Remediation
From a production problem to a pull request your team can review.
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. Merge automation is off by default and requires approval of the exact change when enabled.
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.
Get started
Ready to close the loop?
Create your account, connect a repository, and bring your production telemetry into Aient.
Get startedChoose a plan and connect your first service during setup.