Agentic X
The notion of a ๐๐ฝ๐ฒ๐ฐ๐๐ฟ๐๐บ ๐ผ๐ณ ๐ฎ๐ด๐ฒ๐ป๐ฐ๐ with ๐๐ฎ๐ฟ๐๐ถ๐ป๐ด ๐ฑ๐ฒ๐ด๐ฟ๐ฒ๐ฒ๐ ๐ผ๐ณ ๐ฎ๐๐๐ผ๐ป๐ผ๐บ๐ and imbedded in applications gives rise to something I like to refer to as ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐ซ. Where applications and interfaces are imbued with different levels of agency.
AI Agents are broadly defined as software entities capable of perceiving and acting upon their environment.
However, in the era of ๐๐ฎ๐ฟ๐ด๐ฒ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐ (๐๐๐ ๐), the term ๐๐ด๐ฒ๐ป๐ has taken on a narrower meaning, as even simple systems like thermostats would qualify as agents under the traditional definition.
Researchers have sought to formalise a shared understanding of what constitutes an ๐๐ ๐๐ด๐ฒ๐ป๐ ๐ถ๐ป ๐น๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ-๐บ๐ผ๐ฑ๐ฒ๐น-๐ฏ๐ฎ๐๐ฒ๐ฑ ๐๐๐๐๐ฒ๐บ๐, often framing it as a ๐๐ฝ๐ฒ๐ฐ๐๐ฟ๐๐บ ๐ผ๐ณ โ๐ฎ๐ด๐ฒ๐ป๐๐ถ๐ฐโ behaviour rather than a binary state.
This perspective emphasises ๐๐ฎ๐ฟ๐๐ถ๐ป๐ด ๐ฑ๐ฒ๐ด๐ฟ๐ฒ๐ฒ๐ ๐ผ๐ณ ๐ฎ๐๐๐ผ๐ป๐ผ๐บ๐ & functionality within systems rather than rigid classifications.
Instead of proposing a new definition, we should rather identify factors that contribute to an AI system being perceived as more agentic.
These factors can be grouped into four main clusters as seen in the image, which together provide a nuanced framework for understanding the evolving concept of agency in AI.
Chief Evangelist @ Kore.ai | Iโm passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
