Agentic AI represents a progression from reactive AI, enabling systems with agency to perceive environments, set goals, plan, and learn autonomously. Unlike reactive AI, agentic AI can independently solve complex tasks using advanced reasoning and planning. The article explores the fundamental differences between agentic and reactive AI systems, outlines their key components, like autonomy, goal-setting, and memory, and discusses the challenges they pose, including safety and ethical questions. A tutorial guides readers on building simple agentic AI systems using Python.
Table of contents
Agentic vs Reactive AIKey Components of AI AgencyHow Does Agentic AI Know What to Do?So What’s the Current State of Agentic AI?Building Agentic AI: Frameworks and ApproachesMajor Challenges of Agentic AICode Snippet and Real-World ExamplesTutorial: Build Your First Agentic AI with PythonConclusionSort: