Agentic AI: Complex, Not Complicated
Understanding the difference between deterministic and non-deterministic systems is key to thriving in the new world of AI.
There is a lot of interest in and concern around the use of AI agents. For organizations considering agentic AI, it is recommended to view the model from the perspective of complex, rather than complicated, systems.
Accepting the fact that agentic AI is complex rather than complicated will be key to harnessing its power and applying necessary protections and controls.
The difference between complex and complicated systems is significant. Complicated systems, like those in computer science, involve cause-and-effect relationships from an engineering perspective. In contrast, complex systems, such as those in anthropology, involve multiple variables that cannot be controlled, requiring a focus on factors.
Author's summary: Agentic AI is complex, requiring a nuanced approach.
- Complex systems: multiple variables, focus on factors
- Complicated systems: cause-and-effect relationships, engineering perspective
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