AI Search Optimization & Agentic SEO
Search agents now browse, decide, and act on your behalf — decomposing tasks, choosing sources, and completing steps without a person clicking a link. Learn to make your brand findable, trustworthy, and actionable to the agents mediating discovery, not just the people behind them.
Join marketers & SEO teams worldwide learning with MLforSEO
You’re optimizing for rankings. Agents don’t rank — they act.
Traditional SEO assumes a person types a query, scans a list, and clicks a page. Agentic search breaks every step of that assumption.
An agent decomposes the task, fans out queries, retrieves fragments from many sources, and — increasingly — takes action: comparing, deciding, even transacting, often with no page view anywhere in sight.
You can hold position one and still never be retrieved, cited, or chosen. Visibility no longer guarantees inclusion, inclusion no longer guarantees use, and being read no longer guarantees being acted on.
The work now is being legible and actionable to the systems that mediate discovery — structured enough to be understood, modular enough to be reused, and trustworthy enough to be picked.
This course is built around one question: when an agent — not a person — is deciding and acting, what makes your brand the one it reaches for?
What you’ll take away
Six practical capabilities that move you from ranking-centric SEO to optimizing for agents that browse, decide, and act.
How agentic search actually works
Queries vs. tasks vs. objectives, query fan-out, and why agents evaluate content outside page-level context.
Intelligent agents in SEO workflows
What qualifies as an agent, how memory, tools and feedback loops shape outcomes, and where agents bypass retrieval.
What agentic SEO means in practice
How optimization targets shift from pages and rankings to being retrieved, chosen, and acted on.
Knowledge graphs & structured data for agents
How entities differ from documents, and how structured relationships reduce ambiguity so agents can reason and reuse.
Your audience is no longer only human
The three agent modes, building a non-human ICP, and structured data as a machine-readable contract that lets agents act.
Measuring influence, not just traffic
Read the alternative signals of presence and influence in agent-mediated search when rankings and clicks fall short.
Not another “AI SEO” overview — a build-it-yourself system
Four things set this course apart from the tips-and-tricks content flooding your feed.
Optimize for the systems, not the SERP
Most SEO training ends at the ranked list. This one starts where discovery actually happens now — inside retrieval, decision, and action — so agents can find, trust, and act on your content.
Agent-mediated discovery & actionPractical exercises & a workbook on most lessons
Most lessons pair the concept with a hands-on exercise and a downloadable workbook — audits, roadmaps, and action sheets you run on your own site. You leave with a toolkit, not just notes.
Do the work, not just watchGrounded in real agentic workflows
No toy examples. Knowledge-graph-aware and schema-first, with contextual examples worked end to end — the same messy conditions you’ll face on a live site.
Real data & worked examplesMeasure influence, not just traffic
Learn the alternative signals of presence and influence in agent-mediated search, so you can prove impact when rankings and clicks stop telling the whole story.
Signals beyond rankings & clicksWhat’s included
A systems-focused path from what agentic search is, through the technical foundations agents rely on, to advanced agentic SEO strategy and how to keep up as the landscape shifts.
›Lesson 0.1 — Course Introduction & What We’ll Cover ✦ Free sneak peek before you buy
- What “agentic search” means in this course, and what is explicitly out of scope
- The search systems, agents, and decision processes the course focuses on
- What you’ll be able to do differently by the end
›Lesson 0.2 — Why Traditional SEO Isn’t Enough
- The limits of ranking-centric thinking
- Why visibility does not guarantee inclusion or use
- How generated answers and agent-mediated discovery move where value is created
- Which assumptions from traditional SEO no longer hold
›Lesson 1.1 — How “Modern Search” Works
- The difference between queries, tasks, and underlying objectives
- How systems break complex requests into smaller steps (query fan-out)
- Where information is retrieved, filtered, and combined
- Why content is often evaluated outside of page-level contexts
›Lesson 1.2 — Intelligent Agents in SEO Workflows
- What qualifies a system as an agent in practical terms
- How agents retrieve information, evaluate sources, and combine inputs
- How memory, tools, and feedback loops influence outcomes
- Where agents replace or bypass traditional retrieval paths
›Lesson 1.3 — What Agentic Search Means in Practice
- How agent-mediated systems differ from assisted retrieval
- What changes when autonomy and delegation are introduced
- Why optimisation targets move away from pages and rankings
- What “being useful” means in agent-driven contexts
›Lesson 2.1 — Knowledge Graphs for Intelligent Agents
- How entities differ from documents
- The role of relationships, attributes, and context
- Why structured relationships reduce ambiguity
- How agents use these structures to reason across sources
›Lesson 2.2 — Language Model Visibility Fundamentals
- How information is retrieved and prioritised
- The difference between ranking and reuse
- What increases or reduces citation likelihood
- Common reasons information is ignored or misused
›Lesson 2.3 — Structured Data for Automated Systems
- Why consistency matters more than completeness
- How structured formats support interpretation
- Where structured data helps and where it does not
- Typical implementation mistakes that reduce clarity
›Lesson 2.4 — Trust, Authority, and Machine Confidence
- How credibility is inferred rather than declared
- Signals that reduce ambiguity and contradiction
- Why authority is contextual and situational
- How inconsistency erodes confidence
›Lesson 3.1 — Designing Content for Retrieval and Synthesis
- Content modularity and atomicity
- Designing for extraction and recombination
- Supporting partial reuse without loss of meaning
- Why long-form defaults often fail
›Lesson 3.2 — Measuring Performance in Agent-Mediated Search
- Why rankings and traffic are incomplete indicators
- Alternative signals of influence and presence
- Qualitative versus quantitative assessment
- How to interpret indirect visibility
›Lesson 3.3 — How Systems Narrow Choices
- Early filtering versus late evaluation
- Decision decomposition in complex tasks
- Where most content is discarded
- Implications for optimisation priorities
›Lesson 3.4 — Your Audience Is No Longer Only Human
- The three agent modes and what each one needs from you
- The non-human ICP — and how to build one
- Structured data as a machine-readable contract
- The action layer: from being read to being transacted
›Lesson 4.0 — Course Takeaways
- What stays stable across systems, model generations, and interface changes
- A four-question filter for evaluating any new AI-search tool or claim
- Telling interface changes (product cadence) apart from architectural ones that actually matter
- Decision rules for optimisation choices as the developments space keeps shifting
Every lesson ships with its own downloadable workbook — audits, roadmaps, and action sheets you run on your own site.
Not sure yet? Watch the intro lesson — free
The opening lesson, Course Introduction & What We’ll Cover, is open to everyone. Watch it before you enrol to see exactly how the course works and whether it’s the right fit for you.
Watch the free intro lesson →Is this course a good fit for you?
- You already do SEO and can feel the ground shifting under ranking-based thinking.
- You want to understand how AI-search agents retrieve, decide, and act — not just prompt them.
- You want practical, workbook-driven workflows over theory.
- You’re ready to optimize for entities, retrieval, reuse, and action instead of positions.
- You want to measure influence when rankings and traffic stop telling the whole story.
- You’re looking for a prompt-engineering or “ChatGPT hacks” course.
- You believe traditional ranking tactics are all you’ll ever need.
- You want a purely conceptual overview with no hands-on work.
- You’re brand new to SEO and want a beginner primer before anything agentic.
Built for the people optimizing for what comes after search
Whatever your role, you’ll learn the mechanisms of retrieval and synthesis in modern AI search — and how to tell what’s real from the hype — then apply it to your work.
In-house marketers & SEOs
You own organic growth and can feel ranking-based playbooks losing traction. You’ll learn the mechanisms of retrieval and synthesis behind modern AI search — how agents actually find, judge, and reuse content — make your brand retrievable, trustworthy, and selectable, and measure it when traffic stops telling the whole story.
Content writers & editors
You write the pages agents read. You’ll learn to structure content for extraction and reuse — atomic claims, inline attribution, self-contained sections — so your work survives chunking and gets cited instead of skipped.
Agency marketers & consultants
You deliver SEO across many clients. You’ll get repeatable, workbook-driven workflows — audits, roadmaps, and checklists — you can run across a portfolio and package into a productised agentic-SEO service.
Agency leaders & heads of SEO
You set strategy and pitch what’s next. You’ll learn to tell real signal from hype — evaluating new AI-search tools and claims on how they actually work — brief your team on what moves selection, and position your agency ahead of the shift to agent-mediated search.
What you’ll walk away with
Not just lessons — a library of workbooks, audits, and roadmaps you download and run on your own site.
30-Day Agent Search Optimization Action PlanXLSX
Diagnose and fix your site over 30 days — content audit, entity fixing, extractability, and testing.
Agent SEO Implementation RoadmapXLSX
Sequence the agent-readable infrastructure work — robots.txt, llms.txt, skill.md, and page restructuring.
Language Model Visibility: 4-Week RoadmapXLSX
A time-boxed plan to lift your citation rate across ChatGPT, Claude, Perplexity, and Gemini.
Entity Confidence Recovery TrackerXLSX
Rebuild machine confidence after contradictions, a rebrand, or a migration — in two tracked phases.
Extraction Readiness AuditDOCX
Five checks, under ten minutes, to tell whether a page is actually extraction-ready for AI search.
7 Alternative Signals of AI InfluenceDOCX
Surface your AI visibility weeks before it shows up in traffic — seven proxy signals with exact steps.
Late-Evaluation Rewrite ChecklistXLSX
Rewrite content so chunks survive the generator’s final read — four tests, section by section.
Every workbook is published in the MLforSEO resource library and unlocked with the course. More audits, roadmaps, and action sheets are added as the course grows — plus lifetime access to all materials and a certificate of completion.
Built by someone defining entity SEO and semantic search.
Expert in semantic technologies and the future of search. Helps businesses navigate the transition from traditional SEO to agent-driven discovery.
Beatrice leads the development of knowledge graph solutions that make content accessible to intelligent agents and large language models. Her work sits at the intersection of SEO, semantic web technologies, and digital transformation — helping organizations build sustainable competitive advantages as search becomes increasingly dialogical, personalized, and agent-mediated.
A recognized thought leader in semantic SEO, she’s worked with Fortune 500 companies across industries and currently serves as Head of Innovation at WordLift.
Key dates & pricing
*VAT is added to the price for EU individuals.
Enrol now
The full course is unlocked and available now. Lock in the reduced €350 price before it rises to €400 on 27 July.
Enrol now →Already learning with MLforSEO? Use code Community30 at checkout for 30% off — €245 while the early-bird promotion is live (until 27 July), then €280. A little thank-you for choosing our platform.
Frequently asked questions
It’s about agentic SEO: optimizing for AI-search agents that decompose tasks, retrieve fragments, decide, and increasingly act — not just for a human scanning a ranked list. By the end you’ll be able to audit your site for agent-readiness, design content for retrieval, reuse, and action, and measure presence in agent-mediated search rather than relying on rankings alone.
Beatrice Gamba, Head of Innovation at WordLift and a recognized thought leader in semantic and entity SEO. The course combines strategy with hands-on, workbook-driven workflows tailored for SEO, content, and brand teams.
Traditional SEO optimizes for a ranked list a person scans and clicks. AI search adds systems that synthesize answers. Agentic search goes one step further: agents break a task into sub-questions, choose sources, and often complete the action themselves — comparing, deciding, even transacting. Optimization shifts from ranking pages to being retrievable, trustworthy, and actionable to those agents.
No prerequisites are required. That said, having taken AI Search & LLMs: Entity SEO and Knowledge Graph Strategies for Brands is advantageous — it helps you set up your website’s content and schema architecture and internal knowledge graph, which this course then builds on for agentic optimization.
No advanced background is required. Implementation stays practical, with clear examples and a downloadable workbook per lesson. Existing SEO experience helps, since the course builds on concepts like entities, structured data, and retrieval.
It’s on-demand and self-paced across 5 modules and 15 focused lessons, so there’s no fixed schedule and no time limit for completion. The total video length is around 6 hours, with lessons averaging 20–35 minutes each. Most lessons also include additional practical exercises to complete beyond the guided videos and the workbook, so we estimate that working through everything — all workbooks and exercises included — takes roughly 35–50 hours in total. You work through it all at your own pace.
The full course is live now — when you enrol, all materials unlock immediately. The reduced €350 price is available for a limited time until 27 July, after which the standard €400 price applies. Once you enrol, access is lifetime, including future updates.
€350 for a limited time (until 27 July), then €400 standard. Past MLforSEO students can use the community code for 30% off. VAT is added for EU individuals and calculated at checkout.
Yes. Enter your billing details (including VAT/Tax ID where applicable) at checkout and an invoice is issued automatically. For multiple seats or centralised invoicing, reply to your order email and we’ll set up bulk checkout.
Yes. MLforSEO Academy issues a certificate of completion for each course you finish, which you can share on LinkedIn and professional profiles.
Most lessons ship with a downloadable workbook — audits, roadmaps, trackers, and checklists you run on your own site. They’re published in the MLforSEO resource library and unlocked with the course, and more are added as it grows. You keep them for good.
Most lessons include a practical exercise and contextual, worked examples, so you apply each concept as you go rather than just watching. You leave with a toolkit and a set of completed audits and plans for your own site, not just notes.
Yes. Use the community code Community30 at checkout for 30% off — that’s €245 while the reduced price is live (until 27 July), then €280. It’s a thank-you for learning with us.
Because the full course and all downloadable materials unlock immediately on purchase, sales are final. If something isn’t working or you were charged in error, reply to your order email and we’ll make it right.