Our own experience running internal agents taught us that the best remediation comes from providing the LLMs with the maximum and most accurate context possible. Robust evaluations are also critical to measure accuracy, detect regressions, and improve. But there is no silver bullet.
SOTA LLMs are increasingly better at generating SQL and notoriously bad with math and numbers in general. Combining them with powerful querying capabilities bridges that gap and makes the overall experience an useful one.
IMO, we'll always have to deal with the stochastic nature of these models and hallucinations, which calls for caution and requires raising awareness within the user base. What I found watching our users internally is that, while it's not magical, it allows users to request data more often, and compounds in data-driven decision-making, assuming the users are trained to interpret the interactions