From answers to actions
A chatbot generates a response to your prompt. An agent adds a loop: it chooses an action, uses a tool, observes the result, and decides what to do next. For example, a research assistant might search a document collection, compare results, and assemble a draft with references. The useful distinction is the ability to act on feedback, rather than the ability to sound conversational.
The tools make the difference
An agent can only perform the actions its tools allow. A calculator, a document search tool, and a calendar integration each add a different capability. Good systems keep those permissions narrow. Reading a file and sending an email should have different permission boundaries, because their consequences differ.
Why verification still matters
Models can misunderstand instructions and tool results. An agent may also follow an unhelpful path repeatedly. Clear stopping conditions, a limited action budget, and human review for important decisions reduce the consequences. Evaluate the final outcome against the original task instead of assuming a long sequence of actions means success.
A practical place to start
Start with a small, reversible task such as organizing research notes. Define what a successful result looks like, provide a limited set of tools, and inspect the output. Expand the workflow only after you understand where it fails. Autonomy is useful when the surrounding system makes errors easy to detect and recover from.
