AI SRE
The AI SRE that stops the burn before your SLO breaks
The SteadyNines AI SRE works alongside your SREs. When an SLO starts burning, it investigates through the Context Graph, proposes the safest action, carries it out once approved, and writes the incident record as it goes.
Deploy #4821 (issuer retries) went out at 02:11. Error logs, retry volume and deploy time line up.
Roll back card-auth-api to #4820
Expected burn after: < 1×.
Actions it can take.
Roll back a deployment
Return a service to its last good version when a release starts the burn.
Scale out or up
Add pods, instances or capacity when load is what is spending the budget.
Restart a failing component
Recycle a worker, pod or process that has stopped behaving.
Fail over
Move traffic to a healthy replica or region while the cause is found.
Apply a known patch or config fix
Reuse a fix that worked before, from the situation record.
Toggle a feature flag
Switch off the change behind the burn without a redeploy.
What an approval looks like.
The card your on-call engineer gets in Teams: the burning SLO and its burn rate, the likely cause and evidence, the proposed action, the expected effect, one tap to approve, and a log entry for all of it.
- 1Burning SLO and burn rate
- 2Likely cause and evidence
- 3Proposed action
- 4Expected effect
- 5One tap to approve
- 6Logged
Starts earlier, finishes further.
Most AI SRE tools start once an incident is paged. The SteadyNines AI SRE starts on burn rate, then hands the cause to the AI Engineer, so the problem is fixed in code and not only patched in production.
Where the work starts and ends
- Starts on: error budget burn, before breach
- Stops the burn: with an action your team approves
- Ends at: a reviewed code fix, not a restart