A new framework aims to simplify the increasingly complex world of agentic artificial intelligence, offering developers a guide to navigate the numerous tools and models available. Researchers from several institutions collaborated on the study, which categorizes agentic frameworks based on their focus areas and tradeoffs. The goal is to help developers choose the most suitable tools and strategies for their specific applications.

The study addresses a growing problem: the rapid expansion of agentic AI tools and frameworks has made it difficult for developers to select the right resources. This confusion can lead to delays and inefficiencies in building AI systems. The framework reframes the challenge for enterprise teams, shifting the focus from simply selecting a model to making architectural decisions. These decisions involve determining how to allocate training budgets, maintain modularity, and balance cost, flexibility, and risk.

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 like fine-tuning or reinforcement learning. Tool adaptation, on the other hand, focuses on how the agent interacts with and utilizes external tools or APIs.

According to the researchers, understanding these dimensions is crucial for developers. By considering the tradeoffs between agent and tool adaptation, developers can make more informed decisions about which frameworks to use. For example, a developer might prioritize agent adaptation if they need an AI system that can learn and adapt to new environments. Alternatively, they might focus on tool adaptation if they need an AI system that can effectively utilize a wide range of external resources.

The implications of this framework extend beyond the technical realm. As agentic AI becomes more prevalent, it will be increasingly important to understand its capabilities and limitations. This framework provides a valuable tool for policymakers and the public to engage in informed discussions about the ethical and societal implications of agentic AI. The study was published in December 2025.