Agentic AI and AI agents are closely related, but they are not exactly the same thing. “AI agent” usually refers to a concrete system or instance that performs tasks autonomously, while “Agentic AI” describes a broader design approach or capability. In other words, an AI agent is something you build and run; Agentic AI is the architectural pattern that enables that agent to plan, act, and adapt over time.
An AI agent might be a specific bot that handles IT tickets or automates data cleanup. Agentic AI refers to the properties that make that bot capable: goal-driven behavior, multi-step reasoning, tool usage, memory, and feedback loops. You can build an AI agent that is not very agentic—for example, a simple scripted bot that always follows the same steps. Conversely, Agentic AI emphasizes autonomy and decision-making rather than fixed workflows.
This distinction matters when designing systems. If you think only in terms of “agents,” you might focus on individual bots. If you think in terms of Agentic AI, you focus on reusable components: planning logic, memory, retrieval, safety controls, and observability. For example, long-term memory is often implemented using embeddings stored in a vector database such as Milvus or Zilliz Cloud. That memory layer can be shared across multiple agents, reinforcing that Agentic AI is a system-level concept, not just a single process.