AI agents are autonomous software systems designed to perform specific, goal-oriented tasks. They utilize tools like APIs and databases and are often built on large language models such as GPT-4. Their primary functions include areas like customer service, scheduling, and internal search. Unlike traditional generative AI, AI agents not only respond to prompts but also plan and act based on predefined user goals. For example, they have been shown to reduce customer support ticket resolution time by over 40% and improve internal knowledge retrieval accuracy by 29%.

Agentic AI represents a more advanced framework that consists of multiple specialized agents working together, coordinated by a central orchestrator. This system excels in complex environments that require dynamic planning and inter-agent negotiation. For instance, in a research lab, a multi-agent system can collaboratively write grant proposals by retrieving documents, summarizing literature, and aligning objectives, significantly reducing the time needed to produce drafts. This coordinated approach allows for concurrent execution and strategic adaptability, making it suitable for applications like supply chain optimization and autonomous robotics.

Both AI agents and Agentic AI encounter several challenges. AI agents may struggle with issues like hallucinations, brittleness in prompt design, and limited context retention. On the other hand, Agentic AI faces challenges related to coordination failures, unpredictability, and explainability. Despite these hurdles, ongoing advancements are being made to address these issues, paving the way for more effective and reliable AI systems in the future.