Queries · q339 · VP Platform Engineering · week 2, night 2
“Did our internal health checks detect a problem around the two carts.remind failure windows (2025-11-28, roughly 14:40 and 15:40 UTC)?”
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 2, request 2 of 3
Night 1 results reshape the story in an important way. Report 1 shows the stuck-retry pattern touches 56 tenants (not a handful) at wildly different % rates, but many of the worst percentages (e.g. 50%, 38%) are tiny-volume tenants (1-2 stuck deliveries) - a small-sample artifact, not a real signal. When I compute absolute stuck-delivery counts (rate x volume) by hand from that report, ~91% of all stuck deliveries sit with just 4 tenants: Brennquell Kaffee (~1,574 stuck), Cragsley Outfitters (~700), Ampfeld Elektronik (~161), and Marrowby & Vane (~88) - and these are the same 4 of our 10 carts.remind-affected tenants with real order volume. That's the concentration story Diego wants, and I don't need another slot to prove it, it's derivable from what we have. Report 2 shows the platform-wide stuck rate has been flat at ~5.2-6.6% every month all year (not the ~15% I quoted last week, which was a selection-biased view of only the worst 20 endpoints) - so no recent regression at the platform level. Report 3 is a clean, high-confidence answer on carts.remind: all 20 failures are the identical error ('503 Service Unavailable: storefront API') in two one-hour-apart batches on Nov 28, across the same 10 tenants both times - this reads as a brief outage of one shared upstream dependency, not a tenant-specific or load issue, and not really a 'config/feature flag' story as I speculated last week. Tonight I'll: (1) check whether the stuck rate for those same 4 high-impact tenants has itself been stable all year or is recent/worsening (refines Diego's #2 to the account level that matters for the fix-it conversation); (2) pull internal health checks around the Nov 28 14:00-16:00 window to see if monitoring caught the storefront-API blip in real time (closes out #3 with hard evidence); (3) do a first lightweight look at ticket subject lines from the 10 affected tenants in the weeks before Nov 24 to give Diego his requested 'rough read' on whether support was an early warning signal (#4).
The engineer · 1 dry run on the empty schema, then the submission
SELECT date_trunc('hour', checked_at) AS 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 >= TIMESTAMP '2025-11-28 12:00:00' AND checked_at < TIMESTAMP '2025-11-28 18:00:00' GROUP BY 1, 2 ORDER BY hour ASC, component ASC
“Query compiles cleanly with the expected columns. Submitting now.”
“Submitted. The report groups system_health_checks by hour and component for the 2025-11-28 12:00–18:00 UTC window, with total checks, unhealthy checks, unhealthy rate (%), and average latency — exactly matching the requested grain and columns, so Lucas can see whether the two ~14:40 and ~15:40 UTC carts.remind failure windows line up with spikes in unhealthy internal health checks.”
As Lucas Moreau saw it, from the small dataset the agents worked on
hour | component | total_checks | unhealthy_checks | unhealthy_rate_pct | avg_latency_ms --------------------+----------------------+--------------+------------------+--------------------+--------------- 2025-11-28 12:00:00 | accounting_connector | 5 | 1 | 20.0 | 2085.0 2025-11-28 12:00:00 | checkout | 111 | 1 | 0.9 | 294.0 2025-11-28 12:00:00 | erp_connector | 54 | 2 | 3.7 | 466.0 2025-11-28 12:00:00 | marketplace_feed | 15 | 2 | 13.33 | 1422.0 2025-11-28 12:00:00 | storefront | 111 | 1 | 0.9 | 194.0 2025-11-28 12:00:00 | wms_connector | 13 | 0 | 0.0 | 99.0 2025-11-28 14:00:00 | accounting_connector | 5 | 0 | 0.0 | 96.0 2025-11-28 14:00:00 | checkout | 111 | 111 | 100.0 | 10000.0 2025-11-28 14:00:00 | erp_connector | 54 | 2 | 3.7 | 461.0 2025-11-28 14:00:00 | marketplace_feed | 15 | 0 | 0.0 | 101.0 2025-11-28 14:00:00 | storefront | 111 | 111 | 100.0 | 10000.0 2025-11-28 14:00:00 | wms_connector | 13 | 0 | 0.0 | 110.0 2025-11-28 16:00:00 | accounting_connector | 5 | 0 | 0.0 | 83.0 2025-11-28 16:00:00 | checkout | 111 | 3 | 2.7 | 451.0 2025-11-28 16:00:00 | erp_connector | 54 | 0 | 0.0 | 102.0 2025-11-28 16:00:00 | marketplace_feed | 15 | 1 | 6.67 | 730.0 2025-11-28 16:00:00 | storefront | 111 | 0 | 0.0 | 101.0 2025-11-28 16:00:00 | wms_connector | 13 | 0 | 0.0 | 119.0 (18 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) AS 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 >= TIMESTAMP '2025-11-28 12:00:00' AND checked_at < TIMESTAMP '2025-11-28 18:00:00'GROUP BY 1, 2ORDER BY hour ASC, component ASCORDER BY hour ASC NULLS LAST, component ASC NULLS LAST
SELECT date_trunc('hour', checked_at) AS 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 >= TIMESTAMP '2025-11-28 12:00:00' AND checked_at < TIMESTAMP '2025-11-28 18:00:00' GROUP BY 1, 2 ORDER BY 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: Internal Health Check Signal Around the Nov 28 2025 Storefront API Outage Question: Did our internal health checks detect a problem around the two carts.remind failure windows (2025-11-28, roughly 14:40 and 15:40 UTC)? Grain: one row per hour and component, for 2025-11-28 between 12:00 and 18:00 UTC Columns: - hour - component - total_checks - unhealthy_checks - unhealthy_rate_pct - avg_latency_ms Details: Table system_health_checks. Filter checked_at >= 2025-11-28 12:00:00 and < 2025-11-28 18:00:00. Group by hour of checked_at (truncated to the hour) 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 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 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 >= TIMESTAMP '2025-11-28 12:00:00' AND checked_at < TIMESTAMP '2025-11-28 18:00:00' GROUP BY 1, 2 ORDER BY hour ASC, component ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: hour, component, total_checks, unhealthy_checks, unhealthy_rate_pct, avg_latency_ms
submitSELECT date_trunc('hour', checked_at) AS 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 >= TIMESTAMP '2025-11-28 12:00:00' AND checked_at < TIMESTAMP '2025-11-28 18:00:00' GROUP BY 1, 2 ORDER BY hour ASC, component ASC
Submitted to tonight's batch. Your work on this request is done.