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AI Search & Agents

AI Agent

In shortA system that pursues a goal on its own: it plans, uses tools, keeps track of what it has learned and adapts until the task is done.

Updated 24 Oct 2026· 2 min read

An AI agent is a system that works towards a goal with little step-by-step instruction. You give it an objective, and it decides which sources to consult, which tools to call, what to do with the results and when it has enough to answer. Most agents today use a Large Language Model (LLM) as their reasoning engine, wrapped in code that lets the model take actions.

In MLforSEO’s AI Search Optimisation & Agentic SEO course, the working definition is a system that “autonomously pursues goals by retrieving information using tools, maintaining state, and adapting based on feedback without per-step human direction”. That matters for SEO because agents now decide which content gets read, cited or ignored.

How it works

The course breaks an agent down into five traits:

  • Autonomy: it makes decisions without a human approving each step.
  • Goal-directed behaviour: it infers the objective behind a request (for example, “help this person choose a CRM for a 50-person company”) and works backwards from it.
  • Tool use: it calls search APIs, databases or other functions directly. See Function Calling (Tool Use).
  • State persistence: it remembers earlier steps in the session, including which sources it already judged unreliable.
  • Adaptability: it changes course when results contradict each other or are too thin, for example by running a follow-up query.

MLforSEO models the loop as retrieve, evaluate, act and combine: the agent breaks the question into sub-queries (see Query Fan-Out), gathers candidates, scores them for credibility and consistency, fills gaps with more queries and only then writes an answer. Traditional search engines, simple chatbots and recommendation systems are not agents in this sense; ChatGPT with browsing, Perplexity, Claude with tools and Google’s AI features are the examples the course uses.

How it’s used in SEO and marketing

  • As an audience. Agents read your pages on someone’s behalf, so clear, self-contained and verifiable content is more likely to be selected.
  • As a buyer. Commerce protocols such as ACP and UCP let agents compare and purchase, which rewards machine-readable prices, stock and policies.
  • As a workflow tool. Teams build agents that monitor rankings, draft briefs or check internal links, usually with a Human-in-the-loop approving the output.

Example

A user asks an assistant for the best project management tool for a remote engineering team. The agent splits this into sub-queries about pricing, integrations, reviews from technical users and team size, runs them in parallel, checks pricing pages against review sites and drops sources whose numbers contradict better-established ones. A pricing page that hides prices behind a contact form never makes it into the answer.

Limitations and things to watch

  • Agents fail silently: a page that is blocked, too long or ambiguous is skipped without any error you can see in analytics.
  • They can still hallucinate or act on wrong information, so autonomous actions need guardrails and human review.
  • The word “agent” is used loosely in marketing. Check whether a product actually plans and uses tools, or is a single prompt behind a button.

Learn it on MLforSEO

Where AI Agent shows up on MLforSEO

Learn it in a course

Try it yourself

Sources and further reading

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