Queries · q109 · Chief Information Security Officer · week 1, night 4

Audit-trail coverage of actual soft-deletions, by entity table

“For tables where the app actually soft-deletes rows (deleted_at), does audit_log's 'delete' action consistently capture those deletions, or is there a real gap between what was deleted and what the audit trail shows?”

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

1. Henrik Aalto writes to Noah Fischer

   The manager, at the start of week 1

   Subject: Where I'd like your help first — account security snapshot

   Hi Noah,

   Good to have you working with me this week. I'm still building my mental map of the business, so let's start broad and sharpen later. Here's what's on my mind, roughly in priority order:

   1. How exposed are we on sign-in security today? I want to understand how merchant staff and shoppers are authenticating — are we seeing risky patterns like repeated failed logins, credential-stuffing-type behavior, or logins from unusual places? I need a feel for the baseline before I can say whether we have a problem.
   2. How strong is our multi-factor authentication posture? I'd like to know roughly how many of our merchants' staff (and ideally shoppers, if meaningful) actually have MFA set up and active, versus how many are relying on password-only access. This matters because it's one of the first things enterprise security reviewers ask about, and I need to know whether we can answer confidently or whether it's a gap we need to close.
   3. Who holds powerful access, and is it still appropriate? I'm interested in how permissions and roles are granted and whether there's good hygiene around it — e.g., are there dormant privileged accounts, or grants that nobody seems to be reviewing. This feeds directly into access-control questions in security audits.
   4. How healthy are our API keys and service accounts? These are often the weakest link — long-lived tokens that nobody rotates. I'd like a sense of how many are active, how old they are, and whether any look abandoned or over-privileged.
   5. Can we actually reconstruct "who did what, when" if asked? I want to understand how complete and reliable our audit trail is in practice — are sensitive actions consistently logged, and would we be able to produce a credible record if a merchant or regulator asked us to investigate an incident?

   What I need this for: I'm walking into enterprise deals where security questionnaires will ask us point-blank about authentication strength, access governance, credential hygiene, and auditability. I need an honest, evidence-based picture — good or bad — so I can brief the CEO and sales on what we can credibly claim today versus what needs fixing before the next review cycle.

   Given the slot budget, let's pick whichever 3 of these will give us the most defensible starting picture this week, and we can go deeper on the rest next week.

   Thanks, Henrik

   Show the whole note

2. Noah Fischer plans the night

   The analyst · week 1, night 4, request 1 of 3

   Night 3 brought the access-governance story to a clean, actionable finding: all 16 dormant Owner/Administrator accounts sit on CHURNED tenants only (9 tenants), with one as stale as 599 days since last login — meaning the real gap isn't "someone's not reviewing active-merchant access," it's that user accounts simply never get deactivated when a tenant churns (status still reads 'active'). That's a crisp, fixable finding for the memo. The API-key report came back almost too clean — zero active keys anywhere show as never-used or unused 90+ days, even though the same report shows oldest-active-key ages running into the thousands of days (one at 2,743 days, ~7.5 years, which predates Tidewick's 2023 warehouse cutover and even stretches toward the 2018 founding). That combination — heavily used, never rotated — is exactly the "long-lived token nobody rotates" risk Henrik named, so I want a platform-wide age/rotation distribution rather than relying on a handful of per-tenant data points. The audit-log report surfaced the most actionable gap of the week: 'delete' actions show only 27 events company-wide across three years, across just 16 of 120 tenants (13.3%) — implausibly low for an e-commerce platform this size, and worth testing directly against actual soft-deletions in the database rather than assuming it's a true absence of deletes.

   Tonight (final night) I'm spending all 3 slots closing out the two topics we hadn't quantified yet (API-key rotation hygiene, audit-trail completeness) plus checking whether existing compliance monitoring is a real control or just theoretical, since that's the credibility test for "can we reconstruct who did what" claims in the memo.

   Read the whole plan
   Audit-trail coverage of actual soft-deletions, by entity table

   Question

   For tables where the app actually soft-deletes rows (deleted_at), does audit_log's 'delete' action consistently capture those deletions, or is there a real gap between what was deleted and what the audit trail shows?

   One row per

   entity table, for a fixed set of 6 tables known to support soft-deletion: products, users, discounts, coupons, campaigns, promotions

   Columns

   • entity table
   • total soft-deleted rows (deleted_at is not null, all-time, 2023-01-01 through 2025-12-31)
   • matching audit_log rows (action = 'delete' and entity_table = this table, all-time)
   • coverage rate (matching audit_log rows / soft-deleted rows, %)
   • distinct tenants with at least one soft-deleted row here
   • earliest deleted_at
   • most recent deleted_at

3. 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 entity_table,
    total_soft_deleted,
    matching_audit_rows,
    CASE WHEN total_soft_deleted = 0 THEN NULL
         ELSE ROUND(LEAST(matching_audit_rows::numeric / total_soft_deleted * 100, 100), 1)
    END AS coverage_rate_pct,
    distinct_tenants,
    earliest_deleted_at,
    most_recent_deleted_at
   FROM (
     SELECT 'products' AS entity_table,
       (SELECT count(*) FROM products WHERE deleted_at IS NOT NULL) AS total_soft_deleted,
       (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'products') AS matching_audit_rows,
       (SELECT count(DISTINCT tenant_id) FROM products WHERE deleted_at IS NOT NULL) AS distinct_tenants,
       (SELECT min(deleted_at) FROM products WHERE deleted_at IS NOT NULL) AS earliest_deleted_at,
       (SELECT max(deleted_at) FROM products WHERE deleted_at IS NOT NULL) AS most_recent_deleted_at
     UNION ALL
     SELECT 'users',
       (SELECT count(*) FROM users WHERE deleted_at IS NOT NULL),
       (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'users'),
       (SELECT count(DISTINCT tenant_id) FROM users WHERE deleted_at IS NOT NULL),
       (SELECT min(deleted_at) FROM users WHERE deleted_at IS NOT NULL),
       (SELECT max(deleted_at) FROM users WHERE deleted_at IS NOT NULL)
     UNION ALL
     SELECT 'discounts',
       (SELECT count(*) FROM discounts WHERE deleted_at IS NOT NULL),
       (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'discounts'),
       (SELECT count(DISTINCT tenant_id) FROM discounts WHERE deleted_at IS NOT NULL),
       (SELECT min(deleted_at) FROM discounts WHERE deleted_at IS NOT NULL),
       (SELECT max(deleted_at) FROM discounts WHERE deleted_at IS NOT NULL)
     UNION ALL
     SELECT 'coupons',
       (SELECT count(*) FROM coupons WHERE deleted_at IS NOT NULL),
       (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'coupons'),
       (SELECT count(DISTINCT tenant_id) FROM coupons WHERE deleted_at IS NOT NULL),
       (SELECT min(deleted_at) FROM coupons WHERE deleted_at IS NOT NULL),
       (SELECT max(deleted_at) FROM coupons WHERE deleted_at IS NOT NULL)
     UNION ALL
     SELECT 'campaigns',
       (SELECT count(*) FROM campaigns WHERE deleted_at IS NOT NULL),
       (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'campaigns'),
       (SELECT count(DISTINCT tenant_id) FROM campaigns WHERE deleted_at IS NOT NULL),
       (SELECT min(deleted_at) FROM campaigns WHERE deleted_at IS NOT NULL),
       (SELECT max(deleted_at) FROM campaigns WHERE deleted_at IS NOT NULL)
     UNION ALL
     SELECT 'promotions',
       (SELECT count(*) FROM promotions WHERE deleted_at IS NOT NULL),
       (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'promotions'),
       (SELECT count(DISTINCT tenant_id) FROM promotions WHERE deleted_at IS NOT NULL),
       (SELECT min(deleted_at) FROM promotions WHERE deleted_at IS NOT NULL),
       (SELECT max(deleted_at) FROM promotions WHERE deleted_at IS NOT NULL)
   ) t
   ORDER BY coverage_rate_pct ASC, entity_table ASC

   “Query compiles cleanly with the expected columns. 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 stacks the 6 named tables (products, users, discounts, coupons, campaigns, promotions) into one 6-row table, each with total soft-deletes, matching audit_log 'delete' rows, capped coverage rate, distinct tenant count, and the earliest/most recent deleted_at — ordered worst-coverage-first.”

4. The report comes back overnight

   As Noah Fischer saw it, from the small dataset the agents worked on

   entity_table | total_soft_deleted | matching_audit_rows | coverage_rate_pct | distinct_tenants | earliest_deleted_at        | most_recent_deleted_at    
   -------------+--------------------+---------------------+-------------------+------------------+----------------------------+---------------------------
   products     |                615 |                   0 |               0.0 |               91 | 2023-01-02 10:00:00        | 2025-12-31 10:00:00       
   users        |                 39 |                  27 |              69.2 |               20 | 2020-04-28 19:22:51.144924 | 2025-11-10 15:18:24.435098
   campaigns    |                  0 |                   0 |              NULL |                0 | NULL                       | NULL                      
   coupons      |                  0 |                   0 |              NULL |                0 | NULL                       | NULL                      
   discounts    |                  0 |                   0 |              NULL |                0 | NULL                       | NULL                      
   promotions   |                  0 |                   0 |              NULL |                0 | NULL                       | NULL                      
   (6 rows)

5. What came of it

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

   Later queries that build on this report:

   • q311Full census of audit-trail deletion coverage, all soft-deletable tables
   • q312Audit traceability of order cancellations and invoice voids

   It builds on:

   • q108Audit log coverage by action type

6. It becomes benchmark query q109

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

   • spellingcast
   • numericsdocs/NUMERICS.md: rounding removed; results keep full precision; bare NUMERIC cast that only served the rounding removed; intended fraction: integer numerator cast to NUMERIC(18,6) before the division
   • 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 entity_table, total_soft_deleted, matching_audit_rows, CASE WHEN total_soft_deleted = 0 THEN NULL      ELSE ROUND(LEAST(matching_audit_rows::numeric / total_soft_deleted * 100, 100), 1)      ELSE LEAST(CAST(matching_audit_rows AS numeric(18,6)) / total_soft_deleted * 100, 100) END AS coverage_rate_pct, distinct_tenants, earliest_deleted_at, most_recent_deleted_atFROM (  SELECT 'products' AS entity_table,    (SELECT count(*) FROM products WHERE deleted_at IS NOT NULL) AS total_soft_deleted,    (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'products') AS matching_audit_rows,    (SELECT count(DISTINCT tenant_id) FROM products WHERE deleted_at IS NOT NULL) AS distinct_tenants,    (SELECT min(deleted_at) FROM products WHERE deleted_at IS NOT NULL) AS earliest_deleted_at,    (SELECT max(deleted_at) FROM products WHERE deleted_at IS NOT NULL) AS most_recent_deleted_at  UNION ALL  SELECT 'users',    (SELECT count(*) FROM users WHERE deleted_at IS NOT NULL),    (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'users'),    (SELECT count(DISTINCT tenant_id) FROM users WHERE deleted_at IS NOT NULL),    (SELECT min(deleted_at) FROM users WHERE deleted_at IS NOT NULL),    (SELECT max(deleted_at) FROM users WHERE deleted_at IS NOT NULL)  UNION ALL  SELECT 'discounts',    (SELECT count(*) FROM discounts WHERE deleted_at IS NOT NULL),    (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'discounts'),    (SELECT count(DISTINCT tenant_id) FROM discounts WHERE deleted_at IS NOT NULL),    (SELECT min(deleted_at) FROM discounts WHERE deleted_at IS NOT NULL),    (SELECT max(deleted_at) FROM discounts WHERE deleted_at IS NOT NULL)  UNION ALL  SELECT 'coupons',    (SELECT count(*) FROM coupons WHERE deleted_at IS NOT NULL),    (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'coupons'),    (SELECT count(DISTINCT tenant_id) FROM coupons WHERE deleted_at IS NOT NULL),    (SELECT min(deleted_at) FROM coupons WHERE deleted_at IS NOT NULL),    (SELECT max(deleted_at) FROM coupons WHERE deleted_at IS NOT NULL)  UNION ALL  SELECT 'campaigns',    (SELECT count(*) FROM campaigns WHERE deleted_at IS NOT NULL),    (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'campaigns'),    (SELECT count(DISTINCT tenant_id) FROM campaigns WHERE deleted_at IS NOT NULL),    (SELECT min(deleted_at) FROM campaigns WHERE deleted_at IS NOT NULL),    (SELECT max(deleted_at) FROM campaigns WHERE deleted_at IS NOT NULL)  UNION ALL  SELECT 'promotions',    (SELECT count(*) FROM promotions WHERE deleted_at IS NOT NULL),    (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'promotions'),    (SELECT count(DISTINCT tenant_id) FROM promotions WHERE deleted_at IS NOT NULL),    (SELECT min(deleted_at) FROM promotions WHERE deleted_at IS NOT NULL),    (SELECT max(deleted_at) FROM promotions WHERE deleted_at IS NOT NULL)) tORDER BY coverage_rate_pct ASC, entity_table ASCORDER BY coverage_rate_pct ASC NULLS LAST, entity_table ASC 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

   SELECT entity_table,
    total_soft_deleted,
    matching_audit_rows,
    CASE WHEN total_soft_deleted = 0 THEN NULL
         ELSE LEAST(CAST(matching_audit_rows AS numeric(18,6)) / total_soft_deleted * 100, 100)
    END AS coverage_rate_pct,
    distinct_tenants,
    earliest_deleted_at,
    most_recent_deleted_at
   FROM (
     SELECT 'products' AS entity_table,
       (SELECT count(*) FROM products WHERE deleted_at IS NOT NULL) AS total_soft_deleted,
       (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'products') AS matching_audit_rows,
       (SELECT count(DISTINCT tenant_id) FROM products WHERE deleted_at IS NOT NULL) AS distinct_tenants,
       (SELECT min(deleted_at) FROM products WHERE deleted_at IS NOT NULL) AS earliest_deleted_at,
       (SELECT max(deleted_at) FROM products WHERE deleted_at IS NOT NULL) AS most_recent_deleted_at
     UNION ALL
     SELECT 'users',
       (SELECT count(*) FROM users WHERE deleted_at IS NOT NULL),
       (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'users'),
       (SELECT count(DISTINCT tenant_id) FROM users WHERE deleted_at IS NOT NULL),
       (SELECT min(deleted_at) FROM users WHERE deleted_at IS NOT NULL),
       (SELECT max(deleted_at) FROM users WHERE deleted_at IS NOT NULL)
     UNION ALL
     SELECT 'discounts',
       (SELECT count(*) FROM discounts WHERE deleted_at IS NOT NULL),
       (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'discounts'),
       (SELECT count(DISTINCT tenant_id) FROM discounts WHERE deleted_at IS NOT NULL),
       (SELECT min(deleted_at) FROM discounts WHERE deleted_at IS NOT NULL),
       (SELECT max(deleted_at) FROM discounts WHERE deleted_at IS NOT NULL)
     UNION ALL
     SELECT 'coupons',
       (SELECT count(*) FROM coupons WHERE deleted_at IS NOT NULL),
       (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'coupons'),
       (SELECT count(DISTINCT tenant_id) FROM coupons WHERE deleted_at IS NOT NULL),
       (SELECT min(deleted_at) FROM coupons WHERE deleted_at IS NOT NULL),
       (SELECT max(deleted_at) FROM coupons WHERE deleted_at IS NOT NULL)
     UNION ALL
     SELECT 'campaigns',
       (SELECT count(*) FROM campaigns WHERE deleted_at IS NOT NULL),
       (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'campaigns'),
       (SELECT count(DISTINCT tenant_id) FROM campaigns WHERE deleted_at IS NOT NULL),
       (SELECT min(deleted_at) FROM campaigns WHERE deleted_at IS NOT NULL),
       (SELECT max(deleted_at) FROM campaigns WHERE deleted_at IS NOT NULL)
     UNION ALL
     SELECT 'promotions',
       (SELECT count(*) FROM promotions WHERE deleted_at IS NOT NULL),
       (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'promotions'),
       (SELECT count(DISTINCT tenant_id) FROM promotions WHERE deleted_at IS NOT NULL),
       (SELECT min(deleted_at) FROM promotions WHERE deleted_at IS NOT NULL),
       (SELECT max(deleted_at) FROM promotions WHERE deleted_at IS NOT NULL)
   ) t
   ORDER BY coverage_rate_pct ASC NULLS LAST, entity_table ASC NULLS LAST

   Scale factor 1000, c8a.24xlarge, after the last load. q109 on the start page, with every configuration.

7. 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 Noah Fischer (analyst for the Chief Information Security Officer):
     
     Title: Audit-trail coverage of actual soft-deletions, by entity table
     Question: For tables where the app actually soft-deletes rows (deleted_at), does audit_log's 'delete' action consistently capture those deletions, or is there a real gap between what was deleted and what the audit trail shows?
     Grain: one row per entity table, for a fixed set of 6 tables known to support soft-deletion: products, users, discounts, coupons, campaigns, promotions
     Columns:
     - entity table
     - total soft-deleted rows (deleted_at is not null, all-time, 2023-01-01 through 2025-12-31)
     - matching audit_log rows (action = 'delete' and entity_table = this table, all-time)
     - coverage rate (matching audit_log rows / soft-deleted rows, %)
     - distinct tenants with at least one soft-deleted row here
     - earliest deleted_at
     - most recent deleted_at
     Details: Source: for each of the 6 named tables, count rows with deleted_at is not null (that table itself, e.g. products.deleted_at, users.deleted_at, discounts.deleted_at, coupons.deleted_at, campaigns.deleted_at, promotions.deleted_at). Separately count audit_log rows with action = 'delete' and entity_table equal to that table's name (match on the literal table name string, ignore entity_schema). Coverage rate = matching audit_log rows / total soft-deleted rows, rounded to 1 decimal percent (cap at 100% if audit_log somehow has more rows than soft-deletes, and note if that happens). This is a UNION/stack of 6 independent per-table counts into one 6-row report, not a join across tables. Order by coverage rate ascending (worst gaps first), entity table name as tie-break.
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Felix calls dry_run

     SELECT entity_table,
      total_soft_deleted,
      matching_audit_rows,
      CASE WHEN total_soft_deleted = 0 THEN NULL
           ELSE ROUND(LEAST(matching_audit_rows::numeric / total_soft_deleted * 100, 100), 1)
      END AS coverage_rate_pct,
      distinct_tenants,
      earliest_deleted_at,
      most_recent_deleted_at
     FROM (
       SELECT 'products' AS entity_table,
         (SELECT count(*) FROM products WHERE deleted_at IS NOT NULL) AS total_soft_deleted,
         (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'products') AS matching_audit_rows,
         (SELECT count(DISTINCT tenant_id) FROM products WHERE deleted_at IS NOT NULL) AS distinct_tenants,
         (SELECT min(deleted_at) FROM products WHERE deleted_at IS NOT NULL) AS earliest_deleted_at,
         (SELECT max(deleted_at) FROM products WHERE deleted_at IS NOT NULL) AS most_recent_deleted_at
       UNION ALL
       SELECT 'users',
         (SELECT count(*) FROM users WHERE deleted_at IS NOT NULL),
         (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'users'),
         (SELECT count(DISTINCT tenant_id) FROM users WHERE deleted_at IS NOT NULL),
         (SELECT min(deleted_at) FROM users WHERE deleted_at IS NOT NULL),
         (SELECT max(deleted_at) FROM users WHERE deleted_at IS NOT NULL)
       UNION ALL
       SELECT 'discounts',
         (SELECT count(*) FROM discounts WHERE deleted_at IS NOT NULL),
         (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'discounts'),
         (SELECT count(DISTINCT tenant_id) FROM discounts WHERE deleted_at IS NOT NULL),
         (SELECT min(deleted_at) FROM discounts WHERE deleted_at IS NOT NULL),
         (SELECT max(deleted_at) FROM discounts WHERE deleted_at IS NOT NULL)
       UNION ALL
       SELECT 'coupons',
         (SELECT count(*) FROM coupons WHERE deleted_at IS NOT NULL),
         (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'coupons'),
         (SELECT count(DISTINCT tenant_id) FROM coupons WHERE deleted_at IS NOT NULL),
         (SELECT min(deleted_at) FROM coupons WHERE deleted_at IS NOT NULL),
         (SELECT max(deleted_at) FROM coupons WHERE deleted_at IS NOT NULL)
       UNION ALL
       SELECT 'campaigns',
         (SELECT count(*) FROM campaigns WHERE deleted_at IS NOT NULL),
         (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'campaigns'),
         (SELECT count(DISTINCT tenant_id) FROM campaigns WHERE deleted_at IS NOT NULL),
         (SELECT min(deleted_at) FROM campaigns WHERE deleted_at IS NOT NULL),
         (SELECT max(deleted_at) FROM campaigns WHERE deleted_at IS NOT NULL)
       UNION ALL
       SELECT 'promotions',
         (SELECT count(*) FROM promotions WHERE deleted_at IS NOT NULL),
         (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'promotions'),
         (SELECT count(DISTINCT tenant_id) FROM promotions WHERE deleted_at IS NOT NULL),
         (SELECT min(deleted_at) FROM promotions WHERE deleted_at IS NOT NULL),
         (SELECT max(deleted_at) FROM promotions WHERE deleted_at IS NOT NULL)
     ) t
     ORDER BY coverage_rate_pct ASC, entity_table ASC

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: entity_table, total_soft_deleted, matching_audit_rows, coverage_rate_pct, distinct_tenants, earliest_deleted_at, most_recent_deleted_at

   • FelixQuery compiles cleanly with the expected columns. Submitting now.
   • Felix calls submit

     SELECT entity_table,
      total_soft_deleted,
      matching_audit_rows,
      CASE WHEN total_soft_deleted = 0 THEN NULL
           ELSE ROUND(LEAST(matching_audit_rows::numeric / total_soft_deleted * 100, 100), 1)
      END AS coverage_rate_pct,
      distinct_tenants,
      earliest_deleted_at,
      most_recent_deleted_at
     FROM (
       SELECT 'products' AS entity_table,
         (SELECT count(*) FROM products WHERE deleted_at IS NOT NULL) AS total_soft_deleted,
         (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'products') AS matching_audit_rows,
         (SELECT count(DISTINCT tenant_id) FROM products WHERE deleted_at IS NOT NULL) AS distinct_tenants,
         (SELECT min(deleted_at) FROM products WHERE deleted_at IS NOT NULL) AS earliest_deleted_at,
         (SELECT max(deleted_at) FROM products WHERE deleted_at IS NOT NULL) AS most_recent_deleted_at
       UNION ALL
       SELECT 'users',
         (SELECT count(*) FROM users WHERE deleted_at IS NOT NULL),
         (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'users'),
         (SELECT count(DISTINCT tenant_id) FROM users WHERE deleted_at IS NOT NULL),
         (SELECT min(deleted_at) FROM users WHERE deleted_at IS NOT NULL),
         (SELECT max(deleted_at) FROM users WHERE deleted_at IS NOT NULL)
       UNION ALL
       SELECT 'discounts',
         (SELECT count(*) FROM discounts WHERE deleted_at IS NOT NULL),
         (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'discounts'),
         (SELECT count(DISTINCT tenant_id) FROM discounts WHERE deleted_at IS NOT NULL),
         (SELECT min(deleted_at) FROM discounts WHERE deleted_at IS NOT NULL),
         (SELECT max(deleted_at) FROM discounts WHERE deleted_at IS NOT NULL)
       UNION ALL
       SELECT 'coupons',
         (SELECT count(*) FROM coupons WHERE deleted_at IS NOT NULL),
         (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'coupons'),
         (SELECT count(DISTINCT tenant_id) FROM coupons WHERE deleted_at IS NOT NULL),
         (SELECT min(deleted_at) FROM coupons WHERE deleted_at IS NOT NULL),
         (SELECT max(deleted_at) FROM coupons WHERE deleted_at IS NOT NULL)
       UNION ALL
       SELECT 'campaigns',
         (SELECT count(*) FROM campaigns WHERE deleted_at IS NOT NULL),
         (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'campaigns'),
         (SELECT count(DISTINCT tenant_id) FROM campaigns WHERE deleted_at IS NOT NULL),
         (SELECT min(deleted_at) FROM campaigns WHERE deleted_at IS NOT NULL),
         (SELECT max(deleted_at) FROM campaigns WHERE deleted_at IS NOT NULL)
       UNION ALL
       SELECT 'promotions',
         (SELECT count(*) FROM promotions WHERE deleted_at IS NOT NULL),
         (SELECT count(*) FROM audit_log WHERE action = 'delete' AND entity_table = 'promotions'),
         (SELECT count(DISTINCT tenant_id) FROM promotions WHERE deleted_at IS NOT NULL),
         (SELECT min(deleted_at) FROM promotions WHERE deleted_at IS NOT NULL),
         (SELECT max(deleted_at) FROM promotions WHERE deleted_at IS NOT NULL)
     ) t
     ORDER BY coverage_rate_pct ASC, entity_table ASC

   • The warehouse answers

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

   • FelixSubmitted. The report stacks the 6 named tables (products, users, discounts, coupons, campaigns, promotions) into one 6-row table, each with total soft-deletes, matching audit_log 'delete' rows, capped coverage rate, distinct tenant count, and the earliest/most recent deleted_at — ordered worst-coverage-first.

   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 (272 KB) · Noah Fischer's transcript (875 KB) · Henrik Aalto's transcript (262 KB) · the night's plan (5 KB) · the night's reports (4 KB) · the week's memo (3 KB)