An AI agent is a language model set up to pursue a goal over several steps, choosing and using tools along the way. Instead of answering one prompt, an agent might search the web, open pages, compare options, fill in a form and report back. Agents matter to marketers because they increasingly do research and even transactions on a buyer's behalf, which means your site has to work for software visitors as well as human ones.
What makes something an agent
The term is used loosely. The common thread is a loop: the model looks at the task, decides on an action, observes the result and decides what to do next until it is done or stuck.
- A goal, usually from a user
- Tools: search, a browser, code execution, APIs, files
- A loop of planning, acting and checking results
- Some memory of what has been done so far
- Limits on what it may do without human approval
Agents as website visitors
When an agent researches software for a user, it reads your pages, may click through pricing and docs, and may try a signup or demo form. It does this with a browser or by fetching pages directly. Agents can struggle with content locked behind interactive widgets, pricing that requires a sales call to reveal, cookie walls, and forms that rely on visual cues alone.
This area is moving quickly and agent behavior is not well documented by most vendors. Treat accessibility, clear HTML and plainly stated facts as the durable groundwork, since they help agents, assistive technology and people at the same time.
Agents inside SaaS products
Many SaaS companies are building agents into their own products. Protocols such as the Model Context Protocol make it easier for agents to connect to your product's data and actions, which can itself become a channel: an agent can only recommend or use tools it can reach.
Example
An agent compiling a shortlist
An operations manager asks an agent to find three inventory tools that integrate with Shopify and cost under a set budget, then book demos. The agent reads several vendors' sites. Glimmerdock, a fictional inventory app, shows prices in plain HTML and uses a standard, labeled demo form, so the agent completes the task. A competitor hides pricing in an image and uses a multi-step form with unlabeled fields; the agent skips it.
Common mistakes
Blocking all automated traffic
Aggressive bot rules can stop the agents your buyers send. Decide which automated visitors you want and configure access deliberately.
Assuming agents see what humans see
Agents may miss content in images, canvas elements or complex scripts. Keep key facts in text.
Questions
No. Crawlers fetch pages in bulk for training or indexing. Agents act on a specific user's task, more like a browser session.
Some identify themselves with user agents; others run in normal browsers and look like human visitors. Server logs and analytics anomalies are the main clues, and detection is imperfect.
