Introduction to Agentic SEO
For more than two years, a new concept has been emerging called Agentic SEO. The idea is to perform SEO using agents based on language models (LLMs) that perform complex tasks autonomously or semi-autonomously to save time for SEO experts. Of course, humans remain in the loop to guide these agents and validate the results. Today, with the advent of ChatGPT, Claude, Gemini, and other powerful LLM tools, it is easy to automate complex processes using agents.
What is Agentic SEO?
Agentic SEO is, therefore, the use of AI agents to optimize SEO productivity. It differs from Generative Engine Optimization (GEO), which aims to improve SEO to be visible on search engines powered by LLMs such as SearchGPT, Perplexity, or AI Overviews. This concept is based on three main levers: Ideation, Audit, and Generation.
AI Agents and Workflows
Before presenting detailed use cases regarding ideation, it is essential to explain the concept of an agent. An AI agent needs at least five key elements to function:
- Tools: These are all the resources and technical functionalities available to the agent.
- Memory: This is used to store all interactions so that the agent can remember information previously shared in the discussion.
- Instructions: Which define its limits, its rules.
- Knowledge: This is the database that contains the concepts that the agent can use to solve problems; it can use the knowledge of the LLM or external databases.
- Persona: Which defines its “personality” and often its level of expertise, including, in particular, its way of interacting.
Workflows
Workflows allow complex tasks to be broken down into simpler subtasks and chained together logically. They are useful in SEO because they facilitate the collection and manipulation of data needed to perform specific SEO actions. Furthermore, in recent months, AI providers (OpenAI, Claude, etc.) have moved from simply offering the model as such to enriching the user experience.
Use-Case: Ideation
Let’s start with ideation. As you know, AI excels at opening up possibilities. With the right methods, it is possible to push AI to explore every conceivable idea on a topic. An SEO expert will then select, refine, and prioritize the best suggestions based on their experience. Numerous experiments have demonstrated the positive impact of this synergy between human creativity and artificial intelligence.
AI and Human Collaboration
Ethan Mollick’s diagram illustrates a benchmark of the creative process with and without AI. The figure shows the distribution of creativity scores (from 0 to 10) assigned to different sources: ChatGPT, Bard (now Gemini), a human control group (HumanBaseline), a human group working with AI (HumanPlusAI), and another group working against AI (HumanAgainstAI). The horizontal axis represents the perceived level of creativity, while the vertical axis indicates the frequency of each score (density). We can see that the curve corresponding to HumanPlusAI is generally shifted to the right, meaning that evaluators consider this human+AI collaboration to be the most creative approach.
Tools Like Deep Research
Among the tools available, Deep Research stands out for its ability to conduct in-depth research in several steps, providing a valuable source of inspiration for ideation. I recommend using this open-source version; if you prefer, you can also use the OpenAI or Perplexity versions.
How Does It Work?
This diagram describes the operation of the Open Source Deep Research tool. It generates and executes search queries, crawls the resulting pages, then recursively explores promising leads, and finally produces a detailed report in Markdown format. There are several steps to using Deep Research:
- Enter your query: You will be asked to enter your query. You must try to be as precise as possible. Do not hesitate to ask ChatGPT or Claude to create your DeepResearch search.
- Specify the depth of the search (recommended: between 3 and 10, default: 6): How many topics can be found in each iteration?
- Specify the depth of exploration (recommended: between 1 and 5, default: 3): If the crawler finds an interesting topic, how many pages deep will it explore?
- Refinement: Sometimes, you need to answer follow-up questions to refine the direction of the search.
Use Cases
With this open-source version, you can turn this open-source project into a real SEO tool. I have identified more than four use cases:
- Competitor Content Analysis: The tool can automate the collection and analysis of competitors’ content to identify their strategies and spot opportunities for differentiation.
- Long-Tail Keyword Research: By analyzing the web, it can identify specific keywords with high potential and less competition, facilitating content optimization.
- SERP Analysis: It can collect and analyze search engine results to understand trends and competitors’ positioning.
- Content Idea Generation: Based on in-depth research, it can identify relevant topics and frequently asked questions in a given niche.
No-Code Agent Workflow Tools
Here is an example of a no-code tool called Dng.ai. We use a CSV file provided by Moz, which we analyze using an agent capable of processing the data, generating Python code, and extracting all the necessary information. The agent then compares this data with the topics already on your site to identify missing content. Finally, it generates a complete list of topics to create, ensuring optimal coverage of your SEO strategy.
Conclusion
I invite you to explore the full potential of these tools and experiment with them to optimize your SEO strategy. With Agentic SEO, it is possible not only to customize and improve existing tools but, more importantly, to create your own tool to suit your specific needs. By leveraging AI agents and workflows, you can streamline your SEO process, increase productivity, and achieve better results. Remember, the key to success lies in the collaboration between human expertise and artificial intelligence.