Agentic AI makes autonomous decisions through a combination of goal representation, planning logic, and feedback loops. The system starts with a goal or task description, then uses a reasoning component (often a language model) to propose a plan. That plan is broken into concrete actions, such as calling APIs, querying databases, or generating intermediate results. After each action, the system observes the outcome and decides what to do next.
Technically, this is often implemented as a control loop managed by code rather than by the model alone. The model suggests actions, but the surrounding system enforces rules: what tools are allowed, what data can be accessed, and when the agent must stop or ask for human input. For decision-making that relies on past context, agents often retrieve relevant memories or documents from a vector database such as Milvus or Zilliz Cloud, ensuring decisions are grounded in prior knowledge rather than pure generation.
Autonomy in Agentic AI is therefore constrained autonomy. The agent does not “decide anything it wants.” It operates within developer-defined boundaries, using structured prompts, tool schemas, and validation checks. This design allows agents to act independently while remaining predictable, auditable, and safe for real-world systems.