From "𝐀𝐠𝐞𝐧𝐭𝐬" to "𝐀𝐠𝐞𝐧𝐭𝐢𝐜" Systems: AI Terminology Evolution

Evolution of AI terminology from binary 'agents' to spectrum-based 'agentic' systems

In recent AI news, articles, you might have noticed a subtle but significant shift in terminology - the increasing use of the word "agentic" instead of "agents." This transition reflects a deeper understanding of how AI systems operate and evolve.

𝐀𝐧𝐝𝐫𝐞𝐰 𝐍𝐠 clarified : "Rather than having to choose whether or not something is an agent in a binary way, I thought, it would be more useful to think of systems as being agent-like to different degrees. Unlike the noun “agent,” the adjective “agentic” allows us to contemplate such systems and include all of them in this growing movement."

This shift in terminology from "agents" to "agentic" represents three fundamental changes in our understanding:

First, it acknowledges that AI capabilities exist on a 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐬𝐩𝐞𝐜𝐭𝐫𝐮𝐦 rather than in discrete categories. Just as we recognize varying degrees of intelligence or autonomy, we can appreciate different levels of agentic behavior in AI systems.

Second, it promotes inclusivity in AI development. By moving away from binary classification, we create space for systems that exhibit different degrees of agent-like behavior. This encourages innovation at all levels, 𝐟𝐫𝐨𝐦 𝐬𝐢𝐦𝐩𝐥𝐞 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐭𝐨 𝐜𝐨𝐦𝐩𝐥𝐞𝐱 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐬𝐲𝐬𝐭𝐞𝐦𝐬.

Third, it better reflects the reality of AI system development. As Andrew Ng points out, "There's a grey zone between what clearly is not an agent (prompting a model once) and what clearly is (say, an autonomous agent that, given high-level instructions, plans, uses tools, and carries out multiple, iterative steps of processing)."

This perspective shift has practical implications for AI development, allowing teams to incrementally enhance their systems' agentic capabilities without being constrained by rigid definitions. It encourages a more nuanced and practical approach to building AI systems that can effectively serve their intended purposes."