You can build an SEO agent with Gemini, Google's family of AI models, using Google AI Studio for quick experiments or Vertex AI on Google Cloud for something more permanent. Like the others, Gemini supports function calling, so it can ask your code to fetch data and then reason about what comes back.
It's a comfortable fit if your company already runs on Google Workspace and Google Cloud. Your Search Console, Sheets, and BigQuery data live in the same place, which saves a lot of plumbing. Some setups also let Gemini ground its answers in Google Search results, which helps with “what's current?” questions.
Is this for you?
- Your data already lives in Google tools like Sheets, BigQuery, and Search Console.
- You want to test ideas in a browser playground before writing any code.
- Your team is comfortable working in Google Cloud.
What you need before you start
- A Google AI Studio API key for experiments, or a Google Cloud project with Vertex AI.
- Search Console data. If you have lots of pages, its bulk export to BigQuery (Google's data warehouse) is worth setting up.
- Function definitions for the tools you want the model to use.
Your first build: question-mining from Search Console
A gentle first agent: it reads your own data and makes suggestions, but never changes anything.
- Pull queries for one topic from Search Console, or from your BigQuery export.
- Ask Gemini to group them by what the searcher wants to know.
- For each group, check which of your pages ranks, if any.
- Return the question groups that have no good page yet, with a suggested page for each.
- Go through the list with whoever owns content.
Once the suggestions look sensible, add a tool that pulls the current top results for each gap so the agent can say what a winning page tends to cover.
Mistakes to avoid
- Grounded answers still need checking. A summary of search results isn't the same as looking at them.
- Cloud permissions get complicated. Give the agent's account only the access it needs.
- Trim big exports before sending them to the model, or costs climb.
- Re-test when models change. Google updates them regularly.
Where to go next
- Google Search Console API: the data behind this agent
- Build SEO agents: principles for any model
- Keyword research: related tasks you can automate
- The SEO automation loop: the observe, decide, act, measure cycle every build fits into.
- The SEO autonomy dial: how much control to hand over, and when.
- Examples: what other people have built.