Generative AI creates content. Agentic AI does something with it. That difference sounds small, but it changes how work, search and your visibility will work in the coming years. In this article: what the difference is exactly, which four layers make an agent possible, why it’s not a new technical SEO, and what agents mean for your SEO.
What is generative AI?
Generative AI creates new content based on a prompt: text, image, code. Think of ChatGPT, Claude or Gemini. The model predicts the most logical next piece based on what it was trained on. It’s reactive: you give an instruction, the model answers, and you assess and act. One task at a time, with you as director between each step. Strong at making, but it waits for you.
What is agentic AI?
Agentic AI works independently towards a goal, without you having to steer between every step. It runs in a loop: observe, reason, act, observe again. Give it a brief, for example “find the best supplier and prepare a comparison”, and it plans the steps, uses tools, remembers what it did and corrects itself. Where with generative AI you mainly have to prompt well, with agents it’s about briefing well and daring to delegate. And where generative AI delivers a draft that you still execute, an agent can take on the execution itself: send an email, schedule an appointment, publish content.
The four layers that make an agent possible
- Planning: the goal is broken into logical, sequential steps before anything happens.
- Tools: access to APIs, databases, search engines and code, so the agent can actually do something.
- Memory: holding context across the whole task, so the agent builds on it and corrects itself.
- Action loop: the cycle of observing, reasoning and acting, so errors are caught and the result improves each round.
A layer around the same models
Important misconception: agentic AI is not a new kind of AI. Under the hood are the same language models (GPT, Claude, Gemini). What makes it agentic is the orchestration around it: the planning layer, the tools, the memory and the action loop from above. As with SEO and AI search, it’s the same foundation, deployed more intelligently. And human oversight is still needed, especially for steps with real consequences, because a mistake in a chain can cascade.
LLMs are moving from giving answers to doing things. The question is no longer just whether a model reads your content, but whether an agent can act on it.
Giacomo Perticara, founder and SEO strategist at GRP Digital
What this means for SEO and your visibility
Two things become concrete. First: agents visit your site themselves. In our piece on Generative Engine Optimization we already wrote that LLMs read your content in three ways, one of which is emerging: agent visits. An agent that makes a choice on a user’s behalf has to be able to read, understand and trust your offering, otherwise you don’t get chosen. Second: agents take over routine work. Keyword research, SERP analysis, technical audits and mapping content gaps can be done largely autonomously by an agent, with connections to Analytics, Search Console and your CMS. That shifts the work from doing to steering and judging.
An example from both worlds. B2C: a consumer asks an agent “sort out the cheapest energy supplier for my situation”. The agent compares, chooses and acts. If you’re not clearly and structurally in there, you don’t exist for that transaction. B2B: a buyer has an agent make a shortlist of suppliers based on specifications, certifications and references. In both cases the decision moment shifts to the machine, and your visibility for that machine determines whether you make the cut. Optimising for the search engine thus also becomes optimising for the agent.
What you can do now
- Make sure your content is readable in raw HTML; many agents and AI crawlers render little JavaScript (the visibility gap).
- Make your offering explicit and structured with structured data, so an agent can interpret it unambiguously.
- Build demonstrable authority and trust, because agents choose sources they trust (E-E-A-T).
- Be complete: an agent asks sub-questions, so cover the whole decision, not just your main message.
- Use agentic tools yourself to speed up routine SEO, but keep human oversight on decisions that matter.
A shift that has already begun
This is not a distant future: Gartner expects agentic AI to autonomously resolve around 80% of routine customer service questions by 2029. Agentic systems use the same models, deployed more intelligently, and will increasingly act on their own and even buy on a potential customer’s behalf. Whoever makes sure their brand is readable, structured and trustworthy now is also ready for the agent that will soon choose on a potential customer’s behalf.
Curious how ready your site is for AI agents? At GRP Digital we map it and turn it into concrete growth.
Source: Gartner, on the rise of agentic AI in customer service.
