Anyone else tired of duct-taping tools together just to prep data for AI agents?

I keep running into this when building agents. The agent logic is usually the easy part. Then you hit real-world data like PDFs, emails, spreadsheets, scanned docs, etc. and suddenly you’re stitching together OCR, parsers, LLM calls, regex, schema validation, and random APIs just to get usable input. I’ve been playing with a simpler approach: raw files → describe what you’re trying to do + what the output should look like → get back cleaned / structured / validated data → hand it to the agent I don’t think everyone building agents should have to become a data engineer. For most agent workflows, the data work usually falls into a few buckets: clean/prep 、 chunk 、 generate tags/labels 、 generate Q&A pairs What I’m aiming for is pretty simple: upload the raw data, pick the task, explain in plain English how you want it handled and what you want back, and it does the messy data plumbing for you. For example: email + PDF → extract customer/order info → validate it → clean JSON → agent Basically, describe the end result instead of building the whole pipeline yourself. Anyone else dealing with this? How are you handling it right now? If anyone’s interested, I’d be happy to let you try it for free.