Bug Triage Workflow
Sentry fires at 2:47am. By 2:52am the AI has classified severity, identified 4 customers affected, and filed a Linear issue with a working reproducer.
Sentry throws 400 errors a day. Your bug Slack channel has 6 months of 'looking into it' messages with no follow-up. One customer emails that signups are broken and it takes you 3 hours to notice because the email was buried. When you finally ship a fix you can't remember if this was the same bug you thought you fixed 2 weeks ago — turns out it was, and the regression test never got written.
The AI CTO and AI Customer Support run bug triage as a continuous loop: ingest signals from Sentry, Intercom, status page comments, Twitter, and App Store reviews; dedupe across sources; score severity by user impact × revenue exposure; write a reproducer when possible; file one Linear issue per root cause and route it. Regressions get caught because the AI remembers every prior bug.
How it runs
- 1Multi-source ingestion
Webhooks from Sentry (errors, performance), Datadog (infra), Intercom (customer reports), Crisp, status page comments, App Store/Play Store reviews, Twitter mentions with 'broken/not working' sentiment. Each signal becomes a raw event tagged by source, timestamp, and affected user/account.
- 2Dedupe and root-cause grouping
Sentry already groups by stack trace. The AI does the next layer: it links customer reports to Sentry groups by matching user session IDs, ties Twitter complaints to infra events by timestamp correlation, and collapses near-duplicate groups (same root cause, different surface). Result: one issue per real problem, not 400 alerts for one 500ms DB hiccup.
- 3Severity scoring
AI CTO computes severity: S0 (revenue-blocking, payments down, signups broken, auth failing), S1 (degraded core flow for paid users), S2 (edge case affecting a minority), S3 (cosmetic or low-frequency). Score factors: % of users affected, revenue exposure (paid customers impacted × their MRR), whether a workaround exists, and growth-over-time rate.
- 4Reproducer synthesis
For any S0/S1, AI CTO attempts a reproducer. It pulls the stack trace, recent git commits, user actions from PostHog, and writes a Playwright script or a curl command that triggers the error locally. If it can reproduce, it attaches the script. If it can't, it logs what it tried and hands off to you with 'Couldn't reproduce — here's what I ruled out.'
- 5Route to fix path
S0: pages you directly via Slack/SMS with evidence bundle, drafts a status page incident, and starts a Zoom bridge in case you want backup. S1: creates a Linear issue tagged urgent, assigns to the right sub-repo based on stack trace (frontend / API / billing / etc.), includes reproducer and impacted user list. S2/S3: queue in the weekly bug review for batching.
- 6Customer communication
For customer-reported bugs, AI Customer Support sends an acknowledgment within 10 minutes of the report: 'Got it, we can reproduce, tracking as issue #847. Expected fix: this week/today/checking.' No 'we're looking into it' template — the reply names the issue number and gives a real ETA from the AI CTO's estimate.
- 7Regression check on every deploy
Before any PR merges, the AI CTO cross-references the diff against all closed bug issues from the last 90 days. If the code change touches a file previously implicated in a closed bug, it flags: 'This PR modifies X, which was involved in fixed bug #712. Consider adding a regression test.' Catches the same-bug-twice problem before deploy.
- 8Weekly bug debt review
Friday afternoon, AI CTO posts the bug portfolio: S0/S1 resolved this week, S2 queued, S3 accepted-as-tolerable. Flags any issue aging >30 days ('dark pattern: this has been open since Feb 3, 6 customers still hitting it'). You decide which aged issues to force into this week's slots.
Who runs it
What you get
- ✓S0 incidents from first signal to you being paged in under 5 minutes
- ✓Customer bug reports acknowledged within 10 minutes with a real ETA
- ✓Working reproducer attached to 60-80% of S1 bugs before any human touches them
- ✓Regressions caught pre-deploy via cross-reference against closed bug history
- ✓One Linear issue per root cause, not one per user complaint
- ✓Weekly bug debt is visible — no issue rots unnoticed for 6 months
- ✓Status page kept current without manual 'is this still happening?' checks
Frequently asked questions
How do you avoid Sentry noise spam — we get 400 alerts a day and most are garbage?
Noise suppression happens at classification time, not at alerting time. The AI CTO triages every Sentry group but only opens Linear issues for groups that meet a minimum bar: affecting ≥3 distinct users, OR impacting a paying customer, OR on a critical path (auth, payments, core flow), OR net-new this week. A 404 from a dead crawler hitting /old-api a hundred times a day won't trigger anything. The triage log is still kept so you can audit 'why did we not act on this' but the noise doesn't make it to your Linear. Most founders see their triaged issue count drop from ~400/day signals to 2-8/day actually-actionable issues, which is the volume a human could have handled if they had 4 free hours.
What about bugs that only the customer can reproduce — like 'this doesn't work on my iPhone XR with iOS 16'?
The AI CTO can't reproduce those without the customer's help, and it's honest about it. For customer-specific bugs it generates a structured info-request to the customer: 'We need a screen recording, your browser version, and the URL you were on — reply with these and we'll have a fix candidate in 24h.' The AI Customer Support sends the message in a non-annoying way (not a form, a natural reply). About 60% of customers respond within a day with the info needed. The 40% who don't get logged as 'info needed, low priority' and don't block real work. What the AI does that a human ops person wouldn't: automatically correlates every customer-specific bug with PostHog session data if the customer's user ID can be linked, which often reproduces the issue without the customer doing anything.
Can the AI actually fix bugs itself, or just triage them?
It triages and files; fixing is the AI CTO's developer subagent, which is a separate thing. For low-risk categories — typos in copy, missing null checks in clearly bounded code, documentation fixes — the AI CTO will draft a PR and queue it for your approval. For anything touching auth, payments, schema, or production data flow, it stops at the triage step and hands you a ready-to-fix issue with reproducer. The dividing line is explicit in the AI CTO's configuration — founders can expand or tighten it. Most founders start with 'triage-only' and expand auto-fix scope once they've seen the quality of the triage for a few weeks.
How does this interact with Sentry's own AI-grouping features?
Sentry groups errors by stack trace similarity — same function, same file, same exception type. That's the first layer and Tycoon doesn't re-do it. What Tycoon adds is the next three layers: (1) grouping across data sources (Sentry + customer report + Twitter mention about the same thing become one issue), (2) business context (revenue exposure, user segment, workaround availability) that Sentry has no access to, and (3) memory across time (this group of errors is a regression of issue #712 from February, even though Sentry sees it as new). Sentry is the telemetry layer; Tycoon is the decision layer on top. They're complementary, not competing.
What's the escalation if a bug is S0 at 3am and I'm asleep?
You configure escalation per severity. Default S0 behavior: (1) text you via Twilio with the evidence bundle summary, (2) auto-post an acknowledgment on your status page so customers know you know, (3) ping you on Slack mobile with push notification, (4) if no ack from you in 10 minutes, escalate to a fallback contact (co-founder, on-call dev). Some founders configure tighter: auto-revert the last deploy if the error rate on the new release exceeds X%. Tycoon supports that but doesn't default to it because auto-reverts can cause worse outcomes than letting the incident ride with the existing mitigations. You decide the trade-off.
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