Marvin 3.0 is a Python framework for building agentic AI workflows, where tasks are delegated to Large Language Model (LLM) agents . A core idea in Marvin is that each unit of work (a Task) should have a clear objective described by instructions, and one or more Agents specialized to

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