A new framework aims to simplify the increasingly complex world of agentic artificial intelligence, offering developers a guide to navigate the expanding ecosystem of tools and models. Researchers from several institutions collaborated on the study, which categorizes agentic frameworks based on their focus areas and trade-offs, VentureBeat reported Dec. 29, 2025.

The study addresses the growing challenge developers face in selecting the appropriate tools for their AI applications, as the sheer number of options can lead to confusion and indecision. The framework reframes agentic AI for enterprise teams, shifting the focus from model selection to architectural decisions concerning training budgets, modularity, and trade-offs between cost, flexibility, and risk.

Researchers identified two primary dimensions within the agentic AI landscape: agent adaptation and tool adaptation. Agent adaptation involves modifying the foundation model that underpins the agentic system, updating its internal parameters or policies through methods such as fine-tuning or reinforcement learning. Tool adaptation, on the other hand, focuses on enhancing the tools available to the agent, allowing it to interact more effectively with its environment and accomplish tasks.

The framework's development comes at a crucial time, as agentic AI systems are gaining traction across various industries. These systems, designed to autonomously perceive, reason, and act in complex environments, hold the potential to revolutionize fields such as robotics, healthcare, and finance. However, the complexity of building and deploying these systems has been a significant barrier to entry for many organizations.

By providing a structured approach to understanding and selecting agentic AI tools, the new framework aims to democratize access to this technology and accelerate its adoption. The researchers hope that this work will empower developers to build more effective and efficient AI systems, ultimately leading to broader societal benefits. The study is expected to spur further research into the development of more robust and adaptable agentic AI frameworks.