Agentic AI

Agentic AI refers to artificial intelligence systems designed to autonomously pursue goals over time, often through a sequence of decisions or actions, without constant human prompting.

Key characteristics of agentic AI:

• Goal-directed: The system is given (or can infer) an objective and acts toward achieving it.
• Autonomous decision-making: It determines its next actions based on the current context, often across multiple steps.
• Memory and state: It maintains awareness of past actions and current environment state.
• Planning and reasoning: It can break down tasks into subtasks, replan if conditions change, and adjust strategies dynamically.

Examples in context:

• A coding assistant that not only completes a function but creates, tests, and debugs an entire module based on a high-level goal.
• An AI agent that autonomously browses websites, gathers information, and books travel—rather than responding to discrete queries.

Agentic AI is compute-intensive, often requiring long-running, low-latency inference and persistent memory—making it a strong use case for robust model hosting, edge deployment, and API orchestration.