How do AI Employees keep your production error backlog groomed every hour?
An engineering AI Employee connected to your error monitoring and GitHub. Hourly, it groups new and spiking errors, dedupes against existing issues, drafts issues with the stack trace and impact for the top offenders, and posts a summary to Slack. It never resolves, ignores, or assigns an error itself.
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Your error monitoring catches every error. Turning that stream into work someone actually picks up is a different problem. New error types pile up next to ones that have been spiking for days, half of them already have a tracked issue somewhere and half don't, and by the time an engineer has the hour to sit down and sort it, the backlog is long enough that sorting it feels like its own project. Most weeks no one has that hour, so the backlog just grows. Some errors are brand new, some have sat unresolved for weeks at a low steady rate, and some just started spiking after today's deploy. The common fixes don't groom the backlog, they just move it. Automatically creating an issue for every error floods the tracker with duplicates and one offs no one will ever action. Leaving the monitoring tool as the system of record means the backlog is only as current as the last time someone opened the dashboard. And on call paging tools are built for the opposite end of the problem: one loud alert right now, not the quiet pile of errors that never paged anyone but still need a decision.
An AI Employee runs on an hourly schedule with read only access to your error monitoring. Each run it pulls the errors that are new or whose event frequency has spiked over their trailing baseline, and groups them by fingerprint. For each one it checks GitHub for an existing issue before doing anything else. For the top offenders that aren't already tracked, it drafts a GitHub issue with the stack trace and the impact: event count, affected users, first seen. Then it posts a summary of what's new, what's spiking, and what got drafted to Slack. It never touches the error's state and never assigns anything.
See exactly how the work gets done.
Runs hourly from a clean read
An hourly schedule starts a fresh, isolated run. One hour maps to one run, so the backlog is read fresh from the monitoring tool's current state every time and nothing carries over between runs.
Carries your grooming rules
What counts as a spike versus normal noise, how to rank by impact, the issue template the stack trace and impact get written into, and your tracker's labels and conventions travel with the AI Employee as skills and memory. Tighten a threshold or change the template and the next sweep follows it.
Connects error monitoring read only and GitHub for drafts, with permissions you set
It reads the backlog, meaning new issues and issues whose frequency has spiked over baseline, with full stack trace, first seen, and event and user counts. It checks GitHub for an existing issue by its error ID before drafting. It drafts an issue for the top untracked offenders. And it posts a sweep summary to Slack. Credentials stay in memory, never written to disk, never exposed to the model.
Drafts only; never touches state
The AI Employee never resolves, ignores, or mutes an error, and never assigns an issue to anyone. Those are decisions for the team. Its only writes are the drafted issue and the Slack post.
Grooms the backlog itself
Every hour turns the raw error stream into a short, ranked list of what actually needs a decision: new errors and real spikes, each checked against the tracker so nothing gets duplicated, and the ones that matter most already drafted with the trace and impact attached. The team reads the Slack summary and decides what to prioritize.
Runs in your environment
Every run happens in isolation inside your own infrastructure; your data never leaves it. The run reads error monitoring and searches GitHub, and only the drafted issue and the Slack post leave.
Permissions you set
The monitoring credential and the GitHub token stay in memory, injected at run time, never written to disk, never exposed to the model or the logs.
Never resolves, ignores, or mutes
The AI Employee has no path to changing an error's state. It reports; it doesn't manage the backlog's status.
Never assigns
A drafted issue has no assignee. Triage priority and ownership are decided by the team, not the AI Employee.
You own it
The grooming rules, thresholds, and per system permissions are yours, versioned and changed on your terms, not in a vendor dashboard.
The error backlog that used to need a dedicated hour to sort now arrives groomed every hour: new errors and real spikes ranked by impact, checked against what's already tracked, and the top offenders already drafted with the trace attached. The team spends its time deciding what to fix, not rediscovering what's broken.
Every hour
The backlog read fresh from the monitoring tool's current state
Deduped
Checked against existing GitHub issues before drafting
Draft only
Issues and a Slack alert, never a resolve, ignore, or assign
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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