Today, I'm excited to announce Confident AI's 1st Launch Week in 2026. This week, we're going to do 5 days of launches to show something new everyday — and we're kicking it off with a feature we've been working on for quite some time.
Launch Week Day 1 (1/5): Error Analysis, Fully Automated.
What is Error Analysis?
Error analysis is the process of identifying failure modes in your LLM app's production traces through human review. You look at real outputs, figure out where and why things are going wrong, and use those findings to pick the right evaluation metrics to monitor going forward. It's the critical step between "my LLM app is live" and "I actually know what's breaking and how to catch it automatically."
The Problem
If you've ever tried to set up error analysis for your LLM app in production, you know the pain. The typical workflow looks something like this:
- Pull traces from production. You write some code to export your traces, maybe from a logging pipeline or an observability tool, into a format you can actually work with.
- Manually analyze them. You scroll through hundreds of traces trying to spot patterns — why is the model hallucinating on this type of query? Why does it keep refusing to answer questions about pricing?
- Hack together an LLM to recommend metrics. You prompt GPT-4 with a bunch of failure examples and ask it to suggest what metrics you should track. Maybe it gives you something useful, maybe it doesn't.
- Deploy those metrics to production and hope for the best. You wire up the recommended metrics, start running them on live traffic, and cross your fingers that they actually catch the failure modes you care about.
The result? A fragile, ad-hoc process that takes days to set up, produces metrics you're never fully confident in, and breaks the moment your failure modes shift — which they always do.
I've watched teams go through this exact loop over and over. The more users I talked to, the more I realized that error analysis wasn't a tooling problem — it was a workflow problem. The pieces existed, but no one had stitched them together into something that actually works end-to-end.
The Before vs. After
Before: You're a detective working with a clipboard and a magnifying glass. Pull traces in code. Eyeball them. Write a hacky LLM script to recommend metrics. Deploy to prod. Pray.
After: You do error analysis directly on Confident AI — inside annotation queues — and we handle the rest.
Here's what the workflow looks like now:
- Queue your traces, spans, or threads. Pick the production data you want to analyze and add it to an annotation queue on Confident AI.
- Annotate them. Your team reviews and labels the traces — marking failure modes, flagging issues, categorizing errors.
- Done.
Error analysis inside annotation queues on Confident AI
That's it. Confident AI takes your annotations, runs error analysis directly on the platform, and recommends the right set of metrics based on the failure patterns your team identified. But here's the part that matters most — it also finds the alignment rate between the recommended metrics and your human annotations, so you can be sure that the metrics you run in production continuously are as accurate and reliable as they can be.
Recommended metrics with alignment rate against human annotations
No more guessing. No more "let's try this metric and see if it correlates with what users are complaining about." You get metrics that are validated against your own team's judgment before they ever touch production.
Why This Matters
The gap between "we have observability" and "we actually understand why our LLM is failing" is enormous. Most teams I talk to have tracing set up. They can see what's happening. But identifying why things are going wrong and translating that into reliable, automated monitoring? That's where everyone gets stuck.
Error analysis is the bridge between observability and actionable evaluation. And until now, building that bridge has been a manual, error-prone process that most teams either half-ass or skip entirely.
With this launch, error analysis on Confident AI is fully automated — from annotation to metric recommendation to alignment validation. The entire loop, closed.
What's Next
This is Day 1 of 5. We've got four more launches coming this week, and each one builds on this same philosophy: take the workflows that teams are doing manually today and make them seamless on Confident AI.
Stay tuned — and if you want to try error analysis for yourself, sign up for Confident AI and start queuing your traces today.
Do you want to brainstorm how to evaluate your LLM (application)? Ask us anything in our discord. I might give you an "aha!" moment, who knows?