Queries · q228 · Chief Financial Officer · week 2, night 3

No-Ledger-Entries Gap Trend by Month, 2025

“Is the small 'payment with zero ledger entries' gap an ongoing issue in current operations, or was it confined to earlier in the year and since resolved?”

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

1. A business event lands on the Chief Financial Officer's desk

   Week 2

   The external auditors start the 2025 year-end audit in two weeks. They will want to see that the money adds up across the whole chain: what shoppers paid, what was refunded or credited, what merchants received in payouts, what Tidewick kept, and that invoices agree with all of it. They will also trace sample transactions end to end.

   One of the 35 events we wrote for the role play; the manager heard of it at the start of week 2 and had to cover it in that week's note. How events arise · All the events

2. Tobias Lindqvist writes to Kofi Mensah

   The manager, at the start of week 2

   Subject: Good progress — but the audit clock is now the priority

   Kofi,

   Thanks — this is useful, and I appreciate you flagging the caveats rather than smoothing them over. A few reactions and then where I want to focus next week.

   What stood out:

   • The correction on the revenue report (off by roughly half, now fixed and cross-checked) is reassuring that you caught it, but it also makes me want a second pair of eyes before anything like this goes near the auditors.
   • The refund numbers disagreeing by 16–40% depending on which source we use is the kind of thing an auditor will ask about directly. I don't want to walk in with two different refund totals and no explanation.
   • Concentration easing as we add merchants is good context for the budget conversation — no urgent follow-up needed there this week.
   • I also saw the notes from pricing and risk this week — the free-delivery giveaway and the refund pattern on that welcome discount are small in isolation, but they're exactly the kind of "where did the money go" questions auditors and I will both ask, so I'd like a rough sense of scale rather than letting them sit as separate anecdotes.

   The big new driver: external audit starts in two weeks. They'll want to walk the full money trail — what a shopper paid, what was refunded or credited back, what the merchant was paid out, what we kept as our fee, and that it all agrees with the invoices and accounts — and they'll pick sample transactions and trace them start to finish. I'd rather we got ahead of that than react to it.

   Questions for this week, in priority order:

   1. Can we demonstrate, for a sample of transactions, that the money actually adds up end to end — shopper payment through refund/credit, payout to the merchant, and our retained fee — so we have something ready to hand the auditors rather than assembling it under time pressure?
   2. Which refund figure should I trust for the 2025 accounts, and why do the two methods disagree? I don't need the full reconciliation solved, but I need to understand the likely cause well enough to explain it credibly if asked.
   3. Where do things stand on getting our own plan-fee revenue into the picture, even approximately, so I'm not presenting half of 2025 revenue as the whole story?
   4. If there's room: roughly how much revenue or margin might be leaking through the control gaps your peers flagged this week (free deliveries to canceled customers, uncapped coupon reuse) — just enough for me to judge whether it's noise or something that belongs in front of the auditors too.

   Audit readiness (#1) is the one I most need — please make that the lead request even if it means FX and B2B receivables slip another week.

   Thanks, Tobias

   Show the whole note

3. Kofi Mensah plans the night

   The analyst · week 2, night 3, request 3 of 3

   Report 1 (ledger detail) actually resolves last night's big worry in Tidewick's favor: the "credit = payment + refund" pattern is correct double-entry accounting, not double-counted revenue. On capture: Sales revenue (credit) + VAT payable (credit) = Payment gateway clearing (debit). On refund: Refunds and returns/contra-revenue (debit) + VAT payable (debit) = Payment gateway clearing (credit) — e.g. Ampfeld Elektronik #1727 and every other refunded sample shows this exact structure, balanced to the cent. My "ledger_credit_total" diagnostic from Night 2 was simply summing credits across unrelated accounts (revenue credits at capture + clearing credits at refund), which is apples-to-oranges — the ledger itself is fine. Report 2's scale-up confirms this: 78.55% + 18.74% ≈ 97.3% of 2025's captured payments follow one of these two clean, explainable patterns. That's good news I can tell Tobias with confidence.

   Two smaller things still need a next step before I call #1 fully done: (a) "other mismatch" is 249 payments / €84,443 in payment value (2.1% of count) — Castell Fasteners' SO-100233 in the detail report hints these may be non-gateway payment methods (bank transfer) where VAT/revenue nets differently than my simple gross/net comparison expected, not a real error, but I want to confirm that before saying so; (b) "no ledger entries" is small in money (€3,380 across 67 payments, 0.58%) but is a genuine audit-trail gap spread across 31 merchants — worth knowing whether it's an old, fixed bug or still happening now, since "we found gaps but can't tell if they're ongoing" is a weak answer to give auditors.

   Tonight's three slots: characterize the "other mismatch" bucket by payment method (test the bank-transfer/B2B hypothesis); redo the revenue reconciliation using the correct ledger accounts (Sales/Shipping revenue credits minus Refunds-and-returns debits, compared to payments' net amount) so I have a trustworthy "yes, it ties out" statement built the right way instead of my flawed Night 2 shortcut; and trend the no-ledger-entries gap by month to see if it's historical or still occurring.

   Read the whole plan
   No-Ledger-Entries Gap Trend by Month, 2025

   Question

   Is the small 'payment with zero ledger entries' gap an ongoing issue in current operations, or was it confined to earlier in the year and since resolved?

   One row per

   one calendar month, 2025

   Columns

   • month
   • total_payments
   • payments_without_ledger_entries
   • pct_without_ledger_entries
   • amount_at_risk_eur

4. Grace writes the SQL

   The engineer · 2 dry runs on the empty schema, then the submission

   Dry run 1 · STATUS: REJECTED

   WITH payments_2025 AS (
     SELECT
       p.id,
       p.captured_at,
       p.amount,
       p.currency_code
     FROM payments p
     WHERE p.status IN ('captured', 'refunded', 'partially_refunded')
       AND p.captured_at >= DATE '2025-01-01'
       AND p.captured_at < DATE '2026-01-01'
   ),
   ledger_counts AS (
     SELECT
       le.payment_id,
       COUNT(*) AS entry_count
     FROM ledger_entries le
     WHERE le.payment_id IS NOT NULL
     GROUP BY le.payment_id
   ),
   payments_with_fx AS (
     SELECT
       p.id,
       p.captured_at,
       p.amount / er.rate AS amount_eur,
       COALESCE(lc.entry_count, 0) AS entry_count
     FROM payments_2025 p
     LEFT JOIN ledger_counts lc ON lc.payment_id = p.id
     LEFT JOIN exchange_rates er
       ON er.base_code = 'EUR'
      AND er.quote_code = p.currency_code
      AND er.as_of = DATE(p.captured_at)
   ),
   monthly AS (
     SELECT
       DATE_TRUNC('month', captured_at)::date AS month_start,
       COUNT(*) AS total_payments,
       SUM(CASE WHEN entry_count = 0 THEN 1 ELSE 0 END) AS payments_without_ledger_entries,
       SUM(CASE WHEN entry_count = 0 THEN amount_eur ELSE 0 END) AS amount_at_risk_eur
     FROM payments_with_fx
     GROUP BY 1
   )
   SELECT
     TO_CHAR(month_start, 'YYYY-MM') AS month,
     total_payments,
     payments_without_ledger_entries,
     ROUND(100.0 * payments_without_ledger_entries / total_payments, 2) AS pct_without_ledger_entries,
     ROUND(amount_at_risk_eur, 2) AS amount_at_risk_eur
   FROM monthly
   ORDER BY month_start

   Dry run 2 · 4 changes from the dry run before · STATUS: COMPILES (dry run on the empty schema, no data read)

   WITH payments_2025 AS (  SELECT    p.id,    p.captured_at,    p.amount,    p.currency_code  FROM payments p  WHERE p.status IN ('captured', 'refunded', 'partially_refunded')    AND p.captured_at >= DATE '2025-01-01'    AND p.captured_at < DATE '2026-01-01'),ledger_counts AS (  SELECT    le.payment_id,    COUNT(*) AS entry_count  FROM ledger_entries le  WHERE le.payment_id IS NOT NULL  GROUP BY le.payment_id),payments_with_fx AS (  SELECT    p.id,    p.captured_at,    p.amount / er.rate AS amount_eur,    COALESCE(lc.entry_count, 0) AS entry_count  FROM payments_2025 p  LEFT JOIN ledger_counts lc ON lc.payment_id = p.id  LEFT JOIN exchange_rates er    ON er.base_code = 'EUR'   AND er.quote_code = p.currency_code   AND er.as_of = DATE(p.captured_at)),payments_with_month AS (  SELECT    cd.month_label,    cd.month_start,    pf.amount_eur,    pf.entry_count  FROM payments_with_fx pf  JOIN calendar_days cd ON cd.calendar_date = DATE(pf.captured_at)),monthly AS (  SELECT    DATE_TRUNC('month', captured_at)::date AS month_start,    month_label,    month_start,    COUNT(*) AS total_payments,    SUM(CASE WHEN entry_count = 0 THEN 1 ELSE 0 END) AS payments_without_ledger_entries,    SUM(CASE WHEN entry_count = 0 THEN amount_eur ELSE 0 END) AS amount_at_risk_eur  FROM payments_with_fx  GROUP BY 1  FROM payments_with_month  GROUP BY month_label, month_start)SELECT  TO_CHAR(month_start, 'YYYY-MM') AS month,  month_label AS month,  total_payments,  payments_without_ledger_entries,  ROUND(100.0 * payments_without_ledger_entries / total_payments, 2) AS pct_without_ledger_entries,  ROUND(amount_at_risk_eur, 2) AS amount_at_risk_eurFROM monthlyORDER BY month_start

   “The query compiles and returns the expected columns. Submitting.”

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

   “Submitted. The report groups 2025 captured/refunded/partially_refunded payments by calendar month, left-joins a per-payment ledger-entry count, and flags payments with zero linked ledger entries. Amounts are converted to EUR using the day's EUR→payment-currency exchange rate before summing the at-risk total, giving Kofi a clean month-by-month trend to see whether the gap is ongoing or resolved.”

5. The report comes back overnight

   As Kofi Mensah saw it, from the small dataset the agents worked on

   month   | total_payments | payments_without_ledger_entries | pct_without_ledger_entries | amount_at_risk_eur
   --------+----------------+---------------------------------+----------------------------+-------------------
   2025-01 |           1749 |                              38 |                       2.17 |             1241.0
   2025-02 |           1324 |                              17 |                       1.28 |             606.08
   2025-03 |           1545 |                               6 |                       0.39 |             129.83
   2025-04 |           1487 |                               7 |                       0.47 |             323.67
   2025-05 |           1595 |                               7 |                       0.44 |             290.08
   2025-06 |           1435 |                               3 |                       0.21 |              97.52
   2025-07 |           1554 |                               2 |                       0.13 |             149.21
   2025-08 |           1455 |                               1 |                       0.07 |              52.47
   2025-09 |           1607 |                               4 |                       0.25 |              72.45
   2025-10 |           1766 |                               1 |                       0.06 |                0.0
   2025-11 |           2525 |                               3 |                       0.12 |              119.8
   2025-12 |           3011 |                              11 |                       0.37 |              298.5
   (12 rows)

6. What came of it

   The memo and the queries that followed; links found by title, so inferred

   It builds on:

   • q225Payments With No Ledger Entries, by Merchant, 2025

7. It becomes benchmark query q228

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

   • spellingdate cast (2×)
   • determinismtie-breaker: month_label is not a function of month_start by schema
   • numericsdocs/NUMERICS.md: rounding removed; results keep full precision
   • numericsdocs/NUMERICS.md: x / exchange rate cast to NUMERIC(18,6)
   • made explicita selected t.col gets AS col, its standard name made explicit (11×)
   • 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

   WITH payments_2025 AS (  SELECT    p.id,    p.captured_at,    p.amount,    p.currency_code    p.id AS id,    p.captured_at AS captured_at,    p.amount AS amount,    p.currency_code AS currency_code  FROM payments p  WHERE p.status IN ('captured', 'refunded', 'partially_refunded')    AND p.captured_at >= DATE '2025-01-01'    AND p.captured_at < DATE '2026-01-01'),ledger_counts AS (  SELECT    le.payment_id,    le.payment_id AS payment_id,    COUNT(*) AS entry_count  FROM ledger_entries le  WHERE le.payment_id IS NOT NULL  GROUP BY le.payment_id),payments_with_fx AS (  SELECT    p.id,    p.captured_at,    p.amount / er.rate AS amount_eur,    p.id AS id,    p.captured_at AS captured_at,    CAST(p.amount / er.rate AS NUMERIC(18,6)) AS amount_eur,    COALESCE(lc.entry_count, 0) AS entry_count  FROM payments_2025 p  LEFT JOIN ledger_counts lc ON lc.payment_id = p.id  LEFT JOIN exchange_rates er    ON er.base_code = 'EUR'   AND er.quote_code = p.currency_code   AND er.as_of = DATE(p.captured_at)   AND er.as_of = CAST(p.captured_at AS DATE)),payments_with_month AS (  SELECT    cd.month_label,    cd.month_start,    pf.amount_eur,    pf.entry_count    cd.month_label AS month_label,    cd.month_start AS month_start,    pf.amount_eur AS amount_eur,    pf.entry_count AS entry_count  FROM payments_with_fx pf  JOIN calendar_days cd ON cd.calendar_date = DATE(pf.captured_at)  JOIN calendar_days cd ON cd.calendar_date = CAST(pf.captured_at AS DATE)),monthly AS (  SELECT    month_label,    month_start,    COUNT(*) AS total_payments,    SUM(CASE WHEN entry_count = 0 THEN 1 ELSE 0 END) AS payments_without_ledger_entries,    SUM(CASE WHEN entry_count = 0 THEN amount_eur ELSE 0 END) AS amount_at_risk_eur  FROM payments_with_month  GROUP BY month_label, month_start)SELECT  month_label AS month,  total_payments,  payments_without_ledger_entries,  ROUND(100.0 * payments_without_ledger_entries / total_payments, 2) AS pct_without_ledger_entries,  ROUND(amount_at_risk_eur, 2) AS amount_at_risk_eur  100.0 * payments_without_ledger_entries / total_payments AS pct_without_ledger_entries,  amount_at_risk_eur AS amount_at_risk_eurFROM monthlyORDER BY month_startORDER BY month_start NULLS LAST, month_label NULLS LAST

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

   DuckDB · Hyper · GizmoSQL · CedarDB · pg_duckdb · PostgreSQL · DataFusion · Firebolt · ClickHouse · chDB · clickhouse_brew

   WITH payments_2025 AS (
     SELECT
       p.id AS id,
       p.captured_at AS captured_at,
       p.amount AS amount,
       p.currency_code AS currency_code
     FROM payments p
     WHERE p.status IN ('captured', 'refunded', 'partially_refunded')
       AND p.captured_at >= DATE '2025-01-01'
       AND p.captured_at < DATE '2026-01-01'
   ),
   ledger_counts AS (
     SELECT
       le.payment_id AS payment_id,
       COUNT(*) AS entry_count
     FROM ledger_entries le
     WHERE le.payment_id IS NOT NULL
     GROUP BY le.payment_id
   ),
   payments_with_fx AS (
     SELECT
       p.id AS id,
       p.captured_at AS captured_at,
       CAST(p.amount / er.rate AS NUMERIC(18,6)) AS amount_eur,
       COALESCE(lc.entry_count, 0) AS entry_count
     FROM payments_2025 p
     LEFT JOIN ledger_counts lc ON lc.payment_id = p.id
     LEFT JOIN exchange_rates er
       ON er.base_code = 'EUR'
      AND er.quote_code = p.currency_code
      AND er.as_of = CAST(p.captured_at AS DATE)
   ),
   payments_with_month AS (
     SELECT
       cd.month_label AS month_label,
       cd.month_start AS month_start,
       pf.amount_eur AS amount_eur,
       pf.entry_count AS entry_count
     FROM payments_with_fx pf
     JOIN calendar_days cd ON cd.calendar_date = CAST(pf.captured_at AS DATE)
   ),
   monthly AS (
     SELECT
       month_label,
       month_start,
       COUNT(*) AS total_payments,
       SUM(CASE WHEN entry_count = 0 THEN 1 ELSE 0 END) AS payments_without_ledger_entries,
       SUM(CASE WHEN entry_count = 0 THEN amount_eur ELSE 0 END) AS amount_at_risk_eur
     FROM payments_with_month
     GROUP BY month_label, month_start
   )
   SELECT
     month_label AS month,
     total_payments,
     payments_without_ledger_entries,
     100.0 * payments_without_ledger_entries / total_payments AS pct_without_ledger_entries,
     amount_at_risk_eur AS amount_at_risk_eur
   FROM monthly
   ORDER BY month_start NULLS LAST, month_label NULLS LAST

   • Firebolt{ "errors": [ { "description": "Line 24, Column 19: 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": "f8273355-4dc6-4a62-8d7a-0298a21fcfb7", "query_lab
   • ClickHouseCode: 69. DB::Exception: Decimal result's scale is less than argument's one: while executing function divide on arguments __table4.amount Decimal(14, 4) Decimal64(size = 0), __table8.rate Nullable(Decimal(12, 6)) Nullable(size = 0, Decimal64(size = 0), UInt8(size = 0)). (ARGUMENT_OUT_OF_BOUND)

   Scale factor 1000, c8a.24xlarge, after the last load. q228 on the start page, with every configuration.

8. 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

   Grace's whole session, 9 turns

   • The request, as Grace received it

     Report request from Kofi Mensah (analyst for the Chief Financial Officer):
     
     Title: No-Ledger-Entries Gap Trend by Month, 2025
     Question: Is the small 'payment with zero ledger entries' gap an ongoing issue in current operations, or was it confined to earlier in the year and since resolved?
     Grain: one calendar month, 2025
     Columns:
     - month
     - total_payments
     - payments_without_ledger_entries
     - pct_without_ledger_entries
     - amount_at_risk_eur
     Details: Source: payments with status captured/refunded/partially_refunded and captured_at in 2025, LEFT JOIN to a per-payment count of linked ledger_entries via payment_id. Convert payment amounts to EUR via exchange_rates (base_code='EUR', quote_code=payments.currency_code, as_of=date(captured_at)). Group by calendar month of captured_at. amount_at_risk_eur sums payment_amount_eur only for payments with zero linked ledger_entries. Order chronologically 2025-01 to 2025-12 (12 rows).
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Grace calls dry_run

     WITH payments_2025 AS (
       SELECT
         p.id,
         p.captured_at,
         p.amount,
         p.currency_code
       FROM payments p
       WHERE p.status IN ('captured', 'refunded', 'partially_refunded')
         AND p.captured_at >= DATE '2025-01-01'
         AND p.captured_at < DATE '2026-01-01'
     ),
     ledger_counts AS (
       SELECT
         le.payment_id,
         COUNT(*) AS entry_count
       FROM ledger_entries le
       WHERE le.payment_id IS NOT NULL
       GROUP BY le.payment_id
     ),
     payments_with_fx AS (
       SELECT
         p.id,
         p.captured_at,
         p.amount / er.rate AS amount_eur,
         COALESCE(lc.entry_count, 0) AS entry_count
       FROM payments_2025 p
       LEFT JOIN ledger_counts lc ON lc.payment_id = p.id
       LEFT JOIN exchange_rates er
         ON er.base_code = 'EUR'
        AND er.quote_code = p.currency_code
        AND er.as_of = DATE(p.captured_at)
     ),
     monthly AS (
       SELECT
         DATE_TRUNC('month', captured_at)::date AS month_start,
         COUNT(*) AS total_payments,
         SUM(CASE WHEN entry_count = 0 THEN 1 ELSE 0 END) AS payments_without_ledger_entries,
         SUM(CASE WHEN entry_count = 0 THEN amount_eur ELSE 0 END) AS amount_at_risk_eur
       FROM payments_with_fx
       GROUP BY 1
     )
     SELECT
       TO_CHAR(month_start, 'YYYY-MM') AS month,
       total_payments,
       payments_without_ledger_entries,
       ROUND(100.0 * payments_without_ledger_entries / total_payments, 2) AS pct_without_ledger_entries,
       ROUND(amount_at_risk_eur, 2) AS amount_at_risk_eur
     FROM monthly
     ORDER BY month_start

   • The warehouse answers

     STATUS: REJECTED
       - does not compile: Catalog Error: Scalar Function with name to_char does not exist!

   • Grace calls dry_run

     WITH payments_2025 AS (
       SELECT
         p.id,
         p.captured_at,
         p.amount,
         p.currency_code
       FROM payments p
       WHERE p.status IN ('captured', 'refunded', 'partially_refunded')
         AND p.captured_at >= DATE '2025-01-01'
         AND p.captured_at < DATE '2026-01-01'
     ),
     ledger_counts AS (
       SELECT
         le.payment_id,
         COUNT(*) AS entry_count
       FROM ledger_entries le
       WHERE le.payment_id IS NOT NULL
       GROUP BY le.payment_id
     ),
     payments_with_fx AS (
       SELECT
         p.id,
         p.captured_at,
         p.amount / er.rate AS amount_eur,
         COALESCE(lc.entry_count, 0) AS entry_count
       FROM payments_2025 p
       LEFT JOIN ledger_counts lc ON lc.payment_id = p.id
       LEFT JOIN exchange_rates er
         ON er.base_code = 'EUR'
        AND er.quote_code = p.currency_code
        AND er.as_of = DATE(p.captured_at)
     ),
     payments_with_month AS (
       SELECT
         cd.month_label,
         cd.month_start,
         pf.amount_eur,
         pf.entry_count
       FROM payments_with_fx pf
       JOIN calendar_days cd ON cd.calendar_date = DATE(pf.captured_at)
     ),
     monthly AS (
       SELECT
         month_label,
         month_start,
         COUNT(*) AS total_payments,
         SUM(CASE WHEN entry_count = 0 THEN 1 ELSE 0 END) AS payments_without_ledger_entries,
         SUM(CASE WHEN entry_count = 0 THEN amount_eur ELSE 0 END) AS amount_at_risk_eur
       FROM payments_with_month
       GROUP BY month_label, month_start
     )
     SELECT
       month_label AS month,
       total_payments,
       payments_without_ledger_entries,
       ROUND(100.0 * payments_without_ledger_entries / total_payments, 2) AS pct_without_ledger_entries,
       ROUND(amount_at_risk_eur, 2) AS amount_at_risk_eur
     FROM monthly
     ORDER BY month_start

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: month, total_payments, payments_without_ledger_entries, pct_without_ledger_entries, amount_at_risk_eur

   • GraceThe query compiles and returns the expected columns. Submitting.
   • Grace calls submit

     WITH payments_2025 AS (
       SELECT
         p.id,
         p.captured_at,
         p.amount,
         p.currency_code
       FROM payments p
       WHERE p.status IN ('captured', 'refunded', 'partially_refunded')
         AND p.captured_at >= DATE '2025-01-01'
         AND p.captured_at < DATE '2026-01-01'
     ),
     ledger_counts AS (
       SELECT
         le.payment_id,
         COUNT(*) AS entry_count
       FROM ledger_entries le
       WHERE le.payment_id IS NOT NULL
       GROUP BY le.payment_id
     ),
     payments_with_fx AS (
       SELECT
         p.id,
         p.captured_at,
         p.amount / er.rate AS amount_eur,
         COALESCE(lc.entry_count, 0) AS entry_count
       FROM payments_2025 p
       LEFT JOIN ledger_counts lc ON lc.payment_id = p.id
       LEFT JOIN exchange_rates er
         ON er.base_code = 'EUR'
        AND er.quote_code = p.currency_code
        AND er.as_of = DATE(p.captured_at)
     ),
     payments_with_month AS (
       SELECT
         cd.month_label,
         cd.month_start,
         pf.amount_eur,
         pf.entry_count
       FROM payments_with_fx pf
       JOIN calendar_days cd ON cd.calendar_date = DATE(pf.captured_at)
     ),
     monthly AS (
       SELECT
         month_label,
         month_start,
         COUNT(*) AS total_payments,
         SUM(CASE WHEN entry_count = 0 THEN 1 ELSE 0 END) AS payments_without_ledger_entries,
         SUM(CASE WHEN entry_count = 0 THEN amount_eur ELSE 0 END) AS amount_at_risk_eur
       FROM payments_with_month
       GROUP BY month_label, month_start
     )
     SELECT
       month_label AS month,
       total_payments,
       payments_without_ledger_entries,
       ROUND(100.0 * payments_without_ledger_entries / total_payments, 2) AS pct_without_ledger_entries,
       ROUND(amount_at_risk_eur, 2) AS amount_at_risk_eur
     FROM monthly
     ORDER BY month_start

   • The warehouse answers

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

   • GraceSubmitted. The report groups 2025 captured/refunded/partially_refunded payments by calendar month, left-joins a per-payment ledger-entry count, and flags payments with zero linked ledger entries. Amounts are converted to EUR using the day's EUR→payment-currency exchange rate before summing the at-risk total, giving Kofi a clean month-by-month trend to see whether the gap is ongoing or resolved.

   Not shown here: 239 KB of context the agent was given, its instructions and the schema documentation; it is in the raw transcript.

   Grace's transcript (271 KB) · Kofi Mensah's transcript (906 KB) · Tobias Lindqvist's transcript (269 KB) · the night's plan (6 KB) · the night's reports (5 KB) · the week's memo (3 KB)