The framework addresses a growing problem in the AI community: the rapid proliferation of agentic tools has made it difficult for developers to determine the best solutions for their specific needs. This complexity can lead to analysis paralysis, hindering innovation and slowing down the development of new AI-powered applications.

The researchers identified two primary dimensions for categorizing agentic frameworks: agent adaptation and tool adaptation. Agent adaptation involves modifying the foundation model that underpins the agentic system. This can be achieved by updating the agent's internal parameters or policies through methods such as fine-tuning or reinforcement learning. Tool adaptation, on the other hand, focuses on modifying the tools that the agent uses to interact with the environment. This can involve creating new tools, modifying existing tools, or developing strategies for the agent to effectively utilize available tools.

According to the researchers, this reframing shifts the focus for enterprise teams from simply selecting a model to making architectural decisions. These decisions involve determining how to allocate training budgets, how much modularity to maintain, and what tradeoffs to accept between cost, flexibility, and risk.

The rise of agentic AI reflects a broader trend toward more autonomous and intelligent systems. Agentic AI systems are designed to perceive their environment, reason about their goals, and take actions to achieve those goals. These systems have the potential to revolutionize various industries, including healthcare, finance, and transportation. However, the complexity of developing and deploying agentic AI systems presents a significant challenge.

The new framework seeks to address this challenge by providing a structured approach to understanding and navigating the landscape of agentic AI. By categorizing frameworks based on their adaptation strategies, the researchers hope to empower developers to make more informed decisions and accelerate the development of innovative AI applications. The study also highlights the importance of considering the tradeoffs between different approaches, such as the cost of training a model versus the flexibility of using a modular system. The researchers plan to continue refining the framework and exploring new dimensions for categorizing agentic AI systems. They also hope to collaborate with industry partners to validate the framework in real-world applications.