Queries · q135 · VP Platform Engineering · week 1, night 4
“Do the 10 tenants hit by the Nov carts.remind job failures also show worse webhook delivery reliability than other tenants, suggesting clustered reliability problems on the same accounts?”
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 3 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 j.id = jr.job_id WHERE j.name = 'carts.remind' AND jr.status = 'failed' AND jr.started_at >= DATE '2025-11-01' AND jr.started_at < DATE '2025-12-01' ) SELECT CASE WHEN wd.tenant_id IN (SELECT tenant_id FROM affected) THEN 'Affected' ELSE 'Other' END AS group_name, COUNT(DISTINCT wd.tenant_id) AS tenant_count, COUNT(*) AS total_deliveries_2025, ROUND(100.0 * SUM(CASE WHEN wd.status = 'success' THEN 1 ELSE 0 END) / COUNT(*), 2) AS success_rate_pct, ROUND(100.0 * SUM(CASE WHEN wd.status = 'failed' THEN 1 ELSE 0 END) / COUNT(*), 2) AS failed_rate_pct, ROUND(100.0 * SUM(CASE WHEN wd.status IN ('pending', 'retrying') THEN 1 ELSE 0 END) / COUNT(*), 2) AS pending_or_retrying_rate_pct FROM webhook_deliveries wd JOIN webhooks w ON w.id = wd.webhook_id WHERE wd.created_at >= TIMESTAMP '2025-01-01 00:00:00' AND wd.created_at < TIMESTAMP '2026-01-01 00:00:00' GROUP BY group_name ORDER BY group_name
“Query compiles cleanly and returns exactly the requested columns.”
“Submitted. One note on an assumption I had to make: since I don't have direct access to the tenant list from the "prior reports," I reconstructed the Affected cohort inline as the distinct tenants with a failed job_runs row where jobs.name = 'carts.remind' in November 2025. If that prior report identified the cohort differently (e.g., a different job key or date boundary), the counts could diverge slightly from the expected 10 tenants — flagged this in the submit note for the analyst to verify.”
As Lucas Moreau saw it, from the small dataset the agents worked on
group_name | tenant_count | total_deliveries_2025 | success_rate_pct | failed_rate_pct | pending_or_retrying_rate_pct -----------+--------------+-----------------------+------------------+-----------------+----------------------------- Other | 55 | 49752 | 94.31 | 0.0 | 5.68 (1 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
WITH affected AS ( SELECT DISTINCT jr.tenant_id SELECT DISTINCT jr.tenant_id AS tenant_id FROM job_runs jr JOIN jobs j ON j.id = jr.job_id WHERE j.name = 'carts.remind' AND jr.status = 'failed' AND jr.started_at >= DATE '2025-11-01' AND jr.started_at < DATE '2025-12-01')SELECT CASE WHEN wd.tenant_id IN (SELECT tenant_id FROM affected) THEN 'Affected' ELSE 'Other' END AS group_name, COUNT(DISTINCT wd.tenant_id) AS tenant_count, COUNT(*) AS total_deliveries_2025, ROUND(100.0 * SUM(CASE WHEN wd.status = 'success' THEN 1 ELSE 0 END) / COUNT(*), 2) AS success_rate_pct, ROUND(100.0 * SUM(CASE WHEN wd.status = 'failed' THEN 1 ELSE 0 END) / COUNT(*), 2) AS failed_rate_pct, ROUND(100.0 * SUM(CASE WHEN wd.status IN ('pending', 'retrying') THEN 1 ELSE 0 END) / COUNT(*), 2) AS pending_or_retrying_rate_pct 100.0 * SUM(CASE WHEN wd.status = 'success' THEN 1 ELSE 0 END) / COUNT(*) AS success_rate_pct, 100.0 * SUM(CASE WHEN wd.status = 'failed' THEN 1 ELSE 0 END) / COUNT(*) AS failed_rate_pct, 100.0 * SUM(CASE WHEN wd.status IN ('pending', 'retrying') THEN 1 ELSE 0 END) / COUNT(*) AS pending_or_retrying_rate_pctFROM webhook_deliveries wdJOIN webhooks w ON w.id = wd.webhook_idWHERE wd.created_at >= TIMESTAMP '2025-01-01 00:00:00' AND wd.created_at < TIMESTAMP '2026-01-01 00:00:00'GROUP BY group_nameORDER BY group_nameORDER BY group_name NULLS LAST
WITH affected AS ( SELECT DISTINCT jr.tenant_id AS tenant_id FROM job_runs jr JOIN jobs j ON j.id = jr.job_id WHERE j.name = 'carts.remind' AND jr.status = 'failed' AND jr.started_at >= DATE '2025-11-01' AND jr.started_at < DATE '2025-12-01' ) SELECT CASE WHEN wd.tenant_id IN (SELECT tenant_id FROM affected) THEN 'Affected' ELSE 'Other' END AS group_name, COUNT(DISTINCT wd.tenant_id) AS tenant_count, COUNT(*) AS total_deliveries_2025, 100.0 * SUM(CASE WHEN wd.status = 'success' THEN 1 ELSE 0 END) / COUNT(*) AS success_rate_pct, 100.0 * SUM(CASE WHEN wd.status = 'failed' THEN 1 ELSE 0 END) / COUNT(*) AS failed_rate_pct, 100.0 * SUM(CASE WHEN wd.status IN ('pending', 'retrying') THEN 1 ELSE 0 END) / COUNT(*) AS pending_or_retrying_rate_pct FROM webhook_deliveries wd JOIN webhooks w ON w.id = wd.webhook_id WHERE wd.created_at >= TIMESTAMP '2025-01-01 00:00:00' AND wd.created_at < TIMESTAMP '2026-01-01 00:00:00' GROUP BY group_name ORDER BY 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: Webhook Delivery Reliability: Carts.remind-Affected Tenants vs Others, 2025 Question: Do the 10 tenants hit by the Nov carts.remind job failures also show worse webhook delivery reliability than other tenants, suggesting clustered reliability problems on the same accounts? Grain: one row per tenant group (Affected vs Other) Columns: - group_name - tenant_count - total_deliveries_2025 - success_rate_pct - failed_rate_pct - pending_or_retrying_rate_pct Details: Table webhook_deliveries joined to webhooks for tenant_id, filtered to created_at in 2025. 'Affected' = the same 10 tenant_ids defined in prior reports (failed carts.remind job_runs in Nov 2025); 'Other' = all remaining tenants with at least one webhook delivery in 2025. Rates defined as in the endpoint-level request: success_rate_pct, failed_rate_pct, pending_or_retrying_rate_pct as shares of total_deliveries_2025 for the group, rounded 2dp. Two rows total. 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 j.id = jr.job_id WHERE j.name = 'carts.remind' AND jr.status = 'failed' AND jr.started_at >= DATE '2025-11-01' AND jr.started_at < DATE '2025-12-01' ) SELECT CASE WHEN wd.tenant_id IN (SELECT tenant_id FROM affected) THEN 'Affected' ELSE 'Other' END AS group_name, COUNT(DISTINCT wd.tenant_id) AS tenant_count, COUNT(*) AS total_deliveries_2025, ROUND(100.0 * SUM(CASE WHEN wd.status = 'success' THEN 1 ELSE 0 END) / COUNT(*), 2) AS success_rate_pct, ROUND(100.0 * SUM(CASE WHEN wd.status = 'failed' THEN 1 ELSE 0 END) / COUNT(*), 2) AS failed_rate_pct, ROUND(100.0 * SUM(CASE WHEN wd.status IN ('pending', 'retrying') THEN 1 ELSE 0 END) / COUNT(*), 2) AS pending_or_retrying_rate_pct FROM webhook_deliveries wd JOIN webhooks w ON w.id = wd.webhook_id WHERE wd.created_at >= TIMESTAMP '2025-01-01 00:00:00' AND wd.created_at < TIMESTAMP '2026-01-01 00:00:00' GROUP BY group_name ORDER BY group_name
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: group_name, tenant_count, total_deliveries_2025, success_rate_pct, failed_rate_pct, pending_or_retrying_rate_pct
submitWITH affected AS ( SELECT DISTINCT jr.tenant_id FROM job_runs jr JOIN jobs j ON j.id = jr.job_id WHERE j.name = 'carts.remind' AND jr.status = 'failed' AND jr.started_at >= DATE '2025-11-01' AND jr.started_at < DATE '2025-12-01' ) SELECT CASE WHEN wd.tenant_id IN (SELECT tenant_id FROM affected) THEN 'Affected' ELSE 'Other' END AS group_name, COUNT(DISTINCT wd.tenant_id) AS tenant_count, COUNT(*) AS total_deliveries_2025, ROUND(100.0 * SUM(CASE WHEN wd.status = 'success' THEN 1 ELSE 0 END) / COUNT(*), 2) AS success_rate_pct, ROUND(100.0 * SUM(CASE WHEN wd.status = 'failed' THEN 1 ELSE 0 END) / COUNT(*), 2) AS failed_rate_pct, ROUND(100.0 * SUM(CASE WHEN wd.status IN ('pending', 'retrying') THEN 1 ELSE 0 END) / COUNT(*), 2) AS pending_or_retrying_rate_pct FROM webhook_deliveries wd JOIN webhooks w ON w.id = wd.webhook_id WHERE wd.created_at >= TIMESTAMP '2025-01-01 00:00:00' AND wd.created_at < TIMESTAMP '2026-01-01 00:00:00' GROUP BY group_name ORDER BY group_name
Submitted to tonight's batch. Your work on this request is done.