brain Blog Post for Boomi by eric newcomer
An old Chinese proverb says that every journey of a thousand miles starts with a single step.
When starting out, adopting agentic AI can seem like a journey of a thousand miles, at least as you try to keep up with the latest and greatest industry innovations.
Some agentic AI promoters go to the extreme, asking you to believe that AI agents will replace human employees and act without supervision to carry out any task you can think of.
Others in the industry are more pragmatic, drawing a clear distinction between traditional deterministic and modern probabilistic agentic outcomes, focusing on what each approach is good for.
Traditional IT systems – that is, what everyone used before the advent of generative AI – are deterministic. They produce precise and repeatable results because they use binary computer code with a single mathematical interpretation.
Generative AI systems, on the other hand – including agentic AI – are probabilistic because they use statistical matches (instead of exact matches) to generate responses to human language prompts.
It therefore makes sense to take the first step in working with agentic AI by picking a use case that clearly fits in the “non deterministic” or “probabilistic” category, such as a research project, analyzing and summarizing data, or requires context-awareness, rather than trying to replace a deterministic process.
