Queries · q012 · Chief Executive Officer · week 1, night 4

Operational warning signs trend: refunds, payment failures, support volume

“Are payment, refund, or support problems trending up over the last two years in a way that could become a board-level concern?”

Manager · asked the questionMaren OkaforChief Executive Officer. Joined two weeks ago from a European payments company. Board meeting at the end of January.

Analyst · planned the reportHannah BrooksMethodical: starts with an overview, then drills into whatever looks unusual.

Engineer · wrote the SQLFelixJunior engineer who nests subqueries in FROM and WHERE (IN, EXISTS, correlated subqueries) rather than CTEs.

Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told

Maren Okafor writes to Hannah Brooks
The manager, at the start of week 1
Subject: A few questions to get me oriented before the board meeting

Hi Hannah,

Thanks for taking these on this week — I know slots are tight, so please prioritize in the order below and don't force everything into this week if it doesn't fit cleanly. I'd rather get three solid answers than five rushed ones.
1. Where does our revenue actually come from? I'd like a sense of how concentrated we are — which merchants and which verticals are driving the bulk of our volume and fees today, and how that's shifted over the last year or so. I'm trying to understand if we're overly dependent on a handful of accounts.
2. How healthy is our growth? Are we gaining merchants and transaction volume, or is it flat/declining? I'd like to see the trend over the last several quarters, and ideally whether growth is coming from new merchants or from existing ones doing more.
3. Where do we make our money, and how is that mix changing? Between platform fees and payment processing, I want to understand the balance, and whether one is growing faster than the other.
4. What's working and what's not, vertical by vertical? I'd like a simple read on which verticals are thriving versus struggling — whether that's measured by growth, retention, or merchant satisfaction, whatever tells the clearest story.
5. (If time allows) Are there any warning signs I should know about — merchants at risk of leaving, payment or fulfillment problems, support issues — anything that could become a board-level concern if it's not already on my radar.
What I need this for: I'm building my mental model of the business ahead of the board meeting at the end of January, and I want my first real conversation with the board to show I understand where we're strong, where we're exposed, and what I'm watching closely. Your Friday memo will be the backbone of that.

Let me know if any of these need narrowing once you've had a look at what's feasible — happy to discuss before you lock in the report requests.

Thanks, Maren
Hannah Brooks plans the night
The analyst · week 1, night 4, request 3 of 3
Priority 1 is now fully resolved and consistent (corrected vertical report sums to ~€53,186, matching the merchant-level total exactly). Priority 3 is answered as far as the data allows: take rate is stable (~2.5-2.8%, no clear trend), and I've flagged that the platform-plan billing side genuinely isn't modeled in this warehouse. Priority 2 is covered from night 2. For this final night I'm using all 3 slots to close out priority 4 (vertical health) and priority 5 (warning signs), which haven't been touched yet, since they're explicitly what the Friday memo still needs. One thing jumped out while reviewing: the vertical revenue report only found 57 merchants with a settled payout in the trailing 12 months, versus 111 total tenants reported as 'cumulative active merchants' in night 2 (which only used signup date, not actual activity) — that's a big gap worth quantifying directly, since it could mean either a lot of merchants are still ramping up or a lot have gone quiet/churned. I'm building the status/activity report, a vertical-level health report (status mix + support load), and a company-wide operational trend report (payment failures, refunds, tickets, CSAT) to surface any board-level concerns.
Operational warning signs trend: refunds, payment failures, support volume
Question
Are payment, refund, or support problems trending up over the last two years in a way that could become a board-level concern?
One row per
calendar quarter
Columns
- quarter
- payment failure rate (%, failed payment_attempts / all payment_attempts)
- refund rate (%, succeeded payment_refunds amount EUR / captured payments amount EUR)
- tickets per 100 orders
- average CSAT score (1-5)

Felix writes the SQL

The engineer · 1 dry run on the empty schema, then the submission

Dry run 1 · STATUS: COMPILES (dry run on the empty schema, no data read)

SELECT
  q.year_num || '-Q' || q.quarter_num AS quarter,
  ROUND(pa.failure_rate, 2) AS payment_failure_rate_pct,
  ROUND(CASE WHEN pm.captured_eur > 0 THEN rf.refund_eur / pm.captured_eur * 100 ELSE NULL END, 2) AS refund_rate_pct,
  ROUND(CASE WHEN ord.order_count > 0 THEN tk.ticket_count * 100.0 / ord.order_count ELSE NULL END, 2) AS tickets_per_100_orders,
  ROUND(cs.avg_csat, 2) AS avg_csat_score
FROM (
  SELECT DISTINCT year_num, quarter_num
  FROM calendar_days
  WHERE calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
) q
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num,
    SUM(CASE WHEN pa.status = 'failed' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS failure_rate
  FROM payment_attempts pa
  JOIN calendar_days cd ON cd.calendar_date = CAST(pa.attempted_at AS DATE)
  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) pa ON pa.year_num = q.year_num AND pa.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num,
    SUM(pr.amount / er.rate) AS refund_eur
  FROM payment_refunds pr
  JOIN calendar_days cd ON cd.calendar_date = CAST(pr.processed_at AS DATE)
  JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = cd.calendar_date
  WHERE pr.status = 'succeeded'
    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) rf ON rf.year_num = q.year_num AND rf.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num,
    SUM(p.amount / er.rate) AS captured_eur
  FROM payments p
  JOIN calendar_days cd ON cd.calendar_date = CAST(p.captured_at AS DATE)
  JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = cd.calendar_date
  WHERE p.status IN ('captured', 'partially_refunded', 'refunded')
    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) pm ON pm.year_num = q.year_num AND pm.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num, COUNT(*) AS ticket_count
  FROM tickets t
  JOIN calendar_days cd ON cd.calendar_date = CAST(t.created_at AS DATE)
  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) tk ON tk.year_num = q.year_num AND tk.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num, COUNT(*) AS order_count
  FROM orders o
  JOIN calendar_days cd ON cd.calendar_date = CAST(o.placed_at AS DATE)
  WHERE o.status IN ('paid', 'partially_fulfilled', 'fulfilled', 'refunded')
    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) ord ON ord.year_num = q.year_num AND ord.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num, AVG(c.score) AS avg_csat
  FROM csat_responses c
  JOIN calendar_days cd ON cd.calendar_date = CAST(c.submitted_at AS DATE)
  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) cs ON cs.year_num = q.year_num AND cs.quarter_num = q.quarter_num
ORDER BY q.year_num ASC, q.quarter_num ASC

“The query compiles and returns the expected 5 columns at one row per quarter. This aggregates platform-wide across all tenants/currencies, converting multi-currency amounts to EUR for the refund rate. Submitting now.”

Submitted · the SQL of the last dry run, unchanged · Submitted to tonight's batch. Your work on this request is done.

“Submitted. The report covers 2024-Q1 through 2025-Q4 with one row per quarter, showing payment failure rate, EUR-normalized refund rate, tickets per 100 orders, and average CSAT score, ordered chronologically so the CEO's analyst can spot any upward trend in operational risk.”

The report comes back overnight

As Hannah Brooks saw it, from the small dataset the agents worked on

quarter | payment_failure_rate_pct | refund_rate_pct | tickets_per_100_orders | avg_csat_score
--------+--------------------------+-----------------+------------------------+---------------
2024-Q1 |                     6.98 |            12.8 |                   8.87 |           3.42
2024-Q2 |                     7.25 |           10.71 |                   9.98 |           3.45
2024-Q3 |                     7.24 |            8.78 |                   9.22 |           3.64
2024-Q4 |                     7.23 |           11.64 |                  12.72 |           3.22
2025-Q1 |                      6.5 |           10.34 |                   7.62 |           3.16
2025-Q2 |                      6.6 |           10.31 |                  11.01 |           3.45
2025-Q3 |                     7.29 |           12.65 |                   9.65 |           3.46
2025-Q4 |                     6.74 |           13.68 |                  13.47 |           3.12
(8 rows)

What came of it
The memo and the queries that followed; links found by title, so inferred
It builds on:
- q005Merchant and order growth by quarter

It becomes benchmark query q012

Changed only where the benchmark needs the same result on every system

numericsdocs/NUMERICS.md: rounding removed; results keep full precision
numericsdocs/NUMERICS.md: AVG over an integer computed as a decimal: AVG(CAST(x AS NUMERIC(18,6)))
numericsdocs/NUMERICS.md: x / exchange rate cast to NUMERIC(18,6) (2×)
made explicita selected t.col gets AS col, its standard name made explicit (12×)
determinismthe standard leaves the place of NULLs in a sort to the implementation: NULLS LAST ascending, NULLS FIRST descending, as in the reference results (2×)

The benchmark text, against the submitted SQL

SELECT  q.year_num || '-Q' || q.quarter_num AS quarter,  ROUND(pa.failure_rate, 2) AS payment_failure_rate_pct,  ROUND(CASE WHEN pm.captured_eur > 0 THEN rf.refund_eur / pm.captured_eur * 100 ELSE NULL END, 2) AS refund_rate_pct,  ROUND(CASE WHEN ord.order_count > 0 THEN tk.ticket_count * 100.0 / ord.order_count ELSE NULL END, 2) AS tickets_per_100_orders,  ROUND(cs.avg_csat, 2) AS avg_csat_score  pa.failure_rate AS payment_failure_rate_pct,  CASE WHEN pm.captured_eur > 0 THEN rf.refund_eur / pm.captured_eur * 100 ELSE NULL END AS refund_rate_pct,  CASE WHEN ord.order_count > 0 THEN tk.ticket_count * 100.0 / ord.order_count ELSE NULL END AS tickets_per_100_orders,  cs.avg_csat AS avg_csat_scoreFROM (  SELECT DISTINCT year_num, quarter_num  FROM calendar_days  WHERE calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31') qLEFT JOIN (  SELECT cd.year_num, cd.quarter_num,  SELECT cd.year_num AS year_num, cd.quarter_num AS quarter_num,    SUM(CASE WHEN pa.status = 'failed' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS failure_rate  FROM payment_attempts pa  JOIN calendar_days cd ON cd.calendar_date = CAST(pa.attempted_at AS DATE)  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'  GROUP BY cd.year_num, cd.quarter_num) pa ON pa.year_num = q.year_num AND pa.quarter_num = q.quarter_numLEFT JOIN (  SELECT cd.year_num, cd.quarter_num,    SUM(pr.amount / er.rate) AS refund_eur  SELECT cd.year_num AS year_num, cd.quarter_num AS quarter_num,    SUM(CAST(pr.amount / er.rate AS NUMERIC(18,6))) AS refund_eur  FROM payment_refunds pr  JOIN calendar_days cd ON cd.calendar_date = CAST(pr.processed_at AS DATE)  JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = cd.calendar_date  WHERE pr.status = 'succeeded'    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'  GROUP BY cd.year_num, cd.quarter_num) rf ON rf.year_num = q.year_num AND rf.quarter_num = q.quarter_numLEFT JOIN (  SELECT cd.year_num, cd.quarter_num,    SUM(p.amount / er.rate) AS captured_eur  SELECT cd.year_num AS year_num, cd.quarter_num AS quarter_num,    SUM(CAST(p.amount / er.rate AS NUMERIC(18,6))) AS captured_eur  FROM payments p  JOIN calendar_days cd ON cd.calendar_date = CAST(p.captured_at AS DATE)  JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = cd.calendar_date  WHERE p.status IN ('captured', 'partially_refunded', 'refunded')    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'  GROUP BY cd.year_num, cd.quarter_num) pm ON pm.year_num = q.year_num AND pm.quarter_num = q.quarter_numLEFT JOIN (  SELECT cd.year_num, cd.quarter_num, COUNT(*) AS ticket_count  SELECT cd.year_num AS year_num, cd.quarter_num AS quarter_num, COUNT(*) AS ticket_count  FROM tickets t  JOIN calendar_days cd ON cd.calendar_date = CAST(t.created_at AS DATE)  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'  GROUP BY cd.year_num, cd.quarter_num) tk ON tk.year_num = q.year_num AND tk.quarter_num = q.quarter_numLEFT JOIN (  SELECT cd.year_num, cd.quarter_num, COUNT(*) AS order_count  SELECT cd.year_num AS year_num, cd.quarter_num AS quarter_num, COUNT(*) AS order_count  FROM orders o  JOIN calendar_days cd ON cd.calendar_date = CAST(o.placed_at AS DATE)  WHERE o.status IN ('paid', 'partially_fulfilled', 'fulfilled', 'refunded')    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'  GROUP BY cd.year_num, cd.quarter_num) ord ON ord.year_num = q.year_num AND ord.quarter_num = q.quarter_numLEFT JOIN (  SELECT cd.year_num, cd.quarter_num, AVG(c.score) AS avg_csat  SELECT cd.year_num AS year_num, cd.quarter_num AS quarter_num, AVG(CAST(c.score AS NUMERIC(18,6))) AS avg_csat  FROM csat_responses c  JOIN calendar_days cd ON cd.calendar_date = CAST(c.submitted_at AS DATE)  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'  GROUP BY cd.year_num, cd.quarter_num) cs ON cs.year_num = q.year_num AND cs.quarter_num = q.quarter_numORDER BY q.year_num ASC, q.quarter_num ASCORDER BY q.year_num ASC NULLS LAST, q.quarter_num ASC NULLS LAST

As each system received it; highlighted, the parts spelled for that system

SELECT
  q.year_num || '-Q' || q.quarter_num AS quarter,
  pa.failure_rate AS payment_failure_rate_pct,
  CASE WHEN pm.captured_eur > 0 THEN rf.refund_eur / pm.captured_eur * 100 ELSE NULL END AS refund_rate_pct,
  CASE WHEN ord.order_count > 0 THEN tk.ticket_count * 100.0 / ord.order_count ELSE NULL END AS tickets_per_100_orders,
  cs.avg_csat AS avg_csat_score
FROM (
  SELECT DISTINCT year_num, quarter_num
  FROM calendar_days
  WHERE calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
) q
LEFT JOIN (
  SELECT cd.year_num AS year_num, cd.quarter_num AS quarter_num,
    SUM(CASE WHEN pa.status = 'failed' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS failure_rate
  FROM payment_attempts pa
  JOIN calendar_days cd ON cd.calendar_date = CAST(pa.attempted_at AS DATE)
  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) pa ON pa.year_num = q.year_num AND pa.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num AS year_num, cd.quarter_num AS quarter_num,
    SUM(CAST(pr.amount / er.rate AS NUMERIC(18,6))) AS refund_eur
  FROM payment_refunds pr
  JOIN calendar_days cd ON cd.calendar_date = CAST(pr.processed_at AS DATE)
  JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = cd.calendar_date
  WHERE pr.status = 'succeeded'
    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) rf ON rf.year_num = q.year_num AND rf.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num AS year_num, cd.quarter_num AS quarter_num,
    SUM(CAST(p.amount / er.rate AS NUMERIC(18,6))) AS captured_eur
  FROM payments p
  JOIN calendar_days cd ON cd.calendar_date = CAST(p.captured_at AS DATE)
  JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = cd.calendar_date
  WHERE p.status IN ('captured', 'partially_refunded', 'refunded')
    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) pm ON pm.year_num = q.year_num AND pm.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num AS year_num, cd.quarter_num AS quarter_num, COUNT(*) AS ticket_count
  FROM tickets t
  JOIN calendar_days cd ON cd.calendar_date = CAST(t.created_at AS DATE)
  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) tk ON tk.year_num = q.year_num AND tk.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num AS year_num, cd.quarter_num AS quarter_num, COUNT(*) AS order_count
  FROM orders o
  JOIN calendar_days cd ON cd.calendar_date = CAST(o.placed_at AS DATE)
  WHERE o.status IN ('paid', 'partially_fulfilled', 'fulfilled', 'refunded')
    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) ord ON ord.year_num = q.year_num AND ord.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num AS year_num, cd.quarter_num AS quarter_num, AVG(CAST(c.score AS NUMERIC(18,6))) AS avg_csat
  FROM csat_responses c
  JOIN calendar_days cd ON cd.calendar_date = CAST(c.submitted_at AS DATE)
  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) cs ON cs.year_num = q.year_num AND cs.quarter_num = q.quarter_num
ORDER BY q.year_num ASC NULLS LAST, q.quarter_num ASC NULLS LAST

Firebolt{ "errors": [ { "description": "Line 22, Column 24: Operations between decimals with different precision and scale is not supported at the moment. Explicitly cast one of the decimals to other decimal precision and scale." } ], "query": { "query_id": "c17c99fc-8556-4e73-864e-fbcfb7f47f11", "query_lab
ClickHouseCode: 69. DB::Exception: Decimal result's scale is less than argument's one: while executing function divide on arguments __table7.amount Decimal(14, 4) Decimal64(size = 0), __table9.rate Decimal(12, 6) Decimal64(size = 0). (ARGUMENT_OUT_OF_BOUND)

Scale factor 1000, c8a.24xlarge, after the last load. q012 on the start page, with every configuration.

Everything, as recorded

The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted

Felix's whole session, 7 turns

The request, as Felix received it

Report request from Hannah Brooks (analyst for the Chief Executive Officer):

Title: Operational warning signs trend: refunds, payment failures, support volume
Question: Are payment, refund, or support problems trending up over the last two years in a way that could become a board-level concern?
Grain: one row per calendar quarter
Columns:
- quarter
- payment failure rate (%, failed payment_attempts / all payment_attempts)
- refund rate (%, succeeded payment_refunds amount EUR / captured payments amount EUR)
- tickets per 100 orders
- average CSAT score (1-5)
Details: Source: payment_attempts (attempted_at in quarter; failure rate = count where status='failed' / count of all attempts), payment_refunds (processed_at in quarter, status='succeeded', amount converted to EUR via exchange_rates as_of processed_at date) divided by payments (captured_at in quarter, status='captured' or 'partially_refunded' or 'refunded', amount converted to EUR via exchange_rates as_of captured_at date), tickets (created_at in quarter, count), orders (placed_at in quarter, status in ('paid','partially_fulfilled','fulfilled','refunded'), count) for the tickets-per-100-orders ratio, and csat_responses (submitted_at in quarter, average score). Window: 2024-Q1 through 2025-Q4 (8 quarters), assigned via calendar_days. Order by quarter ascending.

Check your query with the warehouse's dry_run tool, then hand it in with submit.

Felix calls dry_run

SELECT
  q.year_num || '-Q' || q.quarter_num AS quarter,
  ROUND(pa.failure_rate, 2) AS payment_failure_rate_pct,
  ROUND(CASE WHEN pm.captured_eur > 0 THEN rf.refund_eur / pm.captured_eur * 100 ELSE NULL END, 2) AS refund_rate_pct,
  ROUND(CASE WHEN ord.order_count > 0 THEN tk.ticket_count * 100.0 / ord.order_count ELSE NULL END, 2) AS tickets_per_100_orders,
  ROUND(cs.avg_csat, 2) AS avg_csat_score
FROM (
  SELECT DISTINCT year_num, quarter_num
  FROM calendar_days
  WHERE calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
) q
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num,
    SUM(CASE WHEN pa.status = 'failed' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS failure_rate
  FROM payment_attempts pa
  JOIN calendar_days cd ON cd.calendar_date = CAST(pa.attempted_at AS DATE)
  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) pa ON pa.year_num = q.year_num AND pa.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num,
    SUM(pr.amount / er.rate) AS refund_eur
  FROM payment_refunds pr
  JOIN calendar_days cd ON cd.calendar_date = CAST(pr.processed_at AS DATE)
  JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = cd.calendar_date
  WHERE pr.status = 'succeeded'
    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) rf ON rf.year_num = q.year_num AND rf.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num,
    SUM(p.amount / er.rate) AS captured_eur
  FROM payments p
  JOIN calendar_days cd ON cd.calendar_date = CAST(p.captured_at AS DATE)
  JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = cd.calendar_date
  WHERE p.status IN ('captured', 'partially_refunded', 'refunded')
    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) pm ON pm.year_num = q.year_num AND pm.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num, COUNT(*) AS ticket_count
  FROM tickets t
  JOIN calendar_days cd ON cd.calendar_date = CAST(t.created_at AS DATE)
  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) tk ON tk.year_num = q.year_num AND tk.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num, COUNT(*) AS order_count
  FROM orders o
  JOIN calendar_days cd ON cd.calendar_date = CAST(o.placed_at AS DATE)
  WHERE o.status IN ('paid', 'partially_fulfilled', 'fulfilled', 'refunded')
    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) ord ON ord.year_num = q.year_num AND ord.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num, AVG(c.score) AS avg_csat
  FROM csat_responses c
  JOIN calendar_days cd ON cd.calendar_date = CAST(c.submitted_at AS DATE)
  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) cs ON cs.year_num = q.year_num AND cs.quarter_num = q.quarter_num
ORDER BY q.year_num ASC, q.quarter_num ASC

The warehouse answers

STATUS: COMPILES (dry run on the empty schema, no data read)
COLUMNS: quarter, payment_failure_rate_pct, refund_rate_pct, tickets_per_100_orders, avg_csat_score

FelixThe query compiles and returns the expected 5 columns at one row per quarter. This aggregates platform-wide across all tenants/currencies, converting multi-currency amounts to EUR for the refund rate. Submitting now.

Felix calls submit

SELECT
  q.year_num || '-Q' || q.quarter_num AS quarter,
  ROUND(pa.failure_rate, 2) AS payment_failure_rate_pct,
  ROUND(CASE WHEN pm.captured_eur > 0 THEN rf.refund_eur / pm.captured_eur * 100 ELSE NULL END, 2) AS refund_rate_pct,
  ROUND(CASE WHEN ord.order_count > 0 THEN tk.ticket_count * 100.0 / ord.order_count ELSE NULL END, 2) AS tickets_per_100_orders,
  ROUND(cs.avg_csat, 2) AS avg_csat_score
FROM (
  SELECT DISTINCT year_num, quarter_num
  FROM calendar_days
  WHERE calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
) q
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num,
    SUM(CASE WHEN pa.status = 'failed' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS failure_rate
  FROM payment_attempts pa
  JOIN calendar_days cd ON cd.calendar_date = CAST(pa.attempted_at AS DATE)
  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) pa ON pa.year_num = q.year_num AND pa.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num,
    SUM(pr.amount / er.rate) AS refund_eur
  FROM payment_refunds pr
  JOIN calendar_days cd ON cd.calendar_date = CAST(pr.processed_at AS DATE)
  JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = cd.calendar_date
  WHERE pr.status = 'succeeded'
    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) rf ON rf.year_num = q.year_num AND rf.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num,
    SUM(p.amount / er.rate) AS captured_eur
  FROM payments p
  JOIN calendar_days cd ON cd.calendar_date = CAST(p.captured_at AS DATE)
  JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = cd.calendar_date
  WHERE p.status IN ('captured', 'partially_refunded', 'refunded')
    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) pm ON pm.year_num = q.year_num AND pm.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num, COUNT(*) AS ticket_count
  FROM tickets t
  JOIN calendar_days cd ON cd.calendar_date = CAST(t.created_at AS DATE)
  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) tk ON tk.year_num = q.year_num AND tk.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num, COUNT(*) AS order_count
  FROM orders o
  JOIN calendar_days cd ON cd.calendar_date = CAST(o.placed_at AS DATE)
  WHERE o.status IN ('paid', 'partially_fulfilled', 'fulfilled', 'refunded')
    AND cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) ord ON ord.year_num = q.year_num AND ord.quarter_num = q.quarter_num
LEFT JOIN (
  SELECT cd.year_num, cd.quarter_num, AVG(c.score) AS avg_csat
  FROM csat_responses c
  JOIN calendar_days cd ON cd.calendar_date = CAST(c.submitted_at AS DATE)
  WHERE cd.calendar_date BETWEEN DATE '2024-01-01' AND DATE '2025-12-31'
  GROUP BY cd.year_num, cd.quarter_num
) cs ON cs.year_num = q.year_num AND cs.quarter_num = q.quarter_num
ORDER BY q.year_num ASC, q.quarter_num ASC

The warehouse answers

Submitted to tonight's batch. Your work on this request is done.

FelixSubmitted. The report covers 2024-Q1 through 2025-Q4 with one row per quarter, showing payment failure rate, EUR-normalized refund rate, tickets per 100 orders, and average CSAT score, ordered chronologically so the CEO's analyst can spot any upward trend in operational risk.

Not shown here: 240 KB of context the agent was given, its instructions and the schema documentation; it is in the raw transcript.

Felix's transcript (275 KB) · Hannah Brooks's transcript (872 KB) · Maren Okafor's transcript (278 KB) · the night's plan (5 KB) · the night's reports (4 KB) · the week's memo (3 KB)

Operational warning signs trend: refunds, payment failures, support volume

Maren Okafor writes to Hannah Brooks

Hannah Brooks plans the night

Felix writes the SQL

The report comes back overnight

What came of it

It becomes benchmark query q012

Everything, as recorded