Context on arrival
Recent changes, implicated services and prior occurrences, assembled before the responder logs in.
Use case
AI employees for on-call and incident response.
Most of an incident is not clever debugging. It is recall — has this happened before, what did we do about it, and what changed in the last twelve hours. An AI employee that remembers every past incident and every deployment can do that part immediately, propose the fix, and draft the postmortem. A human still approves anything that touches production.
No credit card required.
The problem
It is 2am, and the first twenty minutes are spent finding out what you already knew.
The page fires. Someone opens a laptop in the dark and starts from nothing.
Which dashboard was it again. What shipped yesterday. Is this the same thing that woke someone up in March, or does it just look like it. The answers exist — in a Slack thread that has scrolled away, in a postmortem that was written and never read again, in the head of the one engineer who was there. None of them are reachable in the two minutes you have before someone asks for an update.
So you post in the channel: “has anyone seen this before?” And you wait. Or you escalate to the person who always gets escalated to, who is asleep, and who will fix it in four minutes when they wake up, because they remember. That is not a resilient arrangement. It is one person’s memory holding up your availability target.
Then the incident recurs. Not because nobody understood it, but because the follow-up action was written into a postmortem and never made it into a sprint, and the next person on call has no way of knowing it was ever diagnosed. The same hour is spent again by someone else, three months later.
Your MTTR is mostly the time it takes a tired human to reassemble context that already exists somewhere.
What changes
Give the recall to something that never forgets.
An assistant that meets your outage as a stranger cannot help much. The value is entirely in what it already knows about you.
Every incident is remembered
Past incidents, their symptoms, what was tried, what actually resolved them, and what the follow-up actions were — all of it lives in organizational memory alongside your systems, your services and your deployment history. “Have we seen this before?” stops being a question you ask a channel and start hoping.
Every deployment is context
The most useful question in an incident is what changed recently, and it is tedious to answer under pressure across several repositories and pipelines. An AI employee that already tracks what shipped, when, and by whom can put the plausible candidates in front of the responder in the first minute instead of the twentieth.
24/7always on, never on call, never tired
Remembersevery prior incident and what fixed it
Loggedevery action taken, every approval given
The second effect matters more than the first. Because the incident is captured while it is happening rather than reconstructed a week later, the postmortem stops being a chore that slips. And because the follow-up actions land in memory rather than in a document, the next responder knows this was diagnosed — even if the fix never got prioritised.
How it works in practice
From page to postmortem.
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1
The alert arrives
A page, a ticket in ServiceNow or Jira, or a message in the incident channel. There is no new tool for your responders to learn — the AI employee is already watching the same places they are.
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2
Context lands before the human does
What changed in the last day, which services are implicated, whether this signature has been seen before, what resolved it then, and which of those conditions hold now. The responder opens their laptop to a briefing rather than a blank page.
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3
A fix is proposed, not applied
The AI employee prepares the candidate change — a rollback, a configuration revert, a patch with tests — and explains its reasoning. It sits there, ready. A human decides whether it is right and approves it before anything reaches production.
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4
The postmortem writes itself
Timeline, contributing factors, what was tried, what worked, follow-up actions — drafted from what actually happened, not from memory. Your team edits and owns the conclusions. The incident then becomes memory the next responder inherits.
What stays under human control
Under pressure is exactly when the guard rails have to hold.
An incident is the worst possible time to relax approval. Nothing here changes at 2am. Every action is logged.
AI executes
- Gather logs, metrics and recent changes
- Search past incidents for the same signature
- Correlate the alert with recent deployments
- Reproduce and narrow the fault in non-production
- Prepare a rollback or a fix with tests
- Draft status updates and the postmortem
Humans approve
- Incident severity and who is paged
- Any change that reaches production
- Rollbacks, failovers and infrastructure changes
- Security-sensitive actions
- Customer-facing and status-page messages
- The conclusions and actions in the postmortem
What you get
What is different the next time you get paged.
Institutional recall
Every past incident searchable by symptom, with what was tried and what actually worked.
A prepared candidate fix
A rollback or patch ready for review, with the reasoning shown, waiting on your approval.
Postmortems that get written
Drafted from the real timeline while it is fresh, so the write-up stops slipping to never.
A full audit trail
Every action and approval logged — useful for the review, and for the auditor after it.
One less single point of failure
The engineer who remembers everything stops being the only route to an answer at 2am.
An honest scoping note
An AI employee is not a monitoring system and does not replace one. It needs your alerting, your logs and your metrics to be there in the first place — it makes sense of signals, it does not generate them. It will not diagnose a genuinely novel failure faster than a good engineer who knows the system; on a first-of-its-kind incident, its contribution is the mechanical context-gathering, not the insight. And it does not decide severity or talk to your customers. What it reliably removes is the repeat incident, the cold start, and the postmortem nobody had time to write.
FAQ
What on-call teams ask before they trust it.
Does an AI employee replace our on-call rota?
No. It changes what the on-call engineer is woken up to. Instead of an alert and a blank terminal, they get the alert plus the recent deployments, the related past incidents, what was done about them, and a proposed next step. A human still owns the incident and still makes the calls.
Can it make changes in production during an incident?
Only with human approval. Production deployments, infrastructure changes and security-sensitive changes always require a person to approve them, including at 2am. The AI employee can prepare the rollback or the fix and have it ready to go; a human presses the button, and the whole exchange is logged.
How does it know about incidents that happened before we started using it?
You point it at the record you already have — past tickets in Jira or ServiceNow, incident channels, postmortem documents, the deployment history. It reads that history into organizational memory, so prior incidents are searchable context from the start rather than something it has to live through first.
What does it actually do in the first minutes of an incident?
The recall work. It assembles what changed recently, which services are involved, whether this signature has been seen before, what resolved it last time, and which of those factors are present now. That is the part of triage that is mechanical and slow when a human does it half-asleep.
Will it write our postmortems?
It writes the draft — timeline, contributing factors, what was tried, what worked, and the follow-up actions, assembled from what actually happened rather than from memory a week later. Your team edits it, owns the conclusions and decides what the follow-up actions are. The draft existing is usually the difference between a postmortem happening and not happening.
How is this different from an AIOps alerting tool?
Alerting tools tell you something is wrong. An AI employee has a defined role, persistent memory of your systems and past incidents, and governed access to act — it can investigate, prepare a fix, open a pull request and draft the postmortem. It complements your monitoring rather than replacing it. See how it compares.
Get started
Stop starting every incident from zero.
Hire one AI employee, give it your incident history, and see what the next page looks like when the context is already there.
No credit card required.