Can AI agents really deliver for SEO and AEO? 5 concrete use cases

Everyone uses conversational AI. Open ChatGPT or Claude, type a prompt, read the answer, iterate. It works, but it's manual work with clear limits: one human behind a screen, one conversation at a time, endless copy-pasting between tools.

Agentic AI is something else entirely. You don't talk to an AI anymore: you build a system where multiple agents read your data, execute tasks, call external tools and produce deliverables. In parallel, continuously, without friction.

In a live session co-hosted with Digidop, Thibaut Legrand (Vydera co-founder) and Florian Bodelot (Digidop co-founder) ran live demos of 5 agentic workflows we use daily at Vydera for SEO and AEO. This article revisits the key points of the live, with deeper coverage of the method and the stack.

Why agentic changes the game for SEO and AEO

SEO and AEO have always operated at two speeds: a lot of repetitive work (auditing, tracking positions, checking tags, writing variants, updating content), and a few high-impact strategic decisions (positioning, editorial priorities, cluster architecture).

Conversational AI sped up some of the repetitive work. Agentic AI lets you delegate it entirely to automated systems, keeping humans focused on strategy. This isn't a gadget: it's a shift in how production actually happens.

In practice, it means a technical audit that used to take half a day now takes 13 minutes. A keyword research cross-referenced with Search Console runs while another agent tracks your visibility on ChatGPT, and a third drafts an article in your CMS. All of it, without you sitting in front of a screen.

For context on what's shifting on the search engine side, we break it down in SEO vs AEO: what actually changes.

The stack we use at Vydera

Before talking workflows, you need to understand the toolbox. Agentic AI isn't a magic web interface: it's a stack of building blocks you assemble.

Claude Code

Instead of chatting with Claude in a browser, we work with Claude Code, which lives in your computer's terminal. It can read and write local files, execute code, call external tools. Think of it as having a consultant permanently available, with access to your work environment.

Moving from browser to terminal can feel intimidating. But once you've made the leap, the gain is massive: Claude Code is no longer limited to what you paste into a chat. It can act on your project autonomously.

The CLAUDE.md file: your project's memory

First essential block: a CLAUDE.md file at the project root. It's a simple markdown file describing who you are, what your company does, the conventions to follow, the tools available, the deliverables expected.

Claude reads this file at the start of every conversation. You don't have to re-explain your context with every prompt. Two benefits: results are more consistent across sessions, and you consume fewer tokens since Claude doesn't have to rebuild context each time.

Skills: specialized expertise

A skill is a markdown file that encodes a specific expertise. A geo-audit skill, for example, describes how to analyze a page for its AI citability, which structured data to verify, what deliverable format to produce.

When you invoke that skill with a command like /geo-audit https://example.com, Claude follows the skill's instructions rather than answering generically. This is where human expertise comes in: the quality of the skills you write determines the quality of the output. Skill libraries exist on GitHub, but for specific cases, you have to build them yourself.

MCPs: connecting Claude to external tools

MCPs (Model Context Protocol) let Claude talk directly to third-party services. For SEO and AEO, we mainly use four:

• Firecrawl: to scrape web pages and retrieve their content
• DataForSEO: for search volumes, keyword difficulty, SERP data
• Google Search Console: for actual impressions, clicks and positions on the site
• Webflow: to read and write directly in the CMS

Going through APIs via MCP costs a fraction of traditional SEO SaaS. That's one reason this model is disrupting the sector's economics. We cover this in more depth in our article on AEO tools to track your AI visibility.

The 5 use cases demoed live

1. Full technical SEO and AEO audit

The goal: from a single URL, produce a structured diagnosis of a site's SEO and AEO health.

How it runs: the main agent (an orchestrator) receives the /geo-audit command with a URL. It delegates to specialized sub-agents: geo-ai-visibility, geo-platform-analysis, geo-technical, geo-content, geo-schema. Each sub-agent examines one dimension and returns its data. The orchestrator aggregates, prioritizes and produces an HTML report.

What we saw live on webflow-formation.fr: an overall score by category, detection of a lang="en" attribute on a French-language site, missing llms.txt, incomplete structured data, H1 hierarchy issues on certain pages. Processing time: 13 minutes. Token consumption: 58,000 tokens.

The takeaway: a thorough technical audit, delivered with a prioritized action plan, without human supervision during execution. Humans step in upstream (skill design, criteria selection) and downstream (validation and execution of fixes).

2. Cross-referenced keyword research with DataForSEO and Search Console

The goal: identify keywords to push, gaps to fill and positions to defend, by cross-referencing market data with your actual performance.

How it runs: you give the agent a URL and a topic. It queries DataForSEO to get candidate keywords with volumes and keyword difficulty. It cross-references with the site's Search Console data (impressions, clicks, average positions). It produces a structured deliverable: quick wins, gaps, positions to defend.

What we saw: a report identifying specific opportunities (for example, a keyword with 90 monthly searches and low keyword difficulty), queries where the site was averaging position 1.4 (worth protecting), and whole topic clusters with no coverage (worth building).

The takeaway: the real value isn't in the raw data, but in the cross-reference between market demand and real performance. A high-volume keyword already generating impressions without clicks is a different problem than a high-volume keyword where you're absent entirely. The agent does that cross-reference automatically.

3. AI visibility tracking (GEO/AEO)

The goal: measure a brand's presence in generative AI responses, across a representative set of prompts.

How it runs: you give the agent a topic and the brands to track (yours and your competitors). It automatically generates 10 realistic prompts, sends them to ChatGPT through the OpenAI API with web search enabled, then analyzes each response: is your brand cited? Which sources are used? Which web queries did the model run internally to build its answer?

What we saw: a prompt-by-prompt report with cited sources, detected query fan-out patterns (the sub-queries the AI ran internally), and a positioning summary. For more on how this works, our article on query fan-out explains how LLMs break down searches.

The takeaway: there's rarely one single culprit when your brand isn't cited. It's the combination of factors; topic coverage, content structure, presence on third-party sources; that determines your citability. The agent gives you the diagnosis; you pick the lever.

4. Writing a full article directly in Webflow

The goal: move from audit to execution. From a target keyword, write a complete article and create it as a draft in the Webflow CMS.

How it runs: the agent identifies the Webflow site, the Blog collection, pulls the sitemap to detect internal linking opportunities. It writes the article, creates the CMS item with all fields filled (title, slug, meta description, content, FAQ, structured data), and leaves it as a draft for human validation.

What we saw: an article written from scratch, automatically cross-linked with relevant existing pages, integrated directly into the CMS. The copy-paste step between writing tool and CMS is gone.

The takeaway: the agent does 80% of the work. The remaining 20%; editorial polish, factual validation, brand voice fit; stay human. That's the difference between an okay article and one that actually performs. Our article on optimizing content for AI citations covers this finishing layer.

5. Refreshing existing content

The goal: update an existing article (enrichment, adding FAQs, fixing structured data, adjusting internal linking) without breaking existing SEO.

How it runs: you give the URL of the article to refresh. The agent scrapes the content, identifies weaknesses (missing intro, no FAQ, non-citable data, thin internal linking), then updates the matching Webflow item while keeping the slug and structure intact.

The takeaway and what went wrong live: the first attempt created a duplicate rather than updating the existing article. A useful reminder that agentic AI makes mistakes, and that guardrails (human validation before publishing, precise skills, explicit commands) remain essential. A second iteration with an explicit instruction fixed the issue.

Lessons from the live

Parallel agents change what productivity looks like

Running three audits in parallel is possible. A technical audit, a keyword research, an AI visibility tracking can all run at the same time. You're no longer waiting: you choose where to focus your attention while the agents work.

Token management is the real challenge

At professional scale, even Claude Pro Max plans have limits. Several practices help: a well-written CLAUDE.md reduces context rebuilding; precise skills avoid detours; the /compact command summarizes long conversations; and one conversation per task, rather than chaining everything into the same chat.

Must-have vs nice-to-have

Agentic AI makes building easy. Too easy. It's tempting to build agents for everything, even for tasks you rarely run. Discipline means separating what actually changes how you work from what's just fun to build. The agents that matter are the ones you use several times a week.

Strategy stays human

This is the core of the Vydera method. AI is a workforce: it executes at scale. Human experts set the strategy, frame the skills, validate the deliverables. Without that framing, agents produce fast; but not necessarily well.

What this means for your brand

Three concrete implications.

Generalist SaaS are being challenged. Paying hundreds of dollars a month for a tool where you use only 20% of the features makes less sense when you can build a custom agent that does exactly what you need, for a fraction of the cost.

Execution becomes a differentiator again. Anyone can run an audit. Very few can publish 30 quality articles a month in two languages with clean internal linking and structured data. Agentic work makes that volume achievable.

The gap between experts and non-experts is widening. An SEO expert who spends three months framing their agents will outperform an SEO expert still working the old way. And they'll also outperform a non-expert setting up agents without real strategy.

That's exactly the model we run at Vydera: human-led strategy, AI workforce on execution, measurable results in 90 days. If you want to see what that looks like on your site, let's talk about your context.