Introduction Large Language Models (LLMs) have opened new frontiers in AI applications, but leveraging them effectively requires more than just prompting. Developers need ways to orchestrate context-aware reasoning and memory – feeding the right data to the model at the right time, and enabling multi-step decision making with tools or knowledge

The key difference between Artificial General Intelligence (AGI) and Artificial Intelligence (AI) lies in the scope and flexibility of their capabilities:

Agentic AI Engineering refers to the design and development of AI systems capable of autonomously making decisions, initiating actions, and achieving goals in dynamic, real-world environments. These systems exhibit agency, meaning they operate beyond simply responding to user prompts or predefined tasks. They leverage long-term memory, reasoning, and goal-setting capabilities