Queries · q342 · VP Platform Engineering · week 2, night 3
“Did order placement actually drop during the checkout outage window on Nov 28 2025, compared to the rest of that same (peak traffic) day?”
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 2 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 EXTRACT(HOUR FROM o.placed_at)::int AS hour, COUNT(*) AS total_orders_placed, COUNT(DISTINCT o.tenant_id) AS distinct_tenants_with_orders, ROUND(SUM(o.grand_total / er.rate)) AS total_revenue_eur FROM orders o JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = DATE '2025-11-28' WHERE o.placed_at >= TIMESTAMP '2025-11-28 00:00:00' AND o.placed_at < TIMESTAMP '2025-11-29 00:00:00' GROUP BY EXTRACT(HOUR FROM o.placed_at) ORDER BY hour ASC
“Query compiles and returns the expected columns.”
“Submitted. Note: if any hour on 2025-11-28 had zero orders placed, it simply won't appear as a row in the output (the query only groups over hours with actual order activity) — worth flagging to Lucas since the whole point of the report is spotting a drop during the outage window.”
As Lucas Moreau saw it, from the small dataset the agents worked on
hour | total_orders_placed | distinct_tenants_with_orders | total_revenue_eur -----+---------------------+------------------------------+------------------ 0 | 3 | 1 | 542.0 1 | 3 | 2 | 1076.0 2 | 1 | 1 | 141.0 3 | 2 | 1 | 514.0 4 | 4 | 4 | 406.0 5 | 1 | 1 | 0.0 6 | 1 | 1 | 143.0 7 | 2 | 2 | 189.0 8 | 7 | 5 | 952.0 9 | 6 | 5 | 1153.0 10 | 2 | 2 | 177.0 11 | 5 | 4 | 3119.0 12 | 11 | 7 | 1467.0 13 | 10 | 5 | 994.0 14 | 1 | 1 | 103.0 16 | 10 | 7 | 1657.0 17 | 7 | 6 | 935.0 18 | 3 | 3 | 281.0 19 | 11 | 8 | 1400.0 20 | 11 | 7 | 1545.0 21 | 11 | 5 | 1383.0 22 | 11 | 4 | 3144.0 23 | 5 | 4 | 988.0 (23 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 EXTRACT(HOUR FROM o.placed_at)::int AS hour, CAST(EXTRACT(HOUR FROM o.placed_at) AS int) AS hour, COUNT(*) AS total_orders_placed, COUNT(DISTINCT o.tenant_id) AS distinct_tenants_with_orders, ROUND(SUM(o.grand_total / er.rate)) AS total_revenue_eur SUM(CAST(o.grand_total / er.rate AS NUMERIC(18,6))) AS total_revenue_eurFROM orders oJOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = DATE '2025-11-28'WHERE o.placed_at >= TIMESTAMP '2025-11-28 00:00:00' AND o.placed_at < TIMESTAMP '2025-11-29 00:00:00'GROUP BY EXTRACT(HOUR FROM o.placed_at)ORDER BY hour ASCORDER BY hour ASC NULLS LAST
SELECT CAST(EXTRACT(HOUR FROM o.placed_at) AS int) AS hour, COUNT(*) AS total_orders_placed, COUNT(DISTINCT o.tenant_id) AS distinct_tenants_with_orders, SUM(CAST(o.grand_total / er.rate AS NUMERIC(18,6))) AS total_revenue_eur FROM orders o JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = DATE '2025-11-28' WHERE o.placed_at >= TIMESTAMP '2025-11-28 00:00:00' AND o.placed_at < TIMESTAMP '2025-11-29 00:00:00' GROUP BY EXTRACT(HOUR FROM o.placed_at) ORDER BY hour 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: Order Volume by Hour on Black Friday 2025 (Nov 28) Question: Did order placement actually drop during the checkout outage window on Nov 28 2025, compared to the rest of that same (peak traffic) day? Grain: one row per hour of the day, for 2025-11-28 (UTC) Columns: - hour - total_orders_placed - distinct_tenants_with_orders - total_revenue_eur Details: Table orders, filtered to placed_at on 2025-11-28 (all 24 hours UTC). Group by hour of placed_at. total_revenue_eur = sum of grand_total converted to EUR via exchange_rates (base_code='EUR', quote_code=orders.currency_code, as_of=2025-11-28), rounded to whole number. distinct_tenants_with_orders = count distinct tenant_id. Order by hour ascending (0-23). Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runSELECT EXTRACT(HOUR FROM o.placed_at)::int AS hour, COUNT(*) AS total_orders_placed, COUNT(DISTINCT o.tenant_id) AS distinct_tenants_with_orders, ROUND(SUM(o.grand_total / er.rate)) AS total_revenue_eur FROM orders o JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = DATE '2025-11-28' WHERE o.placed_at >= TIMESTAMP '2025-11-28 00:00:00' AND o.placed_at < TIMESTAMP '2025-11-29 00:00:00' GROUP BY EXTRACT(HOUR FROM o.placed_at) ORDER BY hour ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: hour, total_orders_placed, distinct_tenants_with_orders, total_revenue_eur
submitSELECT EXTRACT(HOUR FROM o.placed_at)::int AS hour, COUNT(*) AS total_orders_placed, COUNT(DISTINCT o.tenant_id) AS distinct_tenants_with_orders, ROUND(SUM(o.grand_total / er.rate)) AS total_revenue_eur FROM orders o JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = DATE '2025-11-28' WHERE o.placed_at >= TIMESTAMP '2025-11-28 00:00:00' AND o.placed_at < TIMESTAMP '2025-11-29 00:00:00' GROUP BY EXTRACT(HOUR FROM o.placed_at) ORDER BY hour ASC
Submitted to tonight's batch. Your work on this request is done.