AI Agents vs Chatbots

Chatbots answer questions. Agents take actions. The distinction matters enormously for what you build.

These terms get used interchangeably and the confusion leads to bad architecture choices. Here is the clean distinction and why it matters for your project.

	AI Agent	Chatbot
What it does	Takes a goal, plans steps, calls tools, produces an outcome.	Takes a message, produces a response.
State	Maintains state across multiple steps and tool calls.	Usually stateless (or just conversation history).
Tool use	Core feature. Agents call APIs, write files, query databases.	Optional. Most chatbots just generate text.
Human in the loop	Optional. Can run fully autonomously or pause for approval.	Always — the human responds to every turn.
Failure modes	Loops, tool errors, scope creep, unpredictable execution.	Hallucination, off-topic responses, poor retrieval.
Cost per interaction	Higher. Multiple model calls + tool calls per task.	Lower. Usually one to three model calls per message.
Build complexity	High. Need orchestration, error handling, state, evals.	Low to medium. Prompt + retrieval usually sufficient.

Pick AI Agent when

Build an agent when: the task requires taking actions in external systems (writing, searching, updating data), not just answering questions.

Pick Chatbot when

Build a chatbot when: the task is answering questions, providing information, or drafting text — no external actions required.

Bottom line

Most "AI chatbot" requests turn out to actually need an agent once you dig into the workflow. Most "AI agent" requests should start as a chatbot until you hit the ceiling of what a chatbot can do.

Need help picking — or stitching them together?

We do this for clients every week. Bring us the workflow, we'll bring the architecture.

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Glossary