What is an AI agent, and how is it different from a chatbot?
An agent pursues a goal over multiple steps, deciding what to do next and taking actions in the world; a chatbot responds to messages. The difference is autonomy and the ability to act rather than only to produce text.
The components of an agent:
A goal, supplied by the user, that requires more than one step.
Tools — functions it can call: searching, reading and writing files, querying databases, calling APIs, executing code, sending messages.
A loop. It decides on an action, takes it, observes the result, and decides again — continuing until the goal is met or it gives up. Observation and adaptation are what make it an agent rather than a script.
Memory, to carry state across steps.
Why this changes the risk profile entirely. A chatbot that is wrong produces a wrong sentence, which a person can evaluate. An agent that is wrong performs actions — deleting files, sending emails, making changes, spending money — and may take many such actions before anyone notices. Errors compound across steps, and an early misunderstanding propagates through everything that follows.
Where they work well: tasks with verifiable intermediate results, where the agent can check its own work — writing and running tests, querying data and validating output, searching and cross-referencing. Feedback is what makes the loop converge.
Where they work badly: long chains with no verification, tasks requiring judgement about acceptability, and anything where an error is expensive and irreversible.
The practical safeguards: narrow, explicitly granted tool permissions; human confirmation for consequential or irreversible actions; operating in a sandbox or on a copy; step limits and budget caps; and full logging so behaviour can be reconstructed.
Prompt injection is the acute danger. An agent reading untrusted content — a web page, an email, a document — can be instructed by that content, and it has the tools to act on the instruction. The combination of untrusted input, tool access and sensitive data is the genuinely hazardous configuration.
The honest assessment: agents work well on bounded, checkable tasks and are unreliable on long open-ended ones, and the gap between demonstration and dependable operation remains substantial.