Every crash grouped, deduped
and assigned to the right person
An error tracker like Sentry catches every exception, but a busy app still produces hundreds of entries a week, many of them the same underlying bug reported a dozen different ways, and sorting that into what actually needs fixing falls on whoever has time. We build an agent that groups exceptions by root cause, dedupes the noise, and assigns each real issue to the engineer whose code touched it last.
The process today
An error tracker generates a steady stream of exceptions from a live application, and a meaningful share of them are the same bug reported through slightly different stack traces, a null check missing in three call sites that all eventually hit the same underlying issue, or the same crash recurring every time a particular edge case comes up. Without active grouping, these show up as separate issues, inflating the backlog and making it hard to see which bugs are actually distinct problems versus the same one counted five times.
The second cost is assignment. Someone has to look at each new issue, figure out roughly what broke and who last touched that part of the code, and either assign it or let it sit in an unowned backlog until someone picks it up. On a team without a dedicated triage rotation, that someone is usually whoever happened to open the error tracker that day, which means triage quality depends entirely on who was paying attention.
The third is that severity gets judged by error count alone, so a low-impact exception that happens to fire a thousand times an hour, a background job retry, outranks a rare but serious bug that only a handful of real customers have hit, simply because the numbers look bigger.
What the agent does
The agent reads every exception as it arrives in your error tracker, groups it with others sharing the same actual root cause rather than just similar-looking stack trace text, and flags recurring crashes that keep showing up release after release despite looking “fixed” in the tracker. Each distinct issue gets a severity score based on estimated user impact, how many distinct users hit it, whether it is in a critical path like checkout or login, not just raw occurrence count.
For assignment, the agent checks the git history of the failing file and function and assigns the issue to whoever committed there most recently, with a documented fallback to a team default when there is no clear single owner. Every assigned ticket carries a plain-language summary of what broke, the likely cause based on the stack trace and recent changes, and links to the specific commits involved, so the engineer picking it up starts with context instead of a raw stack trace. Known issues already being tracked are suppressed from re-alerting, and a weekly rollup shows what is new, what is recurring, and what got resolved. Typical integrations: Sentry, Bugsnag or Rollbar for the error data, GitHub or GitLab for commit history, and Jira, Linear or GitHub Issues for the resulting tickets.
What stays with humans
Deciding how to actually fix a bug, and deciding that an issue is not worth fixing right now and can be deprioritized, both stay engineering decisions. Resolving or dismissing a ticket is always done by the assigned person or a lead, never by the agent automatically closing something because it stopped recurring for a few days. Any disagreement about who owns an issue, which happens when a bug spans a boundary between two services, goes to a person to resolve, with the agent’s commit-history evidence as a starting point rather than a final verdict.
Guards
Every grouping decision and every assignment is logged with the reasoning, so a wrongly grouped or wrongly assigned issue is easy to spot and correct, and that correction feeds back into how future similar cases are handled. The agent never auto-resolves or auto-dismisses a ticket; it only groups, scores and assigns. A kill switch reverts to your error tracker’s default, ungrouped view at any time without losing the issue history already triaged.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $800 | One application, one error tracker, grouping, severity scoring, assignment | 5 to 10 days |
| Department package | from $2,300 | Exception triage plus performance regression alerts and post-release smoke tests | 2 to 4 weeks |
Running cost is usually $20 to $60 a month in model usage depending on exception volume.
Related
This pairs well with performance regression alerts for the slow-but-not-crashing side of the same codebase, and with the existing log triage automation for the active-incident, raw-log side of error handling. For the deploy that likely introduced a new crash, see automated deployments with rollback. Full package details are on the AI agents service page and the automation-everything overview; for how we handle error volume on our own live products, see the ProBay AI agent team case study and the secure infrastructure case study.
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FAQ
How is this different from the log triage automation in your catalogue?
Log triage reads raw production logs and pages someone during an active incident. This works inside your error tracker, Sentry, Bugsnag or similar, grouping and assigning application exceptions day to day, incident or not, so bugs get fixed before they become an incident.
How much does exception triage automation cost?
From $800 for one application connected to one error tracker, live in 5 to 10 days. Multiple services or a larger engineering team usually run $1,500 to $2,500.
How does it know who to assign a bug to?
It checks git blame and recent commit history for the failing file and function, and assigns to whoever touched that code most recently, with a fallback to a team-level default when no clear owner exists.
Will it close or dismiss bugs on its own?
No, it groups, dedupes, scores severity and assigns; resolving or dismissing an issue is always a decision the assigned engineer or a lead makes.
Which error trackers does it work with?
Sentry, Bugsnag and Rollbar directly, or any error tracker with an API, connected to your existing ticketing system, Jira, Linear or GitHub Issues.