Most evaluation methods for LLM systems still seem heavily tied to benchmarks like coding tests or static QA datasets. Those are useful, but they don’t really reflect how these systems behave once you put them into more dynamic environments. In real applications, agents are often using tools, making multi-step decisions, and working with context that changes over time. Failures in those situations also tend to be harder to reproduce or measure consistently. I’m curious how people working closer to applied systems are thinking about this. Is there any direction toward more standardized evaluation for agent behavior, or is this still something that varies too much between implementations? submitted by /u/Electrical_Mine1912
Originally posted by u/Electrical_Mine1912 on r/ArtificialInteligence