As Artificial Intelligence (AI) continues to evolve, the concepts of AI Agents and Agentic AI have emerged as transformative paradigms. While often used interchangeably, these terms represent different levels of autonomy, adaptability, and decision-making in intelligent systems. This document explores both concepts in depth, compares their fundamental characteristics, and examines their real-world applications and future implications.

What is an AI Agent?

An AI Agent is a software-based system capable of perceiving its environment, processing inputs, and performing actions to achieve specific goals. It follows a sense-think-act cycle.

Characteristics:

• Reactive or Proactive behaviour.
• Goal-driven and task-specific
• Can be rule-based or powered by machine learning
• Often operates within a narrow domain

Components:

• Sensors: To perceive the environment (data inputs)
• Decision Engine: To analyse inputs and select actions
• Actuators: To execute actions or responses.

What is Agentic AI?

Agentic AI refers to systems that exhibit agency, meaning they can autonomously form goals, make long-term decisions, and self-improve. These systems are not just responsive but can plan, adapt, and act with a higher degree of autonomy.

Characteristics:

• Long-term planning
• Autonomy in goal selection
• Environment modelling and learning
• Ability to self-reflect or adapt strategies
• Higher level of independence from human instructions

Philosophical Perspective:

The term “agentic” emphasizes intentionality and self-directedness—qualities traditionally associated with human cognition.