An AI Odyssey, Part 1: Correctness Conundrum

I recently talked with a contact who repeated what he'd heard regarding agentic AI systems—namely, that they can greatly increase productivity in professional financial management tasks. However, I pointed out that though this is true, these tools do not guarantee correctness, so one has to be very careful letting them manage critical assets such as financial data. It is widely recognized that AI models, even reasoning models and agentic systems, can make mistakes. One example is a case showing that one of the most recent and capable AI models made multiple factual mistakes in drawing together information for a single slide of a presentation. Sure, people can give examples where agentic systems can perform amazing tasks. But it's another question as to whether they can always do them reliably. Unfortunately, we do not yet have procedural frameworks that provides reliability guarantees that are comparable to those required in other high-stakes engineering domains. Many leading researchers have acknowledged that current AI systems have a degree of technical unpredictability that has not been resolved. For example, one has recently said, "Anyone who has worked with AI models understands that there is a basic unpredictability to them, that in a purely technical way we have not solved."

What industrial-strength reliability looks like Manufacturing has the notion of Six Sigma quality, to reduce the level of manufacturing defects to an extremely low level. In computing, the correctness requirements are even higher, sometimes necessitating provable correctness. The Pentium FDIV bug in the 1990s caused actual errors in calculations to occur in the wild, even though the chance of error was supposedly "rare." These were silent errors that might have occurred undetected in mission critical applications, leading to failure. This being said, the Pentium FDIV error modes were precisely definable, whereas AI models are probabilistic, making it much harder to bound the errors. Exact correctness is at times considered so important that there is an entire discipline, known as formal verification, to prove specified correctness properties for critical hardware and software systems (for example, the manufacture of computing devices). These methods play a key role in multi-billion dollar industries. When provable correctness is not available, having at least a rigorous certification process (see here for one effort) is a step in the right direction.

It has long been recognized that reliability is a fundamental challenge in modern AI systems. Despite dramatic advances in capability, strong correctness guarantees remain an open technical problem. The central question is how to build AI systems whose behavior can be bounded, verified, or certified at domain-appropriate levels. Until this is satisfactorily resolved, we should use these incredibly useful tools in smart ways that do not create unnecessary risks.