I’ve been messing around with AI agents for CSV/data analysis lately and honestly the hardest part isn’t the analysis itself, it’s stopping the model from spiraling after one traceback error. Half the time the agent just keeps retrying the same broken Pandas fix over and over. What helped a bit for me was forcing the model to explain the error in plain English before generating new code. Weirdly that reduced a lot of the looping. I’ve also been testing workflows through Evose since managing multi-step agent state manually was getting pretty messy once tools and execution layers got involved. Still feels like “chat with your data” breaks pretty fast once datasets get large or the schema gets complicated. Curious how other people are handling retry logic or error correction without turning the whole thing into an overengineered orchestration project. submitted by /u/OneMessage4880
Originally posted by u/OneMessage4880 on r/ArtificialInteligence