Queries · q345 · VP Platform Engineering · week 2, night 4
“Was the Nov 28 checkout/storefront outage a one-off, or did similarly severe (>=50% unhealthy) readings happen at other times in the window we have data for?”
Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told
Week 1; shared with Samuel Whitcombe, Head of Merchant Success
Leadership wants a review of platform reliability in 2025: failed and slow background jobs, health check failures, rate limiting, webhook delivery problems, and which merchants felt them.
The manager, at the start of week 2
Subject: Re: 2025 Platform Reliability Review — good start, a few things I need tightened up
Lucas,
Thanks — this is useful and more honest than I expected on the limits of what we can see. A few things stood out and need more work before Friday's memo goes to leadership.
First, the retention gap is itself a finding, not just a caveat. We're flying with 90 days of job history and 60 days of health-check history — that means we genuinely don't know how 2025 looked before October. I want that stated plainly and separately in the memo as a reliability/observability gap in its own right, not buried as a footnote under the carts.remind story.
Second, the ERP/WMS/Accounting webhook stuck-retry pattern is, to me, the most important and actionable thing in here — more than the one-week carts.remind incident. I want that properly validated and sized, not eyeballed, before we say anything about it to leadership.
Questions for this week:
On rate limiting: agreed, drop it for this cycle — don't spend a slot trying to force an answer out of config data alone. Just flag it as a 2026 instrumentation ask, as you already have.
Priority for your remaining slots: #1 and #2 above. The webhook/sync issue is the one I can take into a fix-it conversation with engineering next week if the numbers hold up — I'd rather have that nailed down than more polish on the carts.remind narrative.
Diego
The analyst · week 2, night 4, request 2 of 3
Report 1 confirms health checks for checkout/storefront run only every 2 hours (checks exist at 12:00, 14:00, 16:00 but nothing in between) - so even though monitoring did catch a 100%-unhealthy reading at 14:00, our detection granularity for this path is coarse (up to 2 hours to notice), which is itself worth flagging for Diego's #3. Report 2 is the smoking gun on business impact: hour 14 had just 1 order and hour 15 has NO row at all (zero orders) on Black Friday, versus 7-11 orders in every neighboring hour that day - a near-total order freeze for roughly 2 hours on the year's biggest shopping day. Report 3 shows Black Friday 2025 total revenue (€40,166) came in 26% below Black Friday 2024 (€54,186) despite the business having grown since then, which is directionally consistent with real lost sales, though other factors could contribute and I haven't isolated the outage's exact share of that gap. This is my last night before the memo, so I'm using the 3 slots to: (1) precisely size the lost-order/revenue window by comparing the same 14:00-16:00 UTC slot across Nov 28 against two baseline Fridays and Black Friday 2024, so I can give Diego an actual number rather than an impression; (2) check whether checkout/storefront ever showed another severe (>=50% unhealthy) outage anywhere else in the Nov-Dec window we have, to answer whether this was a true one-off or a recurring latent risk; (3) check payment attempts during the same window, since a checkout-path outage should show up there too and would confirm this was a full checkout failure, not just a reminder-email glitch.
The engineer · 1 dry run on the empty schema, then the submission
SELECT date_trunc('hour', checked_at) AS checked_hour, component, count(*) AS total_checks, count(*) FILTER (WHERE is_healthy = false) AS unhealthy_checks, round(100.0 * count(*) FILTER (WHERE is_healthy = false) / count(*), 2) AS unhealthy_rate_pct, round(avg(latency_ms)) AS avg_latency_ms FROM system_health_checks WHERE checked_at >= DATE '2025-11-01' AND checked_at < DATE '2026-01-01' AND component IN ('checkout', 'storefront') GROUP BY date_trunc('hour', checked_at), component HAVING 100.0 * count(*) FILTER (WHERE is_healthy = false) / count(*) >= 50 ORDER BY checked_hour ASC, component ASC
“Query compiles cleanly with the expected columns. Submitting.”
“Submitted. The report returns one row per hour+component (Nov 1–Dec 31, 2025, checkout/storefront only) where unhealthy_rate_pct >= 50, ordered by hour then component, so Lucas can see at a glance whether Nov 28 was isolated or part of a recurring pattern.”
As Lucas Moreau saw it, from the small dataset the agents worked on
checked_hour | component | total_checks | unhealthy_checks | unhealthy_rate_pct | avg_latency_ms --------------------+------------+--------------+------------------+--------------------+--------------- 2025-11-28 14:00:00 | checkout | 111 | 111 | 100.0 | 10000.0 2025-11-28 14:00:00 | storefront | 111 | 111 | 100.0 | 10000.0 (2 rows)
The memo and the queries that followed; links found by title, so inferred
Lucas Moreau's memo to Diego Morales at the end of the week:
4. This looks like a genuine one-off, not a recurring risk — within the window we can see. Across all of Nov-Dec 2025, the Nov 28 14:00 reading is the only time checkout/storefront hit ≥50% unhealthy. Source: "Checkout/Storefront Severe Outages, Nov-Dec 2025." I can't check before November (health-check retention stops there), so I can't fully rule out a prior occurrence.
It builds on:
Changed only where the benchmark needs the same result on every system
SELECT date_trunc('hour', checked_at) AS checked_hour, component, count(*) AS total_checks, count(*) FILTER (WHERE is_healthy = false) AS unhealthy_checks, round(100.0 * count(*) FILTER (WHERE is_healthy = false) / count(*), 2) AS unhealthy_rate_pct, round(avg(latency_ms)) AS avg_latency_ms 100.0 * count(*) FILTER (WHERE is_healthy = false) / count(*) AS unhealthy_rate_pct, avg(cast(latency_ms as numeric(18,6))) AS avg_latency_msFROM system_health_checksWHERE checked_at >= DATE '2025-11-01' AND checked_at < DATE '2026-01-01' AND component IN ('checkout', 'storefront')GROUP BY date_trunc('hour', checked_at), componentHAVING 100.0 * count(*) FILTER (WHERE is_healthy = false) / count(*) >= 50ORDER BY checked_hour ASC, component ASCORDER BY checked_hour ASC NULLS LAST, component ASC NULLS LAST
SELECT date_trunc('hour', checked_at) AS checked_hour, component, count(*) AS total_checks, count(*) FILTER (WHERE is_healthy = false) AS unhealthy_checks, 100.0 * count(*) FILTER (WHERE is_healthy = false) / count(*) AS unhealthy_rate_pct, avg(cast(latency_ms as numeric(18,6))) AS avg_latency_ms FROM system_health_checks WHERE checked_at >= DATE '2025-11-01' AND checked_at < DATE '2026-01-01' AND component IN ('checkout', 'storefront') GROUP BY date_trunc('hour', checked_at), component HAVING 100.0 * count(*) FILTER (WHERE is_healthy = false) / count(*) >= 50 ORDER BY checked_hour ASC NULLS LAST, component ASC NULLS LAST
The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted
Report request from Lucas Moreau (analyst for the VP Platform Engineering):
Title: Checkout/Storefront Severe Outages, Nov-Dec 2025
Question: Was the Nov 28 checkout/storefront outage a one-off, or did similarly severe (>=50% unhealthy) readings happen at other times in the window we have data for?
Grain: one row per hour and component where unhealthy_rate_pct >= 50
Columns:
- checked_hour
- component
- total_checks
- unhealthy_checks
- unhealthy_rate_pct
- avg_latency_ms
Details: Table system_health_checks. Filter checked_at >= 2025-11-01 and < 2026-01-01, component in ('checkout','storefront'). Group by hour of checked_at and component. Keep only groups where unhealthy_rate_pct (is_healthy=false count / total_checks * 100) >= 50, rounded 2dp. avg_latency_ms over non-null latency_ms, rounded to whole number. Order by checked_hour ascending, component ascending.
Check your query with the warehouse's dry_run tool, then hand it in with submit.dry_runSELECT date_trunc('hour', checked_at) AS checked_hour, component, count(*) AS total_checks, count(*) FILTER (WHERE is_healthy = false) AS unhealthy_checks, round(100.0 * count(*) FILTER (WHERE is_healthy = false) / count(*), 2) AS unhealthy_rate_pct, round(avg(latency_ms)) AS avg_latency_ms FROM system_health_checks WHERE checked_at >= DATE '2025-11-01' AND checked_at < DATE '2026-01-01' AND component IN ('checkout', 'storefront') GROUP BY date_trunc('hour', checked_at), component HAVING 100.0 * count(*) FILTER (WHERE is_healthy = false) / count(*) >= 50 ORDER BY checked_hour ASC, component ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: checked_hour, component, total_checks, unhealthy_checks, unhealthy_rate_pct, avg_latency_ms
submitSELECT date_trunc('hour', checked_at) AS checked_hour, component, count(*) AS total_checks, count(*) FILTER (WHERE is_healthy = false) AS unhealthy_checks, round(100.0 * count(*) FILTER (WHERE is_healthy = false) / count(*), 2) AS unhealthy_rate_pct, round(avg(latency_ms)) AS avg_latency_ms FROM system_health_checks WHERE checked_at >= DATE '2025-11-01' AND checked_at < DATE '2026-01-01' AND component IN ('checkout', 'storefront') GROUP BY date_trunc('hour', checked_at), component HAVING 100.0 * count(*) FILTER (WHERE is_healthy = false) / count(*) >= 50 ORDER BY checked_hour ASC, component ASC
Submitted to tonight's batch. Your work on this request is done.