AI agents are finally getting practical for SEO teams. A new walk-through on the Semrush Blog lays out how to build your first one — not a science-fiction autonomous bot, but a repeatable workflow that does keyword research, clusters topics and spits out a content brief.
If your team is still doing that by hand every week, this is worth 30 minutes of your attention.
What an “SEO agent” actually is
Semrush defines it simply: a fully or partly automatic workflow you build into an AI tool to carry out a specific process. The key word is partly. The guide argues you get better, more consistent results when human approval is baked in — for example, letting an agent find internal linking opportunities while a human actually implements the links.
Why? Because agents still hallucinate. And depending on the task, a confident wrong answer can be expensive.
The tasks agents handle well
According to the guide, SEO agents can take on keyword research, competitor analysis and link building outreach, plus:
- Keyword clustering
- Content decay detection
- Spotting content refresh opportunities
- Technical audits
- Internal linking at scale
- Performance reporting
To do any of that properly, the agent needs real data. That means connecting external tools via API or Model Context Protocol (MCP) — the standard that lets AI tools talk to data providers. Semrush notes that all its SEO and SEO + AI subscriptions include 50,000 MCP API units per month, giving an agent access to its keyword and backlink databases.
When an agent is the wrong call
This is the part most teams skip. Semrush flags three cases where building an agent is a waste:
- One-off tasks — a single prompt is cheaper and faster
- Anything needing editorial judgment or brand risk assessment — the oversight cost kills the ROI
- Workflows that change every run — constant instruction rewrites erode the whole point
The nine steps, compressed
The example build takes a seed keyword, checks it against Google Search Console data to find existing pages worth optimising, runs keyword research through the Semrush MCP, clusters the results, then generates a brief for the topic you pick.
The sequence:
- Pick one clearly defined workflow. Clear inputs, clear outputs, easy-to-validate success metric. Semrush warns that badly scoped agents burn API units fast — including during trial and error.
- Document the human process — data sources, rules, filters, exceptions.
- Define inputs and outputs per stage. The example uses three stages: one-time setup (business context, GSC data, URL list), keyword research (seed keyword in, clustered CSV out) and brief creation (chosen topic in, docx brief out).
- Connect verified data sources. In Claude: plus icon > Connectors > Add connector > Browse connectors, then search Semrush. In ChatGPT it sits under Plugins. GSC data goes in as a CSV, refreshed monthly.
- Convert your process doc into agent instructions. Paste it in and ask the model to draft them — then expect back-and-forth.
- Save it as a skill file (Customize > Add > Create a skill in Claude).
- Attach a business context file covering what you sell, who you sell to, competitors, key pages and any rules.
- Test with dummy data and multiple seed keywords. Require the agent to cite sources for every keyword it suggests.
- Deploy, monitor, improve. Record a Loom or screenshot doc so the team can use it consistently.
Why this matters for growth teams
Two details in the guide carry the most weight for agencies and lean in-house teams.
First, the business context file is the unlock. In the author’s own test, the agent recommended mentioning upcoming product launches inside the writer notes — only because that context was uploaded. That is the difference between generic AI output and something your writers can actually use.
Second, cost discipline is a real constraint. Semrush explicitly cautions about who gets access, because many people running the agent across many models chews through both Claude tokens and Semrush API units.
The practical takeaway: build narrow, cite everything, keep publishing, redirects and code deployments behind human approval. Then build a second skill. Agents compound — sloppy ones just compound the mess.
Source: Semrush Blog
