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Automation & AI agents

What is an AI agent, in plain terms?

The short answer

An AI agent is software that uses a language model to decide its next step and act through tools — reading a document, calling an API, drafting a reply — rather than following only fixed rules. In a real business process, the useful ones are mostly code with a model at one or two steps.

Updated

What the model is good at

Reading unstructured input no schema could describe, producing a first draft, and classifying something genuinely fuzzy. It is bad at arithmetic, bad at giving the same answer twice, and bad at being audited.

Where the model goes

The test: if you would be uncomfortable with a step producing a different answer tomorrow on the same input, it is not a model step — write the function. On our own Growth Engine, one step fetches a prospect's homepage and scores it on six dimensions; the compliance around it lives in code, not in the prompt.

A person at one gate

POD Engine, our own print-on-demand pipeline, runs nine stages from trend to storefront, and exactly one waits for a person: the moment before artwork is generated and published to the printer.

Seeing what it is doing

An agent estate needs a quick answer to "what is running right now?" Agent Console, which we built, puts six control surfaces behind one screen.

The long version is in the guide to putting AI agents into a real process.

Asked next

Is a chatbot the same thing as an AI agent?

Not necessarily. A chatbot answers messages; an agent takes actions through tools, such as updating a record or sending a draft. Many useful agents never talk to a customer at all.

What can go wrong when an agent can use business tools?

It can take a wrong action with real consequences. That is why agents get scoped tool access and a full audit trail, and why the step before anything a customer sees keeps a person on it.