'AI agent' has become a catch-all term, which makes it harder to reason about. The distinction that matters in practice is simple: a chatbot answers, an agent acts.
Chatbots answer questions
A chatbot takes a message and returns a response. It might be grounded in your documents through retrieval, which makes it genuinely useful for support and knowledge lookup. But its job ends at producing text.
Agents take actions
An agent can call tools and APIs, retrieve information, and take several steps toward a goal. Asked to 'prepare a weekly report,' an agent might pull data from multiple systems, summarize it and post it — deciding along the way which tools to use.
That capability is powerful, and it's also why agents need boundaries: scoped access, approval steps and monitoring.
When to use each
If the job is answering questions, a well-grounded chatbot or assistant is often enough — and simpler to run. If the job is completing tasks across systems, an agent is the right tool.
Many real systems combine both: deterministic workflows with AI at specific decision points, and an agent only where multi-step autonomy adds real value.
