Agentic SEO is a new term, and it does not have a settled definition yet. Semrush describes it as “handing a defined SEO workflow to an AI agent that executes it for you”. Siteimprove calls it “the use of proactive AI agents to continuously optimize digital content for discoverability”. Both point at the same shift: the agent does more than write text or list warnings. It gathers the data, follows a method, proposes or makes the change, and checks the result. This page gives a working definition, the setup that makes it repeatable, the jobs worth handing over first, and the decisions that should stay with a person.
A working definition
Take the marketing away and agentic SEO has three parts. An agent, meaning an AI model that can use tools rather than only answer, runs a search optimization workflow. The workflow is written down in advance, so it runs the same way each time; Semrush puts the weight on the word “same”, because one clever answer is not automation. And a person stays in charge of what gets published.
That rules out two things often sold under the same name. Asking a chatbot to write a blog post is AI-assisted writing: nothing is measured before, and nothing is checked after. An audit tool that crawls your site and returns two hundred warnings is not agentic either. It stops where the work starts.
What an agent does that a tool does not
- It gathers its own evidence: it crawls the pages, reads Search Console and opens the source files.
- It picks the next step inside the method, for example checking whether a page that lost clicks also lost its ranking, or only its click-through rate.
- It acts: it drafts the new title, fixes the canonical tag or adds the missing structured data, in a copy you review.
- It checks its own change before it reports back, and it says which findings it measured and which it only estimated.
Siteimprove frames the difference as timing: “Manual audits catch errors weeks or months after publishing.” An agent can look at every release instead of once a quarter.
The four parts of a setup that works
Semrush splits a working setup into four layers. We find the same split useful, in our own words:
- Context. What the site sells, who it is for, which pages matter and which queries you will not chase. Without it, the agent optimizes for traffic you cannot use.
- A written method. The rules you would give a new colleague. For example: a page in the top ten with almost no clicks needs a better title; a page in positions 10 to 20 needs better content and links; a page beyond 20 is usually aimed at the wrong query.
- Live data. Search Console, a crawler and the page source, connected through MCP or built-in connectors, so the findings come from your site and not from the model’s memory.
- Today’s task. One short instruction: review last week, check the pages changed in this release, or draft titles for the five weakest pages.
What to hand over first
Start with work that is dull, repeats every week and needs evidence rather than taste.
A weekly Search Console review. Which pages gained or lost clicks, whether a change you shipped made a difference, and which queries rank on page one but earn no clicks. Our own weekly review of orkas.ai works this way: a scheduled task in Orkas pulls the reports, lines each page up against the date its changes went live, and writes a short report with proposed fixes. A person decides what ships.
A check after every release. Titles, descriptions, canonical tags, hreflang, structured data that matches the visible text, and broken links. These break quietly, and nobody notices until traffic drops.
Answer-first drafts. A brief and a first draft for a page that answers one question directly, with the facts it relies on listed and the ones nobody could verify flagged.
What should stay with a person
- Which queries deserve a page at all. An agent can show you demand; it cannot tell you whether those visitors will buy.
- Anything a page says about your product, your prices or your results. An agent fills gaps with plausible text, so someone has to check the claims.
- Publishing and deploying. Let the agent prepare the change and the evidence; you decide when it goes live.
- Numbers nobody can source. If no data stands behind a search volume or a traffic forecast, the right answer is “unknown”, not an estimate dressed up as a fact.
Where agentic SEO goes wrong
Most failures come from a few habits:
- Treating impressions as success. A page can gain impressions and no clicks; only clicks and click-through rate show that a change worked.
- Changing many pages at once, so nobody can tell which change helped.
- Rewriting titles that already earn clicks, instead of the ones that rank and earn nothing.
- Building pages for queries nobody searches. A new page has to match a query that already exists.
Running agentic SEO in Orkas
Orkas is a desktop workspace where AI agents work on your projects, with your files and the services you connect. Its SeoGeoAgent follows the pattern above. Give it a URL and it audits the page: technical SEO, indexing, structured data, Core Web Vitals, content quality, and how easy the page is for AI answers to cite. It ranks the findings by impact and labels each one as measured or estimated. Connect Google Search Console or Bing Webmaster Tools and it works from your real clicks and impressions. Give it the folder of your site’s source code and it applies the fixes you approve, then checks the edited files again. It never commits, pushes or publishes on its own.
It runs on Orkas-managed models or on your own API key, with no paid SEO data subscription. A monitoring run can be scheduled to compare your pages against a saved baseline. Scheduled tasks run on your computer, so Orkas needs to be open at the scheduled time. Orkas is available for macOS and Windows.
To try this on your own site, download Orkas and start SeoGeoAgent with the address of the page you care about most.