How to Set Up an AI Agent for an SEO Agency: Files, Connectors, Skills, and Project Memory

An AI agent for an SEO agency is a working system that combines project files, external services, role instructions, and accumulated context. A single ChatGPT or Claude subscription does not become such a system: in the author’s view, AI needs to be “hired,” trained, and integrated into the agency’s specific processes.

You should not start with a fancy prompt.

For regular work, it is more important to determine what data the agent can see, what actions it is allowed to take, what rules it uses to make decisions, and where it saves the results.

Why an AI Subscription Does Not Replace a Workflow

A regular chat responds to a request within the context available to it. If a specialist has to resend client files, describe restrictions, and explain which tasks have already been completed every time, the system does not accumulate a complete project history. The author calls this mode constant AI retraining: a new dialogue requires the initial information again, even if similar work has already been done.

A working AI agent is structured differently: it receives access to approved sources, uses the instructions for the required role, refers to project materials, and produces the result in a predefined format. A specialist still reviews the response. The author specifically emphasizes that AI does not replace SEO expertise and should not publish materials or send messages to clients without supervision.

Four Components of an AI Agent for an SEO Agency

In the configuration described by the author, the system consists of four parts: project files, connectors, skills, and project memory. Each component solves a separate task, so they should not be combined into one long prompt.

• Project files. These contain source data, exports, audits, rules, and the results of previous work.
• Connectors. They link AI to external services containing rankings, documents, payments, meetings, and other work-related information.
• Skills. These are instructions for a specific role or process, such as writing copy, conducting a technical audit, or preparing HTML.
• Project memory. It preserves decisions, client restrictions, past mistakes, and information about what has already been done.

This separation makes maintenance easier. When a client rule changes, there is no need to search for it among dozens of universal instructions. If a service or access permission changes, it is enough to adjust the connector without affecting the agency’s entire logic.

How to Organize Project File Memory

Project file memory is a structured document database that the agent refers to when handling new tasks. The author describes an approach in which AI works directly with files in the project folder on a computer rather than only with materials manually attached by a specialist to the current chat. The specific access method depends on the application being used, the operating mode, the operating system, and the permissions.

The project folder should reflect the workflow rather than becoming a warehouse for random files. It is convenient to divide the materials into several levels:

1. Rules. Report formats, tone requirements, the approval process, and source restrictions.
2. Source data. Keywords, ranking exports, analytics data, technical logs, and materials from the client.
3. Results. Audits, content plans, proposals, reports, and finished documents.
4. Decision history. What was changed, why the decision was made, and what result was obtained.
5. Errors and unsuccessful attempts. Examples of incorrect conclusions, overlooked conditions, and approaches that must not be repeated.

Project memory should not consist solely of correspondence. In the author’s approach, it includes working rules, client restrictions, exports, past audits, and descriptions of unsuccessful attempts. This allows a new chat within the project to understand what has already been done, which tasks remain, and what form the next document should take, provided that the necessary materials have actually been saved and are available to the agent.

Client Restrictions Should Be Recorded Separately

Imagine the following rule: “Work only with files on the computer; do not touch the cloud copy.” If this instruction remains in one old message, a new dialogue can easily overlook it. Recording it in the project memory and placing it in the restrictions section turn the rule into a permanent condition of the agent’s work.

What happens if the agent finds a newer file in cloud storage? The answer is determined not by the model’s assumption but by the saved project rule. If access to cloud data is nevertheless needed, it should be allowed through a separate instruction.

Which External Services to Connect to an AI Agent

A connector links AI to an external service and provides it only with the data and actions allowed by the connected account, workspace, and application settings. OpenAI’s help materials specifically state that the availability of connected apps depends on the plan, region, role, model, and workspace settings. Installing an app does not eliminate provider authorization or administrative approval.

For an SEO agency, the author considers the following sources:

• a rank-tracking service, such as SE Ranking;
• Google Search Console for checking search data and the site’s status;
• Google Docs and other project files;
• client payment information;
• the calendar and meeting list;
• task-tracking systems, correspondence, and client inquiries, if they are used by the agency.

You should not connect everything at once. First define the task, then the data source, the required permissions, and the permitted result. Reading reports is sufficient for rank monitoring. Sending a message to a client requires a separate text review and explicit human confirmation. The ability to read data does not mean having the right to change it or perform actions without approval.

How to Describe Skills Through Job Responsibilities

In this system, a skill is not a short prompt but a description of a job responsibility. It should specify what the agent does, which sources it uses, the format in which it returns the result, and which actions it is prohibited from taking.

The author uses skills for writing copy, creating illustrations, web design, transferring layouts to Figma, generating HTML, and frontend, backend, and full-stack development. This approach separates roles: an agent that prepares a content plan does not need permission to modify site code, while a development agent should not independently send a proposal to a client.

The Structure of a Single Skill

• Role. For example, SEO analyst, editor, developer, or reviewer.
• Input data. Files, exports, links to approved sources, and required fields.
• Workflow. The sequence for checking, analyzing, and preparing the result.
• Quality criteria. Completeness, consistency with the data, absence of unverified claims, and compliance with the required format.
• Restrictions. No publishing, modifying source files, or using unconnected sources.
• Response format. A document, table, task list, message draft, or code.

The author recommends not forcing the agent to ask constant clarifying questions. If the data is insufficient, the instructions can require it to make the most reasonable decision and separately state the assumption it used. This mode speeds up the work, but disputed points still require subsequent review because the model may misjudge the significance of missing data.

Automatic Daily Review

In the system described by the author, the computer remains on and the agent starts without a person at the screen. It compares the site’s rankings with the previous day or week and checks payments, the calendar, and project logs. This is the author’s working scenario, not a universal capability of every subscription: automatic launching depends on the specific tool, connected services, permissions, and security settings.

The author divides the daily review result into three sections:

1. urgent issues requiring a response;
2. tasks specifically for the manager or specialist;
3. messages and statuses that need to be passed on to clients.

This format is more useful than a lengthy account of every change. The manager immediately sees where a human decision is required and which operations can remain in the execution queue.

Results Review and the Role of Humans

Access to files and services alone does not make an AI agent reliable. The author cites cases in which the model confused the site’s subject matter, ignored some of the data, or compared different seasonal periods. Without specialist review, the error could have made its way into published material or a client report.

To reduce the risk, the author uses a separate verification agent. It independently reads the created document, looks for errors, and tells the original agent what needs to be corrected. This review requires additional resources and does not guarantee absolute accuracy, but it separates result creation from quality control.

A technical audit also cannot be handed over to a model completely. According to the author, their automated system covers about 80% of audit questions, but unusual sitemaps, HTTP 500 errors, and false positives require manual analysis. The author duplicates the results using several tools and compares them, since discrepancies between checks help identify areas requiring additional analysis.

How to Work with Content

The author does not recommend asking AI to write articles “from scratch.” In their view, it is more reasonable in this mode to obtain an outline and gather the factual basis from the client’s materials: voice messages, videos, key points, and documents. AI then combines the scattered information, formats the text, and prepares screenshots and code snippets.

According to the author, mass generation from a template is better suited to product and category pages for online stores, where there may be thousands of pages. Informational materials require a different process: first, the source knowledge and restrictions are collected; then the structure is created, after which an editor checks the facts, logic, and consistency with the page’s purpose.

How to Check That a New Chat Uses the Project Context

The check should be practical. Open a new chat within the project and ask the agent to list the active restrictions, completed tasks, unresolved questions, and sources it plans to use. Do not provide this information in advance in a new message.

• Ask it to name the rule prohibiting work with the cloud copy, if that rule is saved in memory.
• Give it a task based on a file that is already in the project folder.
• Check whether the agent distinguishes source materials from final results.
• Request a list of assumptions if some data is missing.
• Make sure the agent does not suggest an action for which it has no permission.

If the new chat cannot see the rule or confuses file versions, the problem is not the wording of the next request but the project architecture: the memory is not structured, access permissions are configured incorrectly, or the required source is not connected.

The Final Implementation Plan

Start with one recurring process, such as daily rank monitoring or preparing an audit draft. First collect the files and rules, then connect one data source, describe the skill, and add a results review. After several cycles, it will become clear which actions can be automated and where specialist approval remains mandatory.

In the author’s concept, the manager retains the role of controller, while AI agents perform specific job responsibilities. This model works only when the boundaries are clear: the agent knows which data to access, what it is allowed to do, and when to hand the result over to a human. A subscription provides access to the tool, while files, connectors, skills, and memory turn it into a working SEO agency system.