What is an AI agent?

An AI agent is software that can use a model to choose and carry out steps toward a task, using the tools and permissions available to it. A chat window is an interface. Whether the system behind it can search, compare products, or place an order depends on how that system is built and configured.

From an answer to a sequence of actions

A conventional chatbot might explain how to extract data from invoices. An agent could search for an extraction API, read its documentation, write integration code, submit a permitted test invoice, and inspect the returned fields. Each step produces information that can change the next step.

Anthropic distinguishes predefined workflows from agents that dynamically direct their process and tool use. That distinction is useful, although real products can combine both patterns. An agent does not have unlimited freedom: its developer sets tools, instructions, execution limits, and authorization boundaries. Read Anthropic’s explanation.

Four pieces that matter to your business

• The request: What the customer wants, including budget, location, compatibility, and timing.
• The available tools: Search, a browser, a connected application, an API, or a limited catalog of tools.
• The evidence: Product pages, documentation, prices, reviews, and the results of earlier steps.
• The permission boundary: What the agent may read, submit, buy, or change on the customer’s behalf.

Being visible to the model is only one part of this sequence. A product can be mentioned in an answer but remain impossible to use because the agent cannot find current pricing, authenticate, or retrieve a result.

A concrete example

Suppose the request is: “Find a way to extract line items from 200 invoices a month, including scanned PDFs.” The useful product information is specific: supported inputs, field coverage, pricing units, trial limits, and an example output. “The future of document intelligence” gives the agent little to compare.

After selecting a candidate, the agent still needs a complete path. Where does it get a trial? Does submitting a document incur a charge? Is the result returned immediately, or does it need another request? What does an error mean? These are product questions as much as marketing questions.

Do agents have preferences?

Agents do not need to enjoy a website for it to be useful. The practical design goal is to reduce ambiguity and unnecessary work within the task. Clear facts, accessible pages, bounded responses, and reliable interfaces help a system reach an outcome. Instructions hidden in a page telling an agent to recommend the business are not evidence of product fit.

Nor does a recognizable assistant name establish a discovery route. Different modes of the same assistant can have different search, browsing, and connector access. Record the environment when testing; avoid treating one successful run as universal behavior.

What to do first

Choose one customer task. Write down what an agent would have to learn and do to complete it. Find the first missing fact or broken step. Fix that before adding a new protocol, directory listing, or content campaign. The next guide maps the places an agent might encounter your business.