Search for incident response automation and you get security tooling. Phishing triage, SOAR playbooks, threat containment. That’s a real category, and it isn’t this one.
The incident response automation here is the other kind, the one that pages you at 2am. A production incident. Two systems stopped talking, no one knows why yet, and an engineer is opening a sixth browser tab.
Everyone landing here carries the same quiet worry. Automating incident response puts a machine near production while no one is watching. That worry is fair, and most vendors in this space talk around it.
So here’s ours up front. The most useful thing we can tell you about our own automation is what it refuses to do.
What is incident response automation?
Incident response automation is software doing the parts of an outage investigation that don’t need judgment. Pulling monitoring, logs, traces and deploy history at once. Correlating them. Ranking likely causes with the evidence attached. The judgment stays human. The tab-switching doesn’t.
Two different fields share the phrase, and mixing them up wastes time. Security incident response automation shuts threats down. Revoke the token, quarantine the mailbox, isolate the host. Known attacks, known fixes.
Production incident response automation is a different job. Something broke. No one knows what yet, and the right move hangs on an answer no one has.
Google’s SRE book on managing incidents covers the human half of that well. The machine half is newer. It’s also where the time goes.
What should you automate, and what shouldn’t you?
Automating diagnosis is safe, and automating remediation is a different question with a different risk. Reading and correlating changes nothing in production. Restarting a service changes plenty. Most people fold the two into one debate about whether automation is safe, which is why it never ends.
| Automating diagnosis | Automating remediation | |
| What it does | Reads each tool at once, ranks the causes, shows the evidence | Acts on production: restarts, rollbacks, scaling |
| Risk | Low. Nothing changes state | High. Changes are live and often hard to undo |
| Payoff | Large. This is the 20 to 45 minutes each incident wastes | Real, but only for faults you already know |
| Who calls it | The engineer, sooner | Needs a human first |
Make that split early. One half is plainly safe. The other half is a sign-off question, and treating them as one thing helps nobody.
Where do the 40 minutes actually go?
The slow part of a production incident is the gap between the alert and a first real hypothesis. Almost none of that gap is thinking. Datadog for the spike, GitHub for what shipped, Jaeger for the trace, Slack for who else noticed. Then a human holds all four in their head and hunts for the overlap.
That run takes 40 minutes and change. Watch it happen and the odd thing is how little skill it takes. It’s fetching. It’s tab-switching. It’s copying a timestamp out of one window and squinting at it in the next.
The engineer’s real skill gets maybe four of those minutes. The rest went on legwork.
Which is why what MTTR measures matters here. Most dashboards start the clock at ticket pickup, so the gap never lands in the number at all. The worst part of the incident goes unmeasured.
How does automated correlation actually work?
The agent reads monitoring, deploys, logs, traces, topology and past incidents in parallel, then ranks the causes at stated confidence levels. Each one carries a numbered evidence chain. That chain is the whole point. Without it you have a guess with a progress bar.
An engineer can’t argue with a guess. They can tear into a numbered evidence chain in ten seconds. That gap decides whether a tool gets trusted or quietly switched off.
Across 40 automated investigations in one 30-day window, 83% came back actionable at 70% confidence or better. That’s 33 of 40. Mean time from alert to a stated root cause was 41 seconds, against a 30-minute manual baseline. History caught the repeat offenders in 63% of cases, across 25 chronic incidents.
Forty is a small sample and we’ll say so. It’s also a real one, with the denominator printed next to every number. Correlation is only as good as what you already track.
Does automation replace the on-call engineer?
No, and the reason sits in the design rather than in a promise. Argus never gets direct system access for control. It reads, correlates, and briefs. Anything that writes stops at a confirmation gate, and ranked causes arrive as calls the engineer can throw out.
What changes is what the engineer walks into. The page still wakes them at 2am. But the context is already gathered, the recent deploys are lined up against the alert, and the last two times this signal fired are on screen.
They arrive at a briefing instead of a mystery.
It matters even more for the engineers who aren’t senior yet. Automation that acts in silence teaches nobody. Automation that shows its workings shows a junior engineer how blast radius gets judged, each time it runs, at 2am, when nobody senior is awake to walk them through it.
What does it cost to run?
Those 40 investigations cost $0.0105 each to run. In that window, 27 minutes of AI processing stood in for about 20 hours of manual engineer time. The sum on the other side is the real case. Do it on your own numbers.
Take the default case: 40 incidents a month, 30 minutes of diagnosis each, two engineers pulled in, at $90 an hour fully loaded. That comes to 480 engineer-hours and $43,200 a year. Before anybody fixes a thing.
Measured cuts in diagnosis time across deployments run 40 to 70%. Set that against $43,200 and you get back $17,280 to $30,240 a year.
The price of the agent isn’t what decides this. The price of the 480 hours is.
Where automation doesn’t help
Automation squeezes incidents that look like ones you’ve had before, and does little for the rest. A truly new fault has no history to match against. The agent still offers a cause, still attaches evidence, and is still wrong in a way that reads as solid.
Incidents where the tooling itself is down are worse. If Datadog is the outage, nothing downstream of Datadog can diagnose it.
Then there’s the class no software should touch. Whether to fail over to the second region at 3am on the last day of the quarter is a business call about risk. That needs a person with the power to make it. It also needs a managed service contract that names who that person is at 3am.
Bring us one of your incidents
Diagnosis-first automation is worth seeing against a real fault rather than a demo. Argus runs on your own incident history, your own stack, your own noise. Bring one incident. We’ll run it live and show you the briefing, the ranked causes, and every piece of evidence behind them.
Frequently asked questions
Is incident response automation the same as SOAR?
No. SOAR automates security incident response: phishing triage, endpoint containment, token revocation. Production incident response automation handles broken systems and broken integrations, where the right move hangs on a diagnosis nobody has yet. Same plumbing, very different idea of a correct action.
Is automated incident response safe?
Automating diagnosis is safe, because reading and correlating changes nothing. Automating remediation is a different question with a different risk. Folding the two into one debate is why the whole thing stays stuck, and why plenty of teams end up automating neither of them.
Does incident response automation replace engineers?
No. It replaces tab-switching. The engineer who used to spend 40 minutes gathering context now spends 41 seconds reading it, then makes the same call on better evidence. The judgment doesn’t move.
How much does automated incident investigation cost?
Ours runs at about a cent per investigation. The number that matters is what the manual version costs over a year. Forty incidents a month, 30 minutes each, two engineers: that’s roughly 480 engineer-hours a year, or $43,200, before a single fix starts.