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 and tradeoffs, VentureBeat reported Dec. 29, 2025.
The proliferation of agentic tools has made it difficult for developers to choose the right resources for their AI applications. The framework addresses this challenge by reframing agentic AI for enterprise teams. Instead of solely focusing on model selection, the framework encourages developers to consider architectural decisions, such as how to allocate training budgets, maintain modularity, and balance cost, flexibility, and risk.
The study divides the agentic AI landscape into two primary dimensions: agent adaptation and tool adaptation. Agent adaptation involves modifying the foundation model that underlies the agentic system, updating its internal parameters or policies through methods like fine-tuning or reinforcement learning. Tool adaptation, on the other hand, focuses on enhancing the tools that the agent uses to interact with its environment. This can involve improving the tools' functionality, optimizing their integration with the agent, or developing new tools tailored to specific tasks.
According to the researchers, understanding these two dimensions is crucial for developers to make informed decisions about which frameworks and tools to use. By considering the tradeoffs between agent and tool adaptation, developers can choose the strategies that best suit their specific needs and goals.
The development of agentic AI has significant implications for society. Agentic systems have the potential to automate complex tasks, improve decision-making, and personalize user experiences. However, they also raise concerns about job displacement, algorithmic bias, and the potential for misuse. As agentic AI continues to evolve, it is important to address these ethical and societal implications to ensure that these technologies are used responsibly and for the benefit of all.
The researchers plan to continue refining the framework and exploring new dimensions of agentic AI. They hope that their work will help to foster a more informed and collaborative community of agentic AI developers and researchers.
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