What separates an agent from a chatbot
A chatbot answers. An agent acts. Asked about an order, a chatbot tells you how to check it; an agent looks it up, and where it is permitted, changes it. That difference introduces every hard problem in agent design, because a system that can act can also act wrongly.
What an AI agent is made of
- A goal, stated by a person or by another system.
- Tools it may call: an API, a database query, a search, another model.
- A loop that decides the next step, takes it, observes the result and decides again.
- Boundaries defining what it may do alone, what needs approval, and when it must hand over to a person.
- A log, because an action nobody can reconstruct is one nobody can defend.
Where agents work well
They suit tasks with a clear goal, a bounded set of actions and a tolerable cost of being wrong. Triaging and routing a request, gathering information across several systems, drafting something a person approves, and handling the repetitive middle of a process are all good fits. Each has a check at the end.
Where agents go wrong
Agents fail when the boundary is vague, when the cost of a wrong action is high and there is no approval step, or when nobody defined what happens on failure and the loop simply retries. An agent with unlimited scope and no logging is not an advanced system, it is an unsupervised one.
Do AI agents replace people?
In practice they take the repetitive middle of a process and leave the judgment at either end. The pattern that works is an agent that handles what is routine and escalates what is not, with the context already gathered, so the person arrives at a decision rather than at an investigation.
Related
- AI agent development: how we build them, and the boundaries we build in first.
- What is an AI copilot?: the assisted version of the same idea.