Mean time to recovery (MTTR) is the average time from when something breaks to when it's working again. It's one of the few reliability numbers that both engineers and the business care about, because it maps directly to how long users are hurting.
The usual instinct when MTTR is too high is to add visibility: another dashboard, another tool, another alert. More often, the time isn't lost for lack of data. It's lost getting from the data to an understanding.
Split any incident into stages and look at where the minutes went:
For many teams, "understand" is the longest stage by far. The fix, once you know what it is, is often a rollback or a config change that takes minutes. The time goes into opening tools, lining up timelines and ruling out causes. If you want to move MTTR, that's the stage to attack first.
Error rate and latency on the services users touch catch real problems with far less noise than alerts on every possible cause. Fewer, better alerts also mean the on-call engineer starts every incident with attention to spare.
Every incident starts with the same checks: what changed, which deploy, are there new errors, is it one service or many. These don't need a human to run them. Whether it's a script, a runbook or an AI SRE agent, having the answers ready when the alert lands removes the slowest part of "understand". We wrote a whole post on answering "what changed?".
A finding that lives in a dashboard nobody has open doesn't help. Posting it in the incident channel means everyone starts from the same facts instead of each person investigating separately.
If a rollback takes a ticket, an approval chain and a manual pipeline run, it won't happen quickly at 3am. Decide in advance which fixes are safe, who can approve them, and make them a single step.
An incident marked resolved while the metric is still bad comes back as a second incident, and it counts twice in your MTTR. Watch the metric after the fix and close the incident only when it's actually back to normal.
The fastest incident is the one that doesn't happen again. Track problems that keep coming back on the same service, and turn each incident into a short postmortem with concrete action items while the details are fresh.
MTTR is easy to game by closing incidents early or by not counting the small ones. Measure from the first sign of the problem to verified recovery, include every incident, and compare it over time against your own baseline rather than an industry number. The trend matters more than the absolute value.
Cova is aimed squarely at the "understand" stage. It watches error rate and latency on your services, and when one goes wrong it finds the deploy behind it, checks for new errors and other affected services, and posts what it found in Slack with the evidence. After a fix, it watches the metric and only marks the incident resolved once it has recovered.
Between incidents, Cova watches every production release for regressions, flags problems that keep coming back on the same service, and drafts the postmortem when an incident closes. Its impact report shows your MTTR over time, measured from Cova's own records.
Spend less of every incident gathering context.
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