Queries · q341 · VP Platform Engineering · week 2, night 3
“Exactly when did the checkout/storefront outage start and end on Nov 28 2025?”
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 3, request 1 of 3
Report 2 from last night changes the scale of this story substantially: at the 14:00 UTC hour check on Nov 28 2025, BOTH 'checkout' and 'storefront' components were 100% unhealthy (111/111 checks failing, latency pegged at a 10,000ms timeout ceiling) - a full, unambiguous outage, not the mild 2.44% weekly blip I reported last week (that number was just this severe hour diluted across a whole week of otherwise-healthy checks). And critically, Nov 28 2025 is Black Friday - so if checkout itself was down for part of the busiest shopping day of the year, that's a much bigger finding than '10 merchants' cart-reminder emails failed,' and it would affect every merchant trying to check out then, not just the 10 using carts.remind. I didn't request a 15:00 bucket so I can't see the second (15:40) failure's health-check status yet, and my hourly buckets are too coarse to know exactly when the outage started/ended. Report 3 (ticket subject search) came back empty - no evidence in ticket subjects that shoppers flagged cart/reminder problems before Nov 24, so I can't support the 'support was an early warning sign' theory from this angle; I'm treating that as a genuine (if limited - subjects only, not message bodies) negative result rather than spending more budget on it, since the checkout-outage/Black-Friday angle is now clearly the more important thread with one night left. Tonight's 3 slots: (1) pin down the exact start/end of the checkout outage at finer time resolution; (2) measure the actual order-volume impact hour-by-hour on Nov 28 itself; (3) put Nov 28 in context against surrounding Fridays and Black Friday 2024 so Diego has a real 'how much did this cost us' number for leadership.
The engineer · 1 dry run on the empty schema, then the submission
SELECT date_trunc('hour', checked_at) + (floor(extract(minute from checked_at)/30)::int * interval '30 minute') AS interval_start, 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 >= timestamp '2025-11-28 13:00:00' AND checked_at < timestamp '2025-11-28 17:00:00' AND component IN ('checkout','storefront') GROUP BY 1,2 ORDER BY interval_start ASC, component ASC
“Query compiles cleanly with the expected columns. Submitting now.”
“Submitted. The report will produce one row per 30-minute interval (13:00–17:00 UTC, 8 intervals) for each of the checkout and storefront components, showing total checks, unhealthy checks, unhealthy rate %, and average latency — which should make the outage window visible as a spike in the unhealthy rate.”
As Lucas Moreau saw it, from the small dataset the agents worked on
interval_start | 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 2025-11-28 16:00:00 | checkout | 111 | 3 | 2.7 | 451.0 2025-11-28 16:00:00 | storefront | 111 | 0 | 0.0 | 101.0 (4 rows)
The memo and the queries that followed; links found by title, so inferred
Later queries that build on this report:
It builds on:
Changed only where the benchmark needs the same result on every system
SELECT date_trunc('hour', checked_at) + (floor(extract(minute from checked_at)/30)::int * interval '30 minute') AS interval_start, date_trunc('hour', checked_at) + (CAST(floor(extract(minute from checked_at)/30) AS int) * interval '30 minute') AS interval_start, 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 >= timestamp '2025-11-28 13:00:00' AND checked_at < timestamp '2025-11-28 17:00:00' AND component IN ('checkout','storefront')GROUP BY 1,2ORDER BY interval_start ASC, component ASCORDER BY interval_start ASC NULLS LAST, component ASC NULLS LAST
SELECT date_trunc('hour', checked_at) + (CAST(floor(extract(minute from checked_at)/30) AS int) * interval '30 minute') AS interval_start, 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 >= timestamp '2025-11-28 13:00:00' AND checked_at < timestamp '2025-11-28 17:00:00' AND component IN ('checkout','storefront') GROUP BY 1,2 ORDER BY interval_start 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 Outage Timeline, Nov 28 2025
Question: Exactly when did the checkout/storefront outage start and end on Nov 28 2025?
Grain: one row per 30-minute interval and component, for checkout and storefront only, 13:00-17:00 UTC on 2025-11-28
Columns:
- interval_start
- component
- total_checks
- unhealthy_checks
- unhealthy_rate_pct
- avg_latency_ms
Details: Table system_health_checks. Filter checked_at >= 2025-11-28 13:00:00 and < 2025-11-28 17:00:00, component in ('checkout','storefront'). Group by 30-minute interval (floor checked_at to the nearest 30 minutes) and component. unhealthy_rate_pct = is_healthy=false count / total_checks * 100, rounded 2dp. avg_latency_ms over non-null latency_ms, rounded to whole number. Order by interval_start 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) + (floor(extract(minute from checked_at)/30)::int * interval '30 minute') AS interval_start, 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 >= timestamp '2025-11-28 13:00:00' AND checked_at < timestamp '2025-11-28 17:00:00' AND component IN ('checkout','storefront') GROUP BY 1,2 ORDER BY interval_start ASC, component ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: interval_start, component, total_checks, unhealthy_checks, unhealthy_rate_pct, avg_latency_ms
submitSELECT date_trunc('hour', checked_at) + (floor(extract(minute from checked_at)/30)::int * interval '30 minute') AS interval_start, 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 >= timestamp '2025-11-28 13:00:00' AND checked_at < timestamp '2025-11-28 17:00:00' AND component IN ('checkout','storefront') GROUP BY 1,2 ORDER BY interval_start ASC, component ASC
Submitted to tonight's batch. Your work on this request is done.