What is agentic AI and how does it differ from traditional AI?

Agentic AI refers to artificial intelligence systems capable of autonomous decision-making, planning, and execution without continuous human intervention. Unlike traditional AI, which follows predefined tasks, agentic AI proactively sets goals, strategizes, and adapts to new situations. It mimics human-like problem-solving and independent thinking, making it highly effective in complex environments like robotics, financial forecasting, and autonomous systems.

For example, AI-powered trading bots analyze financial markets and make investment decisions without manual input. Similarly, self-driving cars use agentic AI to navigate unpredictable road conditions. These systems integrate machine learning, reinforcement learning, and real-time decision-making to enhance adaptability. However, challenges include ethical concerns, unpredictability, and the need for advanced safety measures.

Key takeaways:

• Operates autonomously without constant human oversight.

• Learns, plans, and adapts in real-time.

• Used in robotics, finance, and self-driving technology.

• Challenges include ethical concerns and unpredictability.