Droplox

Generation tools are low-stakes when they're wrong draft an email, get a bad draft, edit it. Judgment tools are different: grading, flagging, scoring. If the output looks confident and it's actually a guess, someone might act on it without knowing that.

Working on letsflw's exam-checker made this concrete objective questions are easy (exact match, no ambiguity), but for open-ended answers we made a call to surface the AI's mark as a suggestion to review, not a final score, because presenting it as settled felt dishonest.

Hi all, there's a distinction I keep thinking about: apps built with AI (Cursor, Claude, Copilot writing the code) vs apps that depend on AI APIs at runtime to function.

I'm in the first camp. I used AI-assisted coding heavily to build Briskly (a cleaning app that scans your room and generates a timed task list), but the app itself makes zero LLM API calls. The scanning runs on Apple's Vision framework, entirely on-device. No tokens, no per-user inference costs, no latency, works offline, and my privacy label is basically empty.

Every production AI system eventually encounters failures. The challenge isn't just fixing them it's understanding why they happened in the first place.

I'm interested in how teams investigate failures after deployment.

Do you collect execution traces, save intermediate reasoning steps, analyze tool calls, or build custom debugging dashboards?

What has made the biggest difference in helping your team identify the root cause of difficult AI agent failures?