsoliddigital.com/share-of-model-som
- OpenAPI Initiative. “OpenAPI Specification.” 2024. spec.openapis.org/oas/latest.html
- Schema.org. “potentialAction.” n.d. Retrieved April 2026. schema.org/potentialAction
