Build an AI SEO agent in 9 steps: keyword research to content briefs
SEO work is repetitive by design. Keyword research, topic clustering, content briefs, internal linking checks. The same process, every time, for every new piece of content. That repetition is exactly what makes it a good candidate for an AI agent.
This guide walks through a nine-step process for building an AI SEO agent that takes a seed keyword as input and outputs a finished content brief. The workflow is built around Claude and the Semrush MCP, but the structure applies to any AI tool and data source combination.
What an AI SEO agent actually is
An AI SEO agent is a fully or partly automatic workflow you build into an AI tool to carry out a specific process. It does not have to be fully autonomous. In fact, building human approval into your agent processes tends to produce better and more consistent results.
For example, if an agent finds internal linking opportunities, a human should still implement the actual links. Even well-configured AI agents can hallucinate and make mistakes, and some of those mistakes are expensive depending on the task.
️ What these agents can actually do
SEO agents can automate tasks like keyword research, competitor analysis, and link building outreach. Other tasks they handle well include:
- Keyword clustering
- Content decay detection
- Identifying pages that need a refresh
- Technical audits
- Internal linking at scale
- Performance reporting
Most of these tasks require connecting external tools to your AI platform, either via an API or a Model Context Protocol (MCP) connection. MCP is a standard for how AI tools communicate with data providers. All Semrush SEO subscriptions and SEO + AI subscriptions include 50K MCP API units per month.
⚠️ When an agent is the wrong tool
Before building anything, it is worth being honest about where agents do not make sense:
- One-off tasks: A single prompt in any AI tool is cheaper, faster, and easier than standing up an agent.
- Tasks requiring editorial judgment or brand risk assessment: The oversight required makes the agent less cost-effective to run.
- Workflows that change every time: Constant instruction edits erase the value of having set up the agent at all.
If the task does not repeat on a schedule and with consistent inputs, skip the agent.
How to build the agent: 9 steps
The example workflow below produces keyword research, topic clusters, and a finished content brief. You can adapt every step to a different SEO use case.
Step 1: Pick one clearly defined workflow
Start with a single, repeatable task. Complex multi-step agents have too many failure points for a first build, and API units cost real money. A badly scoped agent can burn through your budget during the trial-and-error setup phase alone.
Choose a workflow with clear inputs, clear outputs, and easy-to-validate success metrics. The example agent in this guide does four things:
- Takes a seed keyword as input
- Checks the seed keyword against existing Google Search Console (GSC) data to spot optimization opportunities on existing pages
- Performs keyword research using the Semrush MCP and groups results into topic clusters
- Creates a content brief for a user-chosen topic based on SERP analysis and Semrush data
Success is measured by the quality of the content brief produced at the end. Multiple steps, one clear output.
Step 2: Document the existing human process
Write down how you would do this task yourself. Include the data sources you use, the rules and filters you apply, and any exceptions you account for. This document becomes the agent’s operating instructions in a later step.
Step 3: Define inputs and outputs for each stage
The example agent has three stages, each with distinct inputs and outputs:
- Stage 1 (one-time setup): User uploads a business context file, GSC data, and a list of existing URLs. Agent outputs a CSV of clustered queries from GSC for reuse in future runs.
- Stage 2 (keyword research): User inputs a seed keyword or topic. Agent outputs a CSV of topics clustered around that keyword.
- Stage 3 (content brief): User selects a topic from the Stage 2 CSV. Agent outputs a content brief as a
.docxfile (or markdown, PDF, or Google Doc if you connect Google Drive).
Specifying file types upfront reduces ambiguity and keeps outputs consistent across runs.
Step 4: Connect verified data sources
Connecting reliable data sources is what keeps the agent from hallucinating metrics. Relevant sources for an SEO agent include:
- The Semrush API or MCP
- Google Search Console
- Google Analytics
- Your CMS
- Manually created files: URL lists, target keyword lists
In Claude, connect the Semrush MCP by clicking the plus icon on any chat window, then selecting Connectors > Add connector > Browse connectors. Search for “Semrush” and click the plus button. Follow the sign-in workflow to finish.
For ChatGPT, the connection lives in the Plugins area. GSC data can be uploaded as a CSV. You only need to do this once at setup, then monthly to keep data current.
Note: Confirm you are permitted to connect any data source before adding it to your agent. Some AI tools may use your inputs for training purposes.
Step 5: Turn your process document into agent instructions
Paste your process document from Step 2 into Claude or ChatGPT and ask it to convert it into a set of agent instructions. Both tools handle this kind of conversion reasonably well.
Include a summary of the inputs and outputs you expect, and clarify which tools the agent should use and when. This reduces unnecessary token and API unit consumption.
Ask the tool whether it understands everything and whether it needs anything else to run the workflow. You will not get a perfect result on the first try. Expect iteration, and possibly some experimenting with different models. Once you have a solid draft, edit it into a finalized skill file for the next step.
Step 6: Add the skill to your AI platform
In Claude, click Customize in the left-hand sidebar, then select Add > Create a skill. Name the skill using only lowercase letters, numbers, and hyphens. Add a description, paste your agent instructions into the free text box, and click Create.
This turns your workflow into a reusable, callable skill you can reference whenever you need it.
Step 7: Add a business context file to the skill
A business context file tells the agent what your business does and who your audience is. This improves output quality for every task the agent runs.
Include at minimum:
- What you sell, including specific product or service features
- Who your primary and secondary audiences are
- Your main competitors
- Important pages on your site (best-selling products, highest-traffic posts)
- Any rules or filters you want applied consistently
For a content brief agent, this context shapes which keywords get prioritized. For example, the agent can surface or weight keywords related to upcoming product launches if those are documented in the context file.
To add the file in Claude, click + Add file within the skill editor, give it a name, paste in your business context, and click Create.
Step 8: Test with dummy data before going live
Upload test versions of your CSV files and run the agent with a real seed keyword. The source article uses “AI tools for freelancers” as the test input. Pay close attention to the reasoning the agent gives for its decisions, not just the output itself. That reasoning trace is what helps you diagnose failures.
Requiring the agent to cite sources for every keyword suggestion is worth the added output length. If a reviewer questions why a specific heading was recommended, they can trace it back to a competitor’s structure or a Semrush search volume figure rather than guessing.
Run multiple tests with different seed keywords. Test a listicle topic, a comprehensive guide topic, and a narrow product-focused topic. The goal is consistent, reliable output across input types before you share the agent with anyone else.
Step 9: Deploy, monitor, and update
Once tests pass consistently, you can use the agent yourself or roll it out to your team. Record a short walkthrough video or write a doc with screenshots so other users know exactly what to prompt and when to approve outputs.
Keep high-impact actions, like publishing live pages, implementing redirects, or deploying code, behind human approval. The agent should hand off to a human for anything that is hard to reverse.
Update the skill file over time as edge cases surface. In Claude, navigate to your skill, click the three-dot menu, select Edit, make your changes, and rerun the testing step before pushing the update to your team.
Be mindful of how many people have access to the agent. Wide usage across multiple Claude models can burn through both Claude tokens and Semrush MCP API units faster than expected.
Common pitfalls
- Starting too complex: A first agent with five interconnected steps and three data sources is a debugging nightmare. One workflow, one output.
- Skipping the process documentation step: If you do not write down how a human would do the task, the agent instructions will be vague and the outputs will be inconsistent.
- No source citations in output: Requiring the agent to justify every suggestion makes the human review step faster and more trustworthy.
- Letting too many people run it at once: API units are finite. Uncontrolled usage during testing phases is a fast way to hit your monthly limit.
I’m founder of BizStack at Cagri Sarigoz LLC and a passionate advocate for entrepreneurs.
With over 14 years in tech, marketing, and AI, including my role as Head of SEO at CitizenShipper and co-founder of TaleBot at Intale AI, I’m dedicated to sharing genuine, useful product insights and tips.
At BizStack, I aim to cut through the digital noise to provide clear, actionable advice.
And more than all else, I’m a father to a (always) little girl and a husband.
Contact me at [email protected] for assistance.
