Chatbots optimize conversation
A chatbot usually answers questions, summarizes information or guides a user through a known flow. Its main interface is text and the risk of a wrong answer is often limited to user experience.
RAG, citations and structured responses can make chatbots much more useful without giving them write access.
Agents add action capabilities
An agent can call tools, update systems, create records, trigger workflows or coordinate multi-step tasks.
Once the system can act, authorization, idempotency, rollback and audit become first-class requirements.
Workflow state changes the architecture
Agents often need to remember what has already happened in a process and what step comes next. This state should live in application logic, not only in conversation history.
Explicit state machines make retries, approvals and recovery predictable.
Evals must match the pattern
For chatbots, groundedness and usefulness may dominate evaluation. For agents, tool selection, argument accuracy, policy violations and final action outcome matter.
A single generic quality score hides the failure modes that operations teams actually care about.
Choose the simplest pattern that solves the job
If the product only needs information retrieval, start with a strong assistant. Add agentic write capabilities only where they create measurable value.
This reduces cost, attack surface and operational complexity.
Decide from action risk, not AI fashion
List the user jobs the product must complete and mark which ones require external state changes. If most value comes from finding, explaining or summarizing information, a strong assistant may be enough. If the system must execute multi-step actions, an agent architecture becomes justified.
This decision matters because every action capability expands the security, testing and support surface. The best product is not the most autonomous one; it is the one that creates the required outcome with the smallest reliable operating model.