Triage
Know what broke, why it broke, and what to do next — before you open the ticket.
A raw stacktrace tells you something crashed. It doesn't tell you how serious it is, which part of the codebase to look at, or what to do first. That analysis — the first 30–60 minutes of any incident — is where engineering time disappears.
Aient's triage system runs that analysis automatically. Every detected problem passes through a layered AI pipeline that reads your actual source code, not just the error message.
How it works
Three-tier model cascade
A fast model handles initial classification. For complex or high-impact problems, a more capable model is brought in with full code context. A critic model reviews critical or low-confidence results.
Code context injection
The AI sees the actual source lines around the crash location, not a generic description of the error type.
Structured output
Every triage produces a title, summary, category, severity, priority (P0–P3), actionability rating, confidence score, remediation hints, and owner suggestions.
Human override
Any AI decision can be overridden. Teams can agree, disagree, and adjust — and that feedback improves future triage results.
Why it matters
Engineers pick up a problem with context already assembled. First-response time drops. Senior engineers stop spending time on initial triage for routine errors. The system learns from your team's corrections over time, capturing institutional knowledge that usually lives only in people's heads.
For smaller teams, it means you don't need to be an expert in every part of your stack. Aient reads the code and tells you what went wrong — with enough context to act, not just to worry.
Early access
Ready to close the loop?
We're running a private alpha with a small group of engineering teams. If you want AI-driven remediation for your production issues, we'd love to have you.
Get early accessNo commitment required. We'll reach out within 48 hours.