New framework simplifies the complex landscape of agentic AI

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 adaptation

The researchers divide the landscape into two primary dimensions: agent adaptation and tool adaptation.

Agent adaptation involves modifying the foundation model that underlies the ...