Remediation
From "something broke" to "there's a PR ready to review" — in under 30 minutes.
Finding the bug and fixing the bug are two different jobs. Most tools stop at the first one. Aient doesn't.
When a problem is identified as actionable, Aient's AI agent assembles the full diagnostic picture — stacktraces, logs, affected sessions, relevant source files — and generates a targeted code fix. It opens a GitHub Pull Request linked back to the original problem. Engineers review, discuss, and merge on their own schedule. Nothing is ever auto-merged.
How it works
Full context assembly
Stacktraces, error logs, frequency data, and the source files most relevant to the crash are gathered before the agent writes a single line.
Targeted fixes
The generated PR addresses the specific problem, not a generic refactor. Aient reads your architecture and works within it.
Linked to the problem
Every PR traces back to the originating problem in the dashboard. The full thread — detection, triage, fix proposal — is visible in one place.
Human review required
Aient never merges code without an engineer's sign-off. The PR is a proposal, not a deployment.
On-demand or automatic
Critical problems trigger a fix attempt automatically. Any problem can also be sent to remediation manually from Slack or the dashboard.
Why it matters
MTTR — mean time to resolution — is one of the most direct measures of engineering reliability. The biggest chunk of that time isn't writing code. It's investigation: finding the crash, understanding the context, and building enough confidence to make a change. Aient handles that part.
The economics are straightforward: engineer time is expensive. Compute time is not. The gap between the two is where Aient's value lives.
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.