30 listopada, 2025
Headlines

Answer Engines & Agentic Commerce 2026

Answer Engines & Agentic Commerce: The 2026 Playbook for AI-First SEO

How to win visibility in AI Overviews, ChatGPT, and shopping agents with AEO, GEO & E-E-A-T

Tagline (for cover / promo):
How to make your brand the default source for AI answers – and the preferred choice for AI shopping agents.

Front Matter

1. Foreword (optional, 1–2 pages)

Content to cover:

  • Short endorsement from a recognized SEO / AI / ecommerce practitioner.
  • Why this shift from classic SEO to AEO/GEO and Agentic Commerce is real, not hype.
  • One concrete story: a brand that lost or gained visibility when AI Overviews and LLM search arrived.

2. Introduction (3–4 pages)

Section 2.1 – Why this book now

  • The collapse of “10 blue links” as the main discovery model.
  • How AI Overviews, Gemini, Perplexity, Copilot, ChatGPT, etc. changed user behavior.
  • The concept of “answer visibility” vs traditional rankings.
  • How Agentic Commerce pushes this further: from answers to actions.

Section 2.2 – Who this guide is for

  • SEO leads and digital marketers in mid-size and enterprise companies.
  • Ecommerce leaders and product managers (DTC, B2B, marketplaces).
  • Technical marketers and growth teams building AI-ready content and catalogs.
  • What level of knowledge is assumed (basic SEO, basic API/cloud familiarity).

Section 2.3 – How to use this guide (60-page playbook)

  • How chapters are structured (concept → checklist → examples).
  • Symbols / callouts to use later (e.g. “Quick win”, “Technical deep dive”, “Agent-ready pattern”).
  • Suggested reading paths:
    • Non-technical marketing lead.
    • Technical SEO / data engineer.
    • Ecommerce leader or founder.

Section 2.4 – What you’ll be able to do after reading

  • Audit your current AEO/GEO readiness.
  • Design answer-ready content for AI-driven search.
  • Track AI visibility and citations.
  • Make a product catalog and checkout “agent-ready”.
  • Build a 90-day implementation plan for your organization.

Chapter 1 – The New Search & Commerce Stack (5–7 pages)

1.1 – From SERPs to answer engines

  • Short history: classic SEO → featured snippets → AI Overviews / AI Mode.
  • Key actors: Google, Microsoft, OpenAI, Perplexity, Meta, Apple, Amazon.
  • What “answer engines” are and how they differ from search engines.
  • Why “zero-click” is now often “zero-page” for many queries.

1.2 – AEO vs GEO vs classic SEO

  • Clear definitions:
    • AEO – Answer Engine Optimization (direct answers).
    • GEO – Generative Engine Optimization (LLM-first visibility).
    • AIO – AI Overviews / AI Search Optimization (Google-specific flavor).
  • How these build on (not replace) technical SEO and content SEO.
  • Mental model: “pages as interfaces” vs “pages as articles”.

1.3 – Enter Agentic Commerce

  • What Agentic Commerce is (agents executing tasks end-to-end).
  • Examples: booking travel, ordering refill products, choosing SaaS plans via AI agents.
  • Why this matters to SEO and marketing (agents pick suppliers, not just links).
  • The relationship: Classic SEO → AEO/GEO → Agentic Commerce.

1.4 – Core concepts you’ll see throughout the book

  • E-E-A-T 2.0 and “Author Proof”.
  • Content as data, not text.
  • Entities and knowledge graphs.
  • Agents, tools, APIs, and “knowledge sources”.
  • Agent-readiness vs human-readiness.

Chapter 2 – E-E-A-T 2.0 and “Author Proof” (6–7 pages)

2.1 – Why expertise suddenly matters more

  • Why answer engines are more sensitive to quality and risk than classic SERPs.
  • How LLMs lean on trusted sources to reduce hallucinations.
  • The “first-hand experience” factor (the first E).

2.2 – Building Author Proof for your experts

  • Defining “Author Proof”:
    • Rich author bios, photos, credentials.
    • Traceable history of publications, talks, contributions.
  • Where to surface Author Proof:
    • Author pages.
    • Article bylines and footers.
    • About / Team pages.
  • Checklist for author profiles:
    • Required fields (role, expertise, markets, years of experience).
    • External signals (LinkedIn, conferences, associations).

2.3 – Organization-level trust signals

  • Brand-level E-E-A-T:
    • Company story, size, markets.
    • Third-party reviews, certifications, awards.
  • Site-wide trust design:
    • Clear contact info, policies, and legal pages.
    • Distinguishing editorial vs sponsored content.
  • How these signals feed into AI engines’ notion of “source reliability”.

2.4 – Original data and “information gain”

  • Why generic summaries are dead in AEO/GEO.
  • Types of original data:
    • Benchmarks, lab tests, surveys, proprietary usage data.
  • How to present original data:
    • Charts, tables, key findings boxes.
    • Explicit methodology sections (“How we measured this”).
  • A repeatable process for generating small, ongoing proprietary data pieces.

2.5 – Building an E-E-A-T / Author Proof roadmap

  • 30-day quick wins (fill obvious gaps).
  • 90-day deeper initiatives (expert publishing, PR, collaborations).
  • How to prioritize by revenue-driving topics and pages.

Chapter 3 – Content as Data: Designing Answer-Ready Pages (7–8 pages)

3.1 – Thinking like an answer engine

  • How an LLM “sees” a page: tokens, headings, structure.
  • Why a clean hierarchy beats long walls of text.
  • Extractable units: short answers, bullet points, tables.

3.2 – The answer-first page pattern

  • The “short answer box” near the top: what it looks like in practice.
  • How to write 2–3 sentence summaries that answer a query directly.
  • Examples:
    • Definition page.
    • Comparison page.
    • “How it works” explainer.

3.3 – Q&A-driven information architecture

  • Building your content around questions:
    • How to mine user questions (tools, support tickets, sales calls).
    • Mapping questions to search intent (informational vs commercial vs transactional).
  • Structuring articles as sequences of H2/H3 questions.
  • When to create a separate page vs a section on the same page.

3.4 – Using lists, tables, and schemas of information

  • Best practices for bullet lists:
    • One fact per bullet.
    • Hierarchical lists (main points vs nested detail).
  • Designing comparison tables:
    • Columns for models/plans, rows for features and metrics.
    • How to keep tables simple enough for machines and humans.
  • “Parameter blocks”:
    • Sections like “Inputs”, “Outputs”, “Use this when”.

3.5 – Content types that win in answer engines

  • Playbook for:
    • Definitions / glossaries.
    • How-to guides and checklists.
    • Buyer’s guides and decision frameworks.
    • Troubleshooting / FAQs.
  • When to use long-form vs short, focused “micro-answers”.

3.6 – Templates and checklist

  • Template 1: Answer-ready explainer.
  • Template 2: Answer-ready buyer’s guide.
  • Template 3: FAQ/How-to page.
  • End-of-chapter checklist: Is this page answer-ready?

Chapter 4 – Schema, Knowledge Graphs & Technical Foundations (7–8 pages)

4.1 – Schema 101 for answer engines

  • What structured data is and how LLMs use it.
  • JSON-LD vs microdata vs RDFa (short, non-technical explanation).
  • Priority schema types for AEO/GEO:
    • Article, BlogPosting, FAQPage, HowTo, Product, Organization, Person, LocalBusiness, BreadcrumbList.

4.2 – Implementing schema at scale

  • Where to start:
    • Key content hubs and revenue pages.
  • How to generate and maintain schema:
    • Manual vs plugin vs specialized tools.
  • Governance:
    • Version control, QA, and monitoring.

4.3 – Knowledge graphs in practice (without heavy jargon)

  • The idea: connecting entities (people, brands, products, use cases).
  • Internal knowledge graph:
    • How to represent your own domain’s “names and relationships”.
  • Benefits:
    • Stronger entity recognition by search and AI.
    • Easier reuse of your content in AI answers.

4.4 – Technical SEO as the plumbing layer

  • Speed and Core Web Vitals:
    • Why performance still matters for AI visibility.
  • Crawlability and indexation:
    • Sitemaps, robots.txt, clean URL structures.
  • Page templates:
    • Ensuring all templates are structured in an answer-friendly way.

4.5 – AI crawlers, robots.txt and llms.txt

  • Overview of major AI crawlers (Google-Extended, GPTBot, etc.).
  • Practical robots.txt patterns:
    • Allowing / disallowing AI crawlers selectively.
  • What llms.txt is and isn’t (today):
    • Example of a simple llms.txt file.
    • Where it fits in your policy stack (legal, privacy, training consent).

4.6 – Technical audit checklist

  • One-page checklist to audit:
    • Schema coverage.
    • Crawlability and indexation.
    • AI crawler controls.
    • Performance on top AEO/GEO pages.

Chapter 5 – Measuring & Improving AI Visibility (6–7 pages)

5.1 – New KPIs for AEO & GEO

  • Shifting from “ranking for keyword X” to:
    • AI answer inclusion.
    • AI share of voice for key entities/topics.
    • AI citation coverage vs competitors.
  • How to build these metrics from available tools.

5.2 – Tracking AI Overviews / AI Mode

  • How to identify queries that trigger AI Overviews.
  • Reading and interpreting external tracker data.
  • Building a “keywords by AI Overview impact” view.

5.3 – Tracking LLM citations and brand mentions

  • Using LLM visibility tools to see:
    • Where your brand is mentioned.
    • Which URLs are cited.
  • Qualitative analysis:
    • Are you framed accurately?
    • Are your competitors framed more positively?

5.4 – AI Citation Auditing in practice

  • Step-by-step for an AI citation audit:
    • Select a topic cluster.
    • Collect AI answers from multiple platforms.
    • Identify missing or misattributed citations.
    • Design interventions (content, schema, PR).

5.5 – Reporting & communicating results internally

  • Simple dashboards your leadership will understand.
  • How to tie AEO/GEO metrics to revenue or lead indicators.
  • Setting realistic expectations and timelines.

Chapter 6 – Making Your Business Agent-Ready (7–8 pages)

6.1 – What “agent-ready” really means

  • Differences between human-ready and agent-ready experiences.
  • Agents need:
    • Clean data (products, services, pricing, availability).
    • Clear rules (policies, constraints).
    • Reliable actions (APIs, forms, checkouts).

6.2 – Product & service data for agents

  • Catalog data model basics:
    • Minimal fields an agent must know (name, SKU, specs, price, etc.).
  • Compatibility and constraints:
    • How to express “works with / doesn’t work with” relationships.
  • Keeping data fresh:
    • Stock, lead times, dynamic pricing.

6.3 – Knowledge sources & RAG for commerce

  • What a “knowledge source” is in a RAG / agent setup.
  • Turning:
    • Help docs, FAQs, and manuals
    • Policies and contract terms
    • Product guides
      into searchable, reliable knowledge sources.
  • How this increases the odds that your own data is used instead of random blog posts.

6.4 – APIs, tools & agent workflows

  • Core building blocks:
    • Product lookup API.
    • Cart / quote API.
    • Booking / checkout API.
  • Defining safe actions:
    • Which actions agents are allowed to trigger (add to cart, place order, schedule demo).
  • Logging and audit trails:
    • Why legal, security, and compliance need this.

6.5 – Policies, trust & risk management

  • Machine-readable policies for:
    • Shipping.
    • Returns and refunds.
    • Warranties and guarantees.
  • Consent and privacy:
    • How to express what data agents can access.
  • Internal risk matrix:
    • Low-, medium-, high-risk actions agents can perform.

6.6 – Agent-readiness checklist

  • Single-page checklist for:
    • Data readiness.
    • API readiness.
    • Policy and compliance readiness.

Chapter 7 – Use Cases & Mini Playbooks (6–7 pages)

7.1 – Ecommerce / DTC

  • AEO examples:
    • Product detail pages with answer-ready sections.
    • Category pages as buying guides.
  • Agentic patterns:
    • Auto-reorders.
    • Bundling recommendations made directly by agents.
  • Practical mini-playbook:
    • 5 specific improvements for a DTC store.

7.2 – B2B SaaS

  • AEO examples:
    • Feature explainers and implementation guides.
    • “Who is this for / not for?” sections.
  • Agentic patterns:
    • Agents scheduling demos and trials.
    • Agents comparing plans and provisioning test accounts.
  • Mini-playbook:
    • Optimizing docs and pricing for agents.

7.3 – Local services (US context)

  • AEO examples:
    • Service-area pages with FAQ clusters.
    • Clear pricing ranges and process explanations.
  • Agentic patterns:
    • Automated booking (plumber, dentist, HVAC).
    • Agents handling rescheduling and cancellations.
  • Mini-playbook:
    • Local provider going from “invisible to agents” to “preferred option”.

7.4 – B2B industrial / complex products

  • AEO examples:
    • Detailed spec sheets with answer-ready summaries.
    • Application notes for specific industries.
  • Agentic patterns:
    • Agents configuring bill of materials or proposals.
    • Agents pre-filling RFQs with the right configurations.
  • Mini-playbook:
    • Using AEO/GEO to own a technical niche.

Chapter 8 – 90-Day Roadmap & Beyond (5–6 pages)

8.1 – 30-day audit and quick wins

  • What to audit:
    • E-E-A-T signals and Author Proof.
    • Key answer hubs and schemas.
    • Current AI Overview and LLM visibility (baseline).
  • Quick wins:
    • Add short answer boxes to top pages.
    • Add basic schema where missing.
    • Clean up author pages and bios.

8.2 – Days 31–60: Build your answer hubs

  • Selecting 2–3 priority topic clusters.
  • Designing or redesigning pages in answer-first format.
  • Creating at least one “original data” asset per cluster.
  • Implementing schema and internal linking.

8.3 – Days 61–90: Agent-readiness foundation

  • Inventory product / service data gaps.
  • Define a minimal commerce API (even if it starts as forms).
  • Make policies explicit and structured.
  • Draft your first “agent brief”:
    • What the agent should do.
    • What knowledge and tools it has.
    • What constraints it must follow.

8.4 – Building long-term capabilities

  • Roles you’ll need in your team:
    • AEO/GEO strategist.
    • Content engineer.
    • Agentic architect / technical marketer.
  • Setting a cadence:
    • Quarterly AI visibility reviews.
    • Annual overhauls for top converting clusters.
  • Staying current:
    • How to monitor platform changes without chasing every shiny object.

8.5 – Final thoughts

  • Reframing SEO from “gaming algorithms” to “designing for intelligent systems”.
  • Why the brands that win will:
    • Ship better data, not only better copy.
    • Treat agents as real users.
    • Invest in trust, structure, and actionability.

Back Matter

Appendix A – Checklists & Templates

  • AEO page checklist.
  • Schema implementation checklist.
  • Agent-readiness checklist.
  • 90-day roadmap template (one-page).

Appendix B – Glossary of Key Terms

  • Short, practitioner-friendly definitions:
    • AEO, GEO, AI Overview, Answer Engine, Agent, RAG, Knowledge Source, etc.

Appendix C – Recommended Tools & Further Reading

  • Short, non-promotional list of tools and key industry reports, updated for 2025–2026.

Meta (for Amazon KDP listing)

KDP title:
From Clicks to Agents: A Practical Field Guide to Answer Engine Optimization, Generative Engine Optimization & Agentic Commerce

KDP subtitle:
How to make your brand the default source for AI answers – and the preferred choice for AI shopping agents in the US market.

Short book description (back cover / KDP):
Search has changed. AI Overviews, ChatGPT, Gemini, Perplexity and other answer engines now sit between your customers and your website. At the same time, AI shopping agents are starting to research, compare and buy on behalf of users.

From Clicks to Agents is a practical 60-page field guide for SEO leaders, marketers and ecommerce teams who want to stay visible – and become agent-ready. You’ll learn how to apply Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), E-E-A-T 2.0, structured data, knowledge graphs and agentic commerce patterns to real-world websites and catalogs.

Use this book to audit your current setup, build answer-ready content, track AI visibility, and design data and APIs that AI agents can safely act on. It’s not theory – it’s a step-by-step playbook for the next era of search and commerce.

KDP keywords (examples):

ecommerce SEO AI

answer engine optimization

generative engine optimization

AI SEO 2025

AI Overviews optimization

agentic commerce

AI shopping agents

E-E-A-T

structured data SEO

knowledge graph SEO