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.
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