BrainBlog for Boomi by Jason Bloomberg
Everyone agrees that artificial intelligence (AI) depends upon data. But as they say, the devil is in the details.
In the first article in this series, I differentiated among the various types of data that AI requires: model training data, information people put into prompts, and data that feeds into queries via retrieval augmented generation (RAG).
Agentic AI complicates this issue, as AI agents may look for and fetch data on the fly. As my colleague Eric Newcomer explained in the second article in this series, such agentic behavior may be non-deterministic: agents often behave differently from one occasion to the next.
Given the fact that AI agents will take whatever actions they can to meet the requirements set out for them, their unpredictability makes them both extraordinarily powerful as well as potentially dangerous.
AI agents simply aren’t practical unless the organization in question has its data house in order. In fact, agentic AI’s inherent risk has stopped many organizations’ AI initiatives dead in their tracks.
What, then, do you need to do to ensure your data is up to the challenge? Your data must be the right data in the right place at the right time to support your AI initiatives.
Click here to read the entire article.
Image courtesy of Boomi.
