With the ecosystem of agentic tools and frameworks exploding in size, navigating the many options for building AI systems is becoming increasingly difficult, leaving developers confused and paralyzed when choosing the right tools and models for their applications.In a new study, researchers from multiple institutions present a comprehensive framework to untangle this complex web. They categorize agentic frameworks based on their area of focus and tradeoffs, providing a practical guide for developers to choose the right tools and strategies for their applications.For enterprise teams, this reframes agentic AI from a model-selection problem into an architectural decision about where to spend training budget, how much modularity to preserve, and what tradeoffs they’re willing to make between cost, flexibility, and risk.Agent vs. tool adaptationThe researchers divide the landscape into two primary dimensions: agent adaptation and tool adaptation.Agent adaptation involves modifying the foundation model that underlies the agentic system. This is done by updating the agent’s internal parameters or policies through methods like fine-tuning or reinforcement learning to better align with specific tasks.Tool adaptation, on the other hand, shifts the focus to the environment surrounding the agent. Instead of retraining the large, expensive foundation model, developers optimize the external tools such as search retrievers, memory modules, or sub-agents. In this strategy, the main agent remains “frozen” (unchanged). This approach allows the system to evolve without the massive computational cost of retraining the core model.The study further breaks these down into four distinct strategies:A1: Tool execution signaled: In this strategy, the agent learns by doing. It is optimized using verifiable feedback directly from a tool’s execution, such as a code compiler interacting with a script or a …
