Agentic AI

Agentic AI describes AI systems that work towards a goal across several steps with a degree of autonomy. Rather than answering a single prompt and stopping, an agentic system decides what to do next, uses tools such as web search or APIs, reads the results and adjusts its plan until the task is done. Most current agentic systems are built around a large language model that acts as the reasoning engine.

How it works

An agentic system typically runs a loop: interpret the goal, plan steps, call a tool, observe the output and decide whether to continue. Tools are exposed through function calling or a standard such as the Model Context Protocol. Anthropic separates workflows, where code fixes the sequence of steps, from agents, where the model directs its own process and tool use. Both are usually called agentic.

In MLforSEO’s AI Search & Agentic SEO course, Beatrice Gamba defines agentic search by four properties: autonomous decision-making, multi-source synthesis, claim-level evaluation and selection before visibility. The system chooses which sources to read and which claims to keep before a human sees anything.

How it’s used in SEO and marketing

• As a channel to optimise for. ChatGPT search, Perplexity, Google AI Overviews and AI Mode retrieve, evaluate and synthesise on the user’s behalf, so being selected and cited replaces ranking as the goal (see agentic search).
• As a visitor to your site. Agents fetch pages, parse structured data and may abandon pages that are too heavy for their context window.
• As a way to automate work. Agents can run SERP analysis, internal-link suggestions or reporting, with a scripted pipeline underneath and the agent answering the “so what?” question.

Example

A user asks an assistant for a waterproof hiking jacket under a set budget. The agent searches, opens several product pages, checks price and stock in structured data, compares claims across sources and returns two recommendations. Your analytics may record that visit as a sub-second session with zero scroll depth, even though your page shaped the answer.

Limitations and things to watch

• Autonomy multiplies errors: a wrong early step, or an AI hallucination, can carry through the whole task.
• Agents need guardrails, logging and a human in the loop for anything that publishes, spends money or contacts people.
• The label is used loosely in marketing. Ask what the system actually decides on its own.
• Agent traffic is hard to measure, because much of it arrives without a referrer or never clicks at all.

Learn it on MLforSEO

• AI Search Optimisation & Agentic SEO course, which starts from the definition of agentic search.
• Your audience is no longer only human
• Your robots.txt might be the silent killer of your AI traffic
• 10 Ways to Automate SERP Analysis with AI puts an agent on top of a scripted pipeline.
• Agent SEO Implementation Roadmap (resource).