How do you triage on call alerts before they page a human?
An on call AI Employee that works up a first pass diagnosis on every alert, pulling the stack trace, the recent deploys, and the correlated logs, posting it to the incident channel, and paging a named human only when it can't resolve the alert or the severity is high.
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An alert fires at 3am. Before anyone can act on it, someone has to wake up, pull the stack trace, check what deployed recently, grep the logs, and work out whether this is a real incident or noise. Most of that is mechanical, and most of it happens before the human has any context: which error is this, when did it start, what shipped just before, is it one user or all of them. The on call engineer answers these by hand, half awake, before they can even judge whether the page was warranted. The common fixes are incomplete. Paging on every alert burns the on call rotation on noise and trains people to ignore the pager. Tuning thresholds cuts the noise but also hides real regressions. A runbook helps, but someone still has to be awake to follow it. None of it does the gathering for you.
Each alert triggers the on call AI Employee. When error tracking fires, the alert spawns a run in your own environment with read only access to the error tracker, the logs, and GitHub. It pulls the stack trace, lists the deploys since the error first appeared, correlates the logs around the spike, and posts a first pass diagnosis to the incident channel. It pages a named human only when it can't resolve the alert or the severity is high.
See exactly how the work gets done.
Runs on every alert, as it fires
A signed webhook from your error tracking points at it. Every alert fires it, and each firing spawns a fresh run seeded with the alert payload. One alert, one run, one clean slate. Nothing carries over between incidents, and concurrent alerts triage in parallel.
Carries your triage playbook
How you triage travels with it as a skill: which services are noisy, what a real regression looks like versus a known flake, the severity rules for when to page, and past incidents with their root causes. When an alert turns out benign, you write it down and it recognizes it next time.
Connects read only, with permissions you set
It reads the error issue (stack trace, frequency, and first seen timestamp) to place the error in time, pulls the log lines around the spike and lines them up against the trace, checks the commits and PRs that shipped just before on GitHub, and posts the diagnosis, the suspected deploy, and the evidence to the incident channel as one message. Credentials stay in memory, never written to disk or exposed to the model.
Read only, it never deploys or rolls back
It investigates across the error tracker, the logs, and GitHub, but it does not deploy, roll back, or change anything. High severity alerts and anything it can't resolve page a named human straight away. The diagnosis is attached, not a substitute for the page.
Pages with the work already done
The trace, the deploy window, the correlated logs, and a first pass diagnosis land in the channel within the first minute. A known benign spike is closed with the reasoning attached. A high severity or unresolved alert pages the on call engineer with the work already done, so they open the page to context instead of a blank terminal.
Runs in your environment
Every alert runs isolated inside your own infrastructure, pulling traces, logs, and deploy history to build the diagnosis; only the posted message and any page leave it. Your data never leaves it.
Permissions you set
The error tracking, logging, and GitHub credentials stay in memory, injected at run time, never exposed to the model or the logs.
Read only, never acts
It never deploys, rolls back, or changes anything. High severity and unresolved alerts page a named human, who owns any action taken.
Diagnosis attached, never a substitute
A page still goes to a person whenever it's warranted. The AI Employee gathers the context; it does not decide the fix or silence a real regression.
You own the rules
The triage playbook, the severity rules, the skills, and the permissions are yours, versioned and changed on your terms, not in a vendor dashboard.
The mechanical first minutes of an incident happen before anyone is paged, and when the AI Employee does page, it pages with the trace, the suspect deploy, and the correlated logs attached. The on call engineer spends their time deciding and fixing rather than gathering context half awake.
First pass
Diagnosis in the channel before the pager fires
Less noise
Benign alerts closed with reasoning, not paged
3 systems
Error tracking, logs, and deploy history in one AI Employee
Our free AI audit shows you where AI fits, what your security risks are, and gets your first AI employee working.
How do AI Employees keep your docs in sync with the code?
Sweeps the code merged each day, rewrites the docs those changes touched, and opens one reviewable pull request. Publishing waits for a human merge.
×How do AI Employees test every pull request before a human reviews it?
Checks out every pull request, runs the suite, exercises the change through the edge on a test deploy, and posts the result. It stays off production, leaving the merge to a person.
×How do you get a postmortem drafted the moment an incident resolves?
Reconstructs the incident timeline, correlates it against deploys and log spikes, and opens a structured postmortem as a doc PR. It drafts, and the team finalizes.
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