Agentic AI differs from traditional chatbots primarily in control flow and responsibility. A traditional chatbot follows a simple request-response pattern: the user asks a question, the system generates an answer, and the interaction ends. Even when chatbots support conversation history, they typically do not take initiative, perform multi-step actions, or manage long-running tasks. Agentic AI systems, in contrast, are designed to pursue goals over multiple steps and interactions.
In practical terms, a chatbot answers questions like “What does this error mean?” An Agentic AI system can be given a goal like “diagnose why this error keeps happening and suggest a fix.” To do that, it may decide to fetch logs, search internal documentation, retrieve similar past incidents using a vector database such as Milvus or Zilliz Cloud, compare patterns, and then propose actions. The key difference is that the agent decides what to do next, not just what to say next.
From an implementation standpoint, traditional chatbots are often stateless or lightly stateful, while Agentic AI systems are explicitly stateful. They maintain memory, track progress, and reason about unfinished tasks. This makes Agentic AI more complex to build and test, but also far more capable for workflows like automation, analysis, and decision support.