From SiliconANGLE
AI agents are practical. Reliability is another matter.
By Paul Gillin
Building artificial intelligence agents that can interact with each other reliably across services presents technical challenges that have never been tackled before. According to one AI researcher, that will limit their use to a narrow set of business processes for the next few years.
Niloufar Salehi (pictured at right), an assistant professor at the University of California at Berkeley, said traditional data processing systems are built to deliver predictable results. But nothing is predictable about machine learning. The same algorithm may produce entirely different results depending on context.
Building trustworthy agentic AI systems involves solving problems that have never before existed. “The way databases work is that you have a data schema and you know what sort of things are there, like accounts and deals,” she said. “What agents are doing is much more unstructured and adapts over time. It’s a completely different way of thinking about data structure and schemas, and we don’t have the right way to build that yet...”
Niloufar Salehi is an assistant professor in the School of Information.
