Native agent models differ from modular agent frameworks because workflow knowledge is embedded directly within the agent’s model through orientational learning [12]. Tasks are learned and executed in an end-to-end manner, unifying perception, reasoning, memory, and action within a single, continuously evolving model [12]. This approach is fundamentally data-driven, allowing for seamless adaptation to new tasks, interfaces, or user needs without relying on manually crafted prompts or predefined rules [12].

Frameworks are design-driven, and lack the ability to learn and generalize across tasks without continuous human involvement [12]. Native agent models lend themselves naturally to online or lifelong learning paradigms [12]. By deploying the agent in real-world GUI environments and collecting new interaction data, the model can be fine-tuned or further trained to handle novel challenges [12].


What differentiates native agent models from modular agent frameworks?

Related Content You May Like