Picture this: 18 AI-augmented developers merging an average of 17 pull requests every working day — 33 on the peak day. And at the time, exactly one QA engineer watching over all of it. The fact that things didn't fall apart daily looks like a miracle. Spoiler: it's not a miracle, it's e2e tests on staging — plus a bot that triages their failures so humans don't have to.
I'm that QA engineer. Here's how we at pdf.net got a GitHub Action + Claude to triage every red run: the architecture, the code, and the rakes we stepped on — the bot blaming innocent people, losing tickets, and staying silent when silence was the worst option. Headline number up front: the whole thing costs less per month than one hour of engineering time.
Our team is AI-first by design: developers ship with AI assistants, and instead of scaling manual testing along with headcount, we bet on automation. Autotests aren't a safety net "just in case" — they're the primary trigger that we're doing something wrong.
We release once a day, and originally the release was where everything got verified: fifteen-plus PRs roll up, tests run, and if something's red, the one QA engineer investigates. A single release could contain several broken PRs at once, each to be identified among 17+ suspects while the release waits. Mornings turned into detective work.
So we shifted the loop left. Quick infra note: every PR gets a Vercel preview (a dev environment — running the full e2e scope there makes no sense: it's slow, and half the features sit behind flags that don't match production), while staging is effectively pre-prod: every merge lands there, and code ships to production from there. E2e now runs on every merge to staging — the suspect range becomes one fresh merge instead of a day's batch, and the release turns into a final "works assembled" check plus manual testing that automation can't cover.
The price: red runs now happen potentially after every one of those 17 merges, each needs a quick investigation, and until it's done the release is in question. Hiring a person for "look at a failed run and decide who to hand it to" is overkill — that's not a role, it's a distraction. The reasoning wrote itself: if our tests are automation, triaging their failures should be automation too.
Handily, the failure sources are a closed list — a PR regression in the main repo, the neighboring backend repo (its deploys trigger our e2e too), external services, CI infrastructure, or a flake. Manual triage was always the same mechanical chain over structured inputs: failed tests, diffs, logs → a three-way call. Exactly the kind of task LLMs are good at.
The triage is a reusable composite GitHub Action, invoked after every e2e run — red and green. Around it: Allure TestOps (test results), Linear (tickets), Slack (notifications), Claude API (the brain).
e2e run on staging
│
red ──────────┴────────── green
│ │
AI verdict Recovery: close tickets,
│ rebase-nudge PR authors
┌──────────┴───────────────┐
likely-pr-related likely-not-pr-related
│ / insufficient-data
Linear ticket │
+ Slack thread Slack heads-up + ONE auto-rerun
│
┌──────────┴──────────┐
rerun green rerun red
│ │
it was a flake — escalation: ticket
note it, move on + @oncall mention
On a red run the bot collects failed tests with steps from Allure, job logs, the deploy commit range, and the diffs of the PRs that went in — and asks Claude (Opus 4.8, Sonnet 5 fallback) for a verdict plus a fix hypothesis:
const verdict = await askClaude({ failedTests, jobLogs, deployRange, prDiffs })
// => 'likely-pr-related' | 'likely-not-pr-related' | 'insufficient-data'
if (attempt === 1 && verdict !== 'likely-pr-related') {
await slack.post(headsUp(verdict)) // "doesn't look PR-related — rerunning"
return requestRerun() // exactly one auto-rerun
}
// PR-related, or a red rerun → Linear ticket + Slack post,
// on a red rerun — with an on-call mention
await createLinearIssue({ verdict, hypothesis, failedTests })
The key decision: the verdict picks a route, it doesn't just write text. PR-related → ticket with a hypothesis and a Slack thread. Anything else → no ticket yet: a heads-up and one auto-rerun. Green rerun — it was a flake, no noise. Red rerun — ticket plus an on-call mention.
🔴 e2e failed on staging — 3 tests
Verdict: likely-pr-related
Hypothesis: regression in PR #1234 — the file upload handler
changed, tests fail on the "attach document" step
Ticket: QA-482 · Staging: 🔴 red — release on hold
Tickets land in one Linear queue, with labels telling frontend deploys from backend ones. One manual habit pays for it all: a confirmed real bug gets a bug
label — so "how many bugs did autotests catch, and how much was noise" is a plain queue filter.
Noise control, four mechanisms: dedup (repeat failures match open auto-tickets instead of spawning new ones), auto-close (green run closes the ticket, posts recovery with outage duration, and nudges open PRs to rebase), two channels (ticket-backed posts go to the important channel, rerun chatter stays in the working one), and manual muting (tickets closed without a fix go to Canceled, not Done — recovery leaves them alone).
Neat on paper. Now the four rakes.
Recovery writes to three places: a Slack post, the Done transition, and a recovered-at: <sha>
marker comment so the next green run skips the ticket. The first version wrote the marker first. Slack post succeeds, Done transition fails on a network hiccup — the marker is already there, and every following run says "already processed". Forever.
The fix is a classic idempotent sequence — each step guarded by its own marker, "fully processed" written last:
if (!hasSlackPostedComment(issue.comments)) {
const resp = await slack.postMessage(buildRecovery({ issue, attribution, cause }))
await linear.addComment(issue.id, `slack-recovery-posted:${resp.ts}`)
}
if (issue.stateType !== 'completed') {
await linear.transitionToDone(issue.id)
}
if (!hasRecoveredAtComment(issue.comments)) {
await linear.addComment(issue.id, `recovered-at:${sha7}`) // strictly last
}
Fail in the middle — the next run completes the rest without duplicating anything. Bonus: state lives in the ticket's own comments — no external database, human-readable, garbage-collected with the ticket.
Recovery posts ended with "thanks for the fix, @author" — author computed from the attached fix-PR, or, as a fallback, from the commits in the green run's deploy range. But when a ticket closed because an external service came back to life, the fallback still found an "author": some random person whose innocent PR rode the same deploy. Publicly thanked for a fix that never existed. A couple of those and trust in the bot is gone.
The fix is a recovery-cause classifier — a pure function, no AI:
type RecoveryCause = 'code-fix' | 'external' | 'ambiguous'
const classifyFixCause = ({ deployRange, originalVerdict }): RecoveryCause => {
const codeFiles = deployRange.files.filter(f => isCodeFile(f.filename))
if (codeFiles.length === 0) return 'external' // only docs/lockfiles deployed
if (originalVerdict === 'likely-not-pr-related') return 'ambiguous'
return 'code-fix'
}
Attribution now fires only for code-fix
; external
and ambiguous
use a no-names template. ambiguous
doubles as telemetry: how often the model's verdict diverged from the actual cause.
The first dispatcher was if (hasFailures) runAnalysis() else runRecovery()
. Breaks on a mixed run: old ticket's tests are fixed, but something new failed — the run is red, recovery never fires, the fixed ticket hangs open. The fix: both paths run on every invocation, recovery first, each no-oping when idle:
await runRecovery(ctx) // close everything that recovered
await runAnalysis(ctx) // triage everything that failed
Immediate blast radius: our "rerun helped" signal was just runAttempt >= 2
, which was safe while recovery ran only on green runs. Once it ran always, the bot cheerfully posted "✅ recovered — auto-rerun helped" on a red rerun. Hence the explicit guard: runAttempt >= 2 && failureSet.size === 0
. Every check that implicitly relied on the old call order has to become explicit.
Our e2e lives in the frontend repo and walks the full user scenario through the UI — so it catches backend breakage too, and the backend's staging deploy triggers the same workflow via workflow_dispatch
. The GitHub Actions quirk you learn the hard way: a dispatched run has no push context. GITHUB_EVENT_BEFORE
is empty, there is no deploy range — and the bot died silently: no ticket, no post. Red staging, and silence. The worst failure mode for a triage system: everyone assumes quiet bot = all under control.
The fix — the deploy range became an explicit input:
- uses: our-org/staging-triage@v1.6
with:
deploy-repo: our-org/backend # whose deploy triggered the e2e
deploy-before-sha: ${{ inputs.before-sha }}
deploy-head-sha: ${{ inputs.head-sha }}
Everything downstream — diffs for the verdict, authors for attribution and mentions — now comes from the repo that actually deployed. One triage, both repos.
You can't unit-test a system whose behavior lives across CI, Linear, Slack, and Allure. Two things worked: a smoke PR walked commit by commit through the whole state series — first failure (ticket created?), repeat failure (dedup quiet?), mixed run (both paths fire?), green run (closed + recovery post?) — real runs, real APIs, no mocks. And a kill switch: manual workflow_dispatch
is suppressed unless STAGING_TRIAGE_SIMULATED=true
, so you can poke the workflow without spamming Linear.
The most dangerous failure mode of an LLM classifier isn't a wrong verdict — it's confident text about nothing: "possibly a flake", "check the environment", "probably a cold start". So the prompt is built on bans, not wishes:
likely-pr-related
→ file:line and what to change; likely-not-pr-related
→ facts with quotes, what's missing, where a human should look.insufficient-data
is declared a correct, useful answer — otherwise the model invents hypotheses for the sake of pretty text.What still happens: hypotheses right about the area, wrong in the details — the first critical bug the bot caught, it pointed at the right place, but a human wrote the actual fix, just faster. That's fine. The bot doesn't replace an engineer — it replaces the first 15–30 minutes of their work.
Honest part first: we haven't collected rigorous metrics, and two months is a small sample. But releases got noticeably faster — bugs are localized when the code lands on staging, not on release morning, and the 17-suspects detective game is gone. The bug
label groundwork means in six months there'll be real numbers.
Cost: one model call per red run (recovery needs no AI), typically 30–80K input tokens — $0.3–0.5 per red run at Opus 4.8 prices, ~$2.5 worst case, $30–70 a month total. The QA team has grown since — I went from the only QA to leading it — but triage stayed with the bot: people do automation and agent experiments, not red-run duty. The automation was born from a shortage of hands and stayed because it's simply better.
They do. Cognition sells Devin Auto-Triage; GitHub shipped a “Fix with Copilot” button on failed workflow runs. We saw all that and deliberately built our own — because our task isn't "fix the red CI". It's a decision about the fate of a release: classify the cause, pick a route (ticket / rerun / escalation), dedup, auto-close, wake nobody without a reason — welded to our exact stack (Allure TestOps, Linear, Slack, two repos) and our processes.
And the price tag: for an AI-first team this is days, not weeks — the code is almost entirely written with Claude Code. The honest division of labor: the human part is the prototype and the architecture — verdict separated from routing, idempotent recovery steps, pure-function classifiers, state in ticket comments instead of a database. AI writes great code inside a given structure and speeds iteration up by an order of magnitude; the structure that survives six releases in two months is set by a human.
Why not open-source it, then? Similar "AI triages your failed CI" actions sit at 3–11 GitHub stars, and ours needs the exact Linear + Slack + Allure TestOps + Anthropic combination — that audience is vanishingly small, while baseline "AI explains the failure" is arriving in GitHub out of the box. What's worth open-sourcing here are the ideas, not the code — which is exactly what this article is.
AI-first means AI-first.