Queries · q133 · VP Platform Engineering · week 1, night 4
“Was the Nov-Dec ticket rise for the 10 carts.remind-affected tenants a real spike against their own baseline, or just their normal (or seasonal) level?”
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 1
Subject: 2025 platform reliability review — what I need by Friday
Lucas,
Leadership wants a year-in-review on platform reliability for 2025, and I'm leading it jointly with Samuel Whitcombe (Merchant Success). The deliverable is a memo that tells leadership how reliable the platform actually was last year, where it hurt merchants most, and what that implies for our 2026 investment priorities (reliability engineering headcount, which integrations/carriers to fix or replace, whether we need stricter rate limiting or SLAs). Samuel's side will map our findings to specific merchant relationships and churn risk, so where possible, surface things in a way that can be tied back to individual merchants rather than only platform-wide averages.
Given you only get a handful of report slots a night, here's what matters most to me, roughly in priority order:
If you have to cut scope to fit your nightly slots, protect #1 and #2 — those are what the memo absolutely must answer. The rest can wait or be addressed at a higher level if time runs short.
Let me know if anything above is ambiguous before you spend a slot on it.
Diego
Subject: Platform reliability review — what I need from my side
Hi Lucas,
As you pull together the 2025 reliability picture, here's what matters from the merchant-health side:
Happy to flag specific merchant names if that helps scope the analysis.
Best, Samuel
The analyst · week 1, night 4, request 1 of 3
Night 3 gave two important, slightly conflicting signals I need to resolve on this final night. First, Report 1 shows the Nov carts.remind incident did hit real top-tier accounts - Cragsley Outfitters, our #1 tenant by 2025 revenue, is among the 10 affected, alongside #4, #6, #7 and #8 - alongside four zero-revenue/zero-order tenants, so it's a mixed bag, not purely long-tail. Second, Report 2's ticket spike for the 10 affected tenants actually starts climbing weeks before the Nov 24 failure and peaks a week after, which could mean real merchant pain preceded our systems detecting it (bad for the 'are we flying blind' question) - but it could also just be that these are naturally higher-ticket (bigger) merchants, or a seasonal Black-Friday/holiday ticket bump everyone gets. I need a baseline comparison to tell those apart before I write that into the memo as a finding. With my last 3 slots I'll: (1) settle the ticket-spike-vs-baseline question properly using an Aug-Sep baseline for affected vs other tenants, so I can honestly say cause vs. coincidence; (2) finally give Diego a first cut at webhook reliability by individual endpoint, since Report 3 showed a flat ~5-6% of deliveries each month never resolve to success or hard failure (stuck retrying) and he explicitly wants to know which integrations are chronically troublesome; (3) check whether that same webhook stuck-rate clusters on the 10 affected tenants vs everyone else, which is exactly Samuel's cross-dimension clustering question for his top-tier accounts.
The engineer · 1 dry run on the empty schema, then the submission
WITH affected AS ( SELECT DISTINCT jr.tenant_id FROM job_runs jr JOIN jobs j ON jr.job_id = j.id WHERE j.handler = 'carts.remind' AND jr.status = 'failed' AND jr.queued_at >= DATE '2025-11-01' AND jr.queued_at < DATE '2025-12-01' ), groups AS ( SELECT t.id AS tenant_id, CASE WHEN a.tenant_id IS NOT NULL THEN 'Affected' ELSE 'Other' END AS group_name FROM tenants t LEFT JOIN affected a ON a.tenant_id = t.id ), tenant_counts AS ( SELECT group_name, COUNT(*) AS tenant_count FROM groups GROUP BY group_name ), ticket_counts AS ( SELECT g.group_name, SUM(CASE WHEN tk.created_at >= DATE '2025-08-01' AND tk.created_at < DATE '2025-10-01' THEN 1 ELSE 0 END) AS baseline_tickets, SUM(CASE WHEN tk.created_at >= DATE '2025-11-01' AND tk.created_at < DATE '2026-01-01' THEN 1 ELSE 0 END) AS incident_tickets FROM groups g LEFT JOIN tickets tk ON tk.tenant_id = g.tenant_id GROUP BY g.group_name ) SELECT tc.group_name, tcn.tenant_count, ROUND(tc.baseline_tickets::numeric / tcn.tenant_count / (61/7.0), 2) AS avg_weekly_tickets_per_tenant_baseline, ROUND(tc.incident_tickets::numeric / tcn.tenant_count / (61/7.0), 2) AS avg_weekly_tickets_per_tenant_incident, ROUND( (((tc.incident_tickets::numeric / tcn.tenant_count) - (tc.baseline_tickets::numeric / tcn.tenant_count)) / NULLIF(tc.baseline_tickets::numeric / tcn.tenant_count, 0)) * 100 , 1) AS pct_change FROM ticket_counts tc JOIN tenant_counts tcn ON tcn.group_name = tc.group_name ORDER BY tc.group_name
“Query compiles and returns the expected columns. Submitting.”
“Submitted. The query:
- Identifies the "Affected" group as tenants with at least one failed job_runs row (joined to jobs on handler='carts.remind') queued in November 2025, and labels every other tenant as "Other".
- Counts tickets per group in the baseline window (2025-08-01 to 2025-09-30) and incident window (2025-11-01 to 2025-12-31), each 61 days.
- Computes avg_weekly_tickets_per_tenant as tickets ÷ tenant_count ÷ (61/7), rounded to 2dp for each period, and pct_change from the unrounded per-tenant weekly rates, rounded to 1dp.
- Returns exactly two rows (Affected, Other), ordered by group_name.”
As Lucas Moreau saw it, from the small dataset the agents worked on
group_name | tenant_count | avg_weekly_tickets_per_tenant_baseline | avg_weekly_tickets_per_tenant_incident | pct_change -----------+--------------+----------------------------------------+----------------------------------------+----------- Affected | 10 | 1.89 | 5.19 | 173.9 Other | 110 | 0.15 | 0.36 | 147.5 (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:
5. Ticket-volume spike is real but mostly seasonal, not clearly caused by the incident. Affected tenants' ticket rate rose 174% from an Aug–Sep baseline (1.89→5.19/tenant/week); other tenants rose 148% over the same two periods (0.15→0.36). Source: "Support Tickets: Affected vs Other Tenants." Since both groups roughly doubled-to-tripled, most of this looks like a platform-wide holiday-season ticket bump. The extra ~26pp lift for affected tenants is suggestive but not strong evidence with only 10 tenants — I'd call this correlation, not proven cause.
Later queries that build on this report:
It builds on:
Changed only where the benchmark needs the same result on every system
WITH affected AS ( SELECT DISTINCT jr.tenant_id SELECT DISTINCT jr.tenant_id AS tenant_id FROM job_runs jr JOIN jobs j ON jr.job_id = j.id WHERE j.handler = 'carts.remind' AND jr.status = 'failed' AND jr.queued_at >= DATE '2025-11-01' AND jr.queued_at < DATE '2025-12-01'),groups AS ( SELECT t.id AS tenant_id, CASE WHEN a.tenant_id IS NOT NULL THEN 'Affected' ELSE 'Other' END AS group_name FROM tenants t LEFT JOIN affected a ON a.tenant_id = t.id),tenant_counts AS ( SELECT group_name, COUNT(*) AS tenant_count FROM groups GROUP BY group_name),ticket_counts AS ( SELECT g.group_name, SELECT g.group_name AS group_name, SUM(CASE WHEN tk.created_at >= DATE '2025-08-01' AND tk.created_at < DATE '2025-10-01' THEN 1 ELSE 0 END) AS baseline_tickets, SUM(CASE WHEN tk.created_at >= DATE '2025-11-01' AND tk.created_at < DATE '2026-01-01' THEN 1 ELSE 0 END) AS incident_tickets FROM groups g LEFT JOIN tickets tk ON tk.tenant_id = g.tenant_id GROUP BY g.group_name)SELECT tc.group_name, tcn.tenant_count, ROUND(tc.baseline_tickets::numeric / tcn.tenant_count / (61/7.0), 2) AS avg_weekly_tickets_per_tenant_baseline, ROUND(tc.incident_tickets::numeric / tcn.tenant_count / (61/7.0), 2) AS avg_weekly_tickets_per_tenant_incident, ROUND( (((tc.incident_tickets::numeric / tcn.tenant_count) - (tc.baseline_tickets::numeric / tcn.tenant_count)) / NULLIF(tc.baseline_tickets::numeric / tcn.tenant_count, 0)) * 100 , 1) AS pct_change tc.group_name AS group_name, tcn.tenant_count AS tenant_count, CAST(tc.baseline_tickets AS numeric(18,6)) / tcn.tenant_count / (61/7.0) AS avg_weekly_tickets_per_tenant_baseline, CAST(tc.incident_tickets AS numeric(18,6)) / tcn.tenant_count / (61/7.0) AS avg_weekly_tickets_per_tenant_incident, (((CAST(tc.incident_tickets AS numeric(18,6)) / tcn.tenant_count) - (CAST(tc.baseline_tickets AS numeric(18,6)) / tcn.tenant_count)) / NULLIF(CAST(tc.baseline_tickets AS numeric(18,6)) / tcn.tenant_count, 0)) * 100 AS pct_changeFROM ticket_counts tcJOIN tenant_counts tcn ON tcn.group_name = tc.group_nameORDER BY tc.group_nameORDER BY tc.group_name NULLS LAST
WITH affected AS ( SELECT DISTINCT jr.tenant_id AS tenant_id FROM job_runs jr JOIN jobs j ON jr.job_id = j.id WHERE j.handler = 'carts.remind' AND jr.status = 'failed' AND jr.queued_at >= DATE '2025-11-01' AND jr.queued_at < DATE '2025-12-01' ), groups AS ( SELECT t.id AS tenant_id, CASE WHEN a.tenant_id IS NOT NULL THEN 'Affected' ELSE 'Other' END AS group_name FROM tenants t LEFT JOIN affected a ON a.tenant_id = t.id ), tenant_counts AS ( SELECT group_name, COUNT(*) AS tenant_count FROM groups GROUP BY group_name ), ticket_counts AS ( SELECT g.group_name AS group_name, SUM(CASE WHEN tk.created_at >= DATE '2025-08-01' AND tk.created_at < DATE '2025-10-01' THEN 1 ELSE 0 END) AS baseline_tickets, SUM(CASE WHEN tk.created_at >= DATE '2025-11-01' AND tk.created_at < DATE '2026-01-01' THEN 1 ELSE 0 END) AS incident_tickets FROM groups g LEFT JOIN tickets tk ON tk.tenant_id = g.tenant_id GROUP BY g.group_name ) SELECT tc.group_name AS group_name, tcn.tenant_count AS tenant_count, CAST(tc.baseline_tickets AS numeric(18,6)) / tcn.tenant_count / (61/7.0) AS avg_weekly_tickets_per_tenant_baseline, CAST(tc.incident_tickets AS numeric(18,6)) / tcn.tenant_count / (61/7.0) AS avg_weekly_tickets_per_tenant_incident, (((CAST(tc.incident_tickets AS numeric(18,6)) / tcn.tenant_count) - (CAST(tc.baseline_tickets AS numeric(18,6)) / tcn.tenant_count)) / NULLIF(CAST(tc.baseline_tickets AS numeric(18,6)) / tcn.tenant_count, 0)) * 100 AS pct_change FROM ticket_counts tc JOIN tenant_counts tcn ON tcn.group_name = tc.group_name ORDER BY tc.group_name 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: Support Tickets: Affected vs Other Tenants, Baseline vs Incident Period Question: Was the Nov-Dec ticket rise for the 10 carts.remind-affected tenants a real spike against their own baseline, or just their normal (or seasonal) level? Grain: one row per tenant group (Affected vs Other), comparing two time periods Columns: - group_name - tenant_count - avg_weekly_tickets_per_tenant_baseline - avg_weekly_tickets_per_tenant_incident - pct_change Details: Table tickets, created_at. 'Affected' = the same 10 tenant_ids with a failed job_runs row for jobs.handler='carts.remind' queued in Nov 2025 (as established in prior reports); 'Other' = all remaining tenants. baseline period = 2025-08-01 to 2025-09-30 (61 days); incident period = 2025-11-01 to 2025-12-31 (61 days), so periods are equal length for a fair comparison. avg_weekly_tickets_per_tenant = (count of tickets for the group in that period) / tenant_count / (period_days/7.0), rounded 2dp. pct_change = (incident value - baseline value) / baseline value * 100, rounded 1dp. Two rows total: one per group. Order by group_name. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runWITH affected AS ( SELECT DISTINCT jr.tenant_id FROM job_runs jr JOIN jobs j ON jr.job_id = j.id WHERE j.handler = 'carts.remind' AND jr.status = 'failed' AND jr.queued_at >= DATE '2025-11-01' AND jr.queued_at < DATE '2025-12-01' ), groups AS ( SELECT t.id AS tenant_id, CASE WHEN a.tenant_id IS NOT NULL THEN 'Affected' ELSE 'Other' END AS group_name FROM tenants t LEFT JOIN affected a ON a.tenant_id = t.id ), tenant_counts AS ( SELECT group_name, COUNT(*) AS tenant_count FROM groups GROUP BY group_name ), ticket_counts AS ( SELECT g.group_name, SUM(CASE WHEN tk.created_at >= DATE '2025-08-01' AND tk.created_at < DATE '2025-10-01' THEN 1 ELSE 0 END) AS baseline_tickets, SUM(CASE WHEN tk.created_at >= DATE '2025-11-01' AND tk.created_at < DATE '2026-01-01' THEN 1 ELSE 0 END) AS incident_tickets FROM groups g LEFT JOIN tickets tk ON tk.tenant_id = g.tenant_id GROUP BY g.group_name ) SELECT tc.group_name, tcn.tenant_count, ROUND(tc.baseline_tickets::numeric / tcn.tenant_count / (61/7.0), 2) AS avg_weekly_tickets_per_tenant_baseline, ROUND(tc.incident_tickets::numeric / tcn.tenant_count / (61/7.0), 2) AS avg_weekly_tickets_per_tenant_incident, ROUND( (((tc.incident_tickets::numeric / tcn.tenant_count) - (tc.baseline_tickets::numeric / tcn.tenant_count)) / NULLIF(tc.baseline_tickets::numeric / tcn.tenant_count, 0)) * 100 , 1) AS pct_change FROM ticket_counts tc JOIN tenant_counts tcn ON tcn.group_name = tc.group_name ORDER BY tc.group_name
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: group_name, tenant_count, avg_weekly_tickets_per_tenant_baseline, avg_weekly_tickets_per_tenant_incident, pct_change
submitWITH affected AS ( SELECT DISTINCT jr.tenant_id FROM job_runs jr JOIN jobs j ON jr.job_id = j.id WHERE j.handler = 'carts.remind' AND jr.status = 'failed' AND jr.queued_at >= DATE '2025-11-01' AND jr.queued_at < DATE '2025-12-01' ), groups AS ( SELECT t.id AS tenant_id, CASE WHEN a.tenant_id IS NOT NULL THEN 'Affected' ELSE 'Other' END AS group_name FROM tenants t LEFT JOIN affected a ON a.tenant_id = t.id ), tenant_counts AS ( SELECT group_name, COUNT(*) AS tenant_count FROM groups GROUP BY group_name ), ticket_counts AS ( SELECT g.group_name, SUM(CASE WHEN tk.created_at >= DATE '2025-08-01' AND tk.created_at < DATE '2025-10-01' THEN 1 ELSE 0 END) AS baseline_tickets, SUM(CASE WHEN tk.created_at >= DATE '2025-11-01' AND tk.created_at < DATE '2026-01-01' THEN 1 ELSE 0 END) AS incident_tickets FROM groups g LEFT JOIN tickets tk ON tk.tenant_id = g.tenant_id GROUP BY g.group_name ) SELECT tc.group_name, tcn.tenant_count, ROUND(tc.baseline_tickets::numeric / tcn.tenant_count / (61/7.0), 2) AS avg_weekly_tickets_per_tenant_baseline, ROUND(tc.incident_tickets::numeric / tcn.tenant_count / (61/7.0), 2) AS avg_weekly_tickets_per_tenant_incident, ROUND( (((tc.incident_tickets::numeric / tcn.tenant_count) - (tc.baseline_tickets::numeric / tcn.tenant_count)) / NULLIF(tc.baseline_tickets::numeric / tcn.tenant_count, 0)) * 100 , 1) AS pct_change FROM ticket_counts tc JOIN tenant_counts tcn ON tcn.group_name = tc.group_name ORDER BY tc.group_name
Submitted to tonight's batch. Your work on this request is done.