Agentic AI refers to AI systems that are designed to act as agents rather than passive responders. In simple terms, an Agentic AI system can take a goal, decide what steps are needed to achieve that goal, execute those steps using tools or APIs, observe the results, and adjust its behavior accordingly. Unlike a standard language model that only responds to a single prompt, Agentic AI operates in loops: plan → act → observe → decide again. This makes it suitable for tasks that require multi-step reasoning and ongoing interaction with external systems.
From a technical perspective, Agentic AI is usually built by combining a language model with additional components such as a planner, memory, tool interfaces, and a control loop. The language model handles reasoning and language understanding, while the surrounding system manages state, task decomposition, and execution. For example, an agent might receive a request to “investigate recent error logs and propose fixes.” The agent can break this down into steps, query logs, retrieve relevant documentation from a vector database such as Milvus or Zilliz Cloud, summarize findings, and generate recommendations.
Agentic AI is not about making models “sentient” or independent in a human sense. It is about giving software systems the ability to manage complex workflows autonomously within defined boundaries. Developers control what tools the agent can use, what data it can access, and what actions it can take. This makes Agentic AI a software architecture pattern rather than a single model or feature.