Queries · q325 · Data Protection Officer · week 2, night 1

Retention reality check beyond email: events, job_runs, page_views, system_health_checks

“Beyond the confirmed email_log failure, how much data in the other four policy-covered tables is currently sitting past its stated retention window?”

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 Data Protection Officer's desk

   Week 2; shared with Henrik Aalto, Chief Information Security Officer

   A data protection authority asked how we handle privacy: how many privacy requests we receive and how fast we complete them, how consent is recorded, and what remains after an account is deleted.

   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. Ines Laurent writes to Priyanka Desai

   The manager, at the start of week 2

   Subject: Week 2 — DPA inquiry changes our priorities

   Priyanka,

   Good work — this is exactly the kind of digging I wanted. A few things surprised me and now matter more because of the regulator inquiry (which I'm handling jointly with Henrik Aalto, CISO — our response needs to be accurate and defensible, not just reassuring).

   What struck me:

   • The three "open" erasure requests aren't really open, they're parked behind legal holds — but we don't actually know if those holds are still justified. If a regulator asked us today why those erasures haven't happened, "disputes" that are 1.5–2.6 years old is not a comfortable answer without evidence.
   • We have a closure-tracking gap (rejected requests never get a completion date) and apparently zero portability requests in three years. Both could be true, or both could be an artifact of how we log things — and I don't want to hand the regulator a number I can't stand behind.
   • The email retention failure is concrete and fixable, but it also means I can't yet answer "what remains after an account is deleted" with confidence for other categories until we know how widespread this is.
   • The consent anomaly (one category showing suspiciously perfect opt-in) undermines my confidence in saying "this is how consent is recorded" to a regulator until it's explained.

   So, for this week, please prioritize work that lets us answer the DPA's three questions honestly: volume and speed of privacy requests, how consent is captured and whether it's reliable, and what data persists post-deletion. That's the deliverable Henrik and I need to brief the board audit committee before we respond.

   My questions:

   1. For the three long-running legal holds, can we establish — from whatever evidence exists — whether the underlying disputes are still active, so I can decide whether to push for release or document why they must stand?
   2. Can we get a trustworthy, defensible count of privacy requests received and resolved, including the rejected ones and whether "zero portability requests ever" is real or a logging blind spot — the regulator will ask for exactly this?
   3. How confident can we be in what consent records actually show, given the anomaly you flagged — do we have other categories with the same too-clean pattern, and does that change how I'd describe our consent process to a regulator?
   4. Beyond email, how much shopper data elsewhere is sitting past its stated retention window right now — I need a true current picture of "what remains after deletion," not just the one confirmed violation.

   If all four can't fit, start with 2 and 4 — those go directly into the regulator response.

   Thanks, Ines

   Show the whole note
   A note from the Chief Information Security Officer, Henrik Aalto

   Subject: Security angle for the DPA response

   Hi Priyanka,

   A few things from my side that would strengthen the privacy response:

   1. When a data subject request results in erasure, can we show what login/session and access history remains afterward, and for how long — i.e. do our retention rules actually clear it out, or does it linger?
   2. For consent withdrawals, do we have any evidence that we stop using/collecting the related data promptly (e.g. no further sign-ins or sensitive-data access tied to that account after withdrawal)?
   3. On the completion-time question the DPA is asking about — can we also show whether requests touching account security data (sessions, API/service credentials, access logs) take longer to close than others? That's often where delays hide.

   Happy to compare notes before you send anything to data engineering.

   Henrik

3. Priyanka Desai plans the night

   The analyst · week 2, night 1, request 3 of 3

   Ines said to lead with priorities 2 and 4 if slots are tight, so tonight is entirely those two. For priority 2, I'm not just re-stating last week's 60-request total — Claire Dubois (Risk & Payments) posted ~48 requests company-wide in the same channel, which doesn't match my count, so before anything goes to the regulator I need an auditable type-x-status breakdown that either reconciles the two numbers or pinpoints exactly where they diverge. I'm also treating "zero portability requests ever" as a hypothesis to test rather than a fact: since data_subject_requests.data_export_id links to data_exports, I can check whether export jobs are actually being run for shoppers outside the formal DSAR table — if so, that's a logging blind spot, not a true zero. For priority 4, last week I only checked 2 of the 6 policy-covered tables (email_log confirmed broken, audit_log mostly fine with a narrow gap); tonight I'm closing out the remaining four (events, job_runs, page_views, system_health_checks) at a compact one-row-per-table grain so Ines has the full current picture of "what persists past its stated window," not just the one confirmed violation. I'm deferring the legal-hold evidence question (priority 1), the consent anomaly (priority 3), and Henrik's three asks to night 2, since those need either external evidence (disputes) or more design thought (what "touches security data" means for a request) that I don't want to rush tonight.

   Read the whole plan
   Retention reality check beyond email: events, job_runs, page_views, system_health_checks

   Question

   Beyond the confirmed email_log failure, how much data in the other four policy-covered tables is currently sitting past its stated retention window?

   One row per

   entity_table: events, job_runs, page_views, system_health_checks

   Columns

   • entity_table
   • stated retention_days (from retention_policies, confirmed uniform across tenants last week)
   • action_on_expiry
   • total rows currently in the live table (platform-wide)
   • oldest row age in days (as of 2026-01-05)
   • rows exceeding stated retention (count)
   • pct of rows exceeding stated retention

4. Bea 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)

   WITH policy AS (
     SELECT entity_table, MIN(retention_days) AS retention_days, MIN(action_on_expiry) AS action_on_expiry
     FROM retention_policies
     WHERE entity_table IN ('events','job_runs','page_views','system_health_checks')
     GROUP BY entity_table
   ),
   events_stats AS (
     SELECT 'events' AS entity_table,
            COUNT(*) AS total_rows,
            (DATE '2026-01-05' - CAST(MIN(t0.occurred_at) AS DATE)) AS oldest_age_days,
            COUNT(*) FILTER (WHERE t0.occurred_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'events'))) AS exceeding_rows
     FROM events t0
   ),
   job_runs_stats AS (
     SELECT 'job_runs' AS entity_table,
            COUNT(*) AS total_rows,
            (DATE '2026-01-05' - CAST(MIN(t0.queued_at) AS DATE)) AS oldest_age_days,
            COUNT(*) FILTER (WHERE t0.queued_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'job_runs'))) AS exceeding_rows
     FROM job_runs t0
   ),
   page_views_stats AS (
     SELECT 'page_views' AS entity_table,
            COUNT(*) AS total_rows,
            (DATE '2026-01-05' - CAST(MIN(t0.viewed_at) AS DATE)) AS oldest_age_days,
            COUNT(*) FILTER (WHERE t0.viewed_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'page_views'))) AS exceeding_rows
     FROM page_views t0
   ),
   system_health_checks_stats AS (
     SELECT 'system_health_checks' AS entity_table,
            COUNT(*) AS total_rows,
            (DATE '2026-01-05' - CAST(MIN(t0.checked_at) AS DATE)) AS oldest_age_days,
            COUNT(*) FILTER (WHERE t0.checked_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'system_health_checks'))) AS exceeding_rows
     FROM system_health_checks t0
   ),
   all_stats AS (
     SELECT * FROM events_stats
     UNION ALL SELECT * FROM job_runs_stats
     UNION ALL SELECT * FROM page_views_stats
     UNION ALL SELECT * FROM system_health_checks_stats
   )
   SELECT
     a.entity_table AS entity_table,
     COALESCE(p.retention_days, 0) AS stated_retention_days,
     COALESCE(p.action_on_expiry, 'unknown') AS action_on_expiry,
     a.total_rows AS total_rows,
     a.oldest_age_days AS oldest_row_age_days,
     a.exceeding_rows AS rows_exceeding_retention,
     ROUND(100.0 * a.exceeding_rows / NULLIF(a.total_rows, 0), 2) AS pct_exceeding_retention
   FROM all_stats a
   LEFT JOIN policy p ON p.entity_table = a.entity_table
   ORDER BY pct_exceeding_retention DESC, a.entity_table ASC

   “Query compiles cleanly and returns the expected one-row-per-table grain with all requested 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 gives one row per policy-covered table (events, job_runs, page_views, system_health_checks) with stated retention days, action on expiry, total rows, oldest row age, count and percentage of rows past the retention window — ordered worst-first by percentage exceeding, exactly as requested.”

5. The report comes back overnight

   As Priyanka Desai saw it, from the small dataset the agents worked on

   entity_table         | stated_retention_days | action_on_expiry | total_rows | oldest_row_age_days | rows_exceeding_retention | pct_exceeding_retention
   ---------------------+-----------------------+------------------+------------+---------------------+--------------------------+------------------------
   system_health_checks |                    35 | delete           |     129780 |                  39 |                    14832 |                   11.43
   job_runs             |                    90 | delete           |      49247 |                  94 |                     2281 |                    4.63
   events               |                  1095 | delete           |     360478 |                1100 |                     1324 |                    0.37
   page_views           |                  1095 | delete           |     396998 |                1100 |                     1449 |                    0.36
   (4 rows)

6. What came of it

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

   It builds on:

   • q119Retention policy landscape: distinct rules by entity table
   • q122email_log actual retention vs. the stated 730-day delete policy
   • q123audit_log actual archiving vs. the stated 730-day archive policy

7. It becomes benchmark query q325

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

   • numericsdocs/NUMERICS.md: rounding removed; results keep full precision
   • 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 policy AS (  SELECT entity_table, MIN(retention_days) AS retention_days, MIN(action_on_expiry) AS action_on_expiry  FROM retention_policies  WHERE entity_table IN ('events','job_runs','page_views','system_health_checks')  GROUP BY entity_table),events_stats AS (  SELECT 'events' AS entity_table,         COUNT(*) AS total_rows,         (DATE '2026-01-05' - CAST(MIN(t0.occurred_at) AS DATE)) AS oldest_age_days,         COUNT(*) FILTER (WHERE t0.occurred_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'events'))) AS exceeding_rows  FROM events t0),job_runs_stats AS (  SELECT 'job_runs' AS entity_table,         COUNT(*) AS total_rows,         (DATE '2026-01-05' - CAST(MIN(t0.queued_at) AS DATE)) AS oldest_age_days,         COUNT(*) FILTER (WHERE t0.queued_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'job_runs'))) AS exceeding_rows  FROM job_runs t0),page_views_stats AS (  SELECT 'page_views' AS entity_table,         COUNT(*) AS total_rows,         (DATE '2026-01-05' - CAST(MIN(t0.viewed_at) AS DATE)) AS oldest_age_days,         COUNT(*) FILTER (WHERE t0.viewed_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'page_views'))) AS exceeding_rows  FROM page_views t0),system_health_checks_stats AS (  SELECT 'system_health_checks' AS entity_table,         COUNT(*) AS total_rows,         (DATE '2026-01-05' - CAST(MIN(t0.checked_at) AS DATE)) AS oldest_age_days,         COUNT(*) FILTER (WHERE t0.checked_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'system_health_checks'))) AS exceeding_rows  FROM system_health_checks t0),all_stats AS (  SELECT * FROM events_stats  UNION ALL SELECT * FROM job_runs_stats  UNION ALL SELECT * FROM page_views_stats  UNION ALL SELECT * FROM system_health_checks_stats)SELECT  a.entity_table AS entity_table,  COALESCE(p.retention_days, 0) AS stated_retention_days,  COALESCE(p.action_on_expiry, 'unknown') AS action_on_expiry,  a.total_rows AS total_rows,  a.oldest_age_days AS oldest_row_age_days,  a.exceeding_rows AS rows_exceeding_retention,  ROUND(100.0 * a.exceeding_rows / NULLIF(a.total_rows, 0), 2) AS pct_exceeding_retention  100.0 * a.exceeding_rows / NULLIF(a.total_rows, 0) AS pct_exceeding_retentionFROM all_stats aLEFT JOIN policy p ON p.entity_table = a.entity_tableORDER BY pct_exceeding_retention DESC, a.entity_table ASCORDER BY pct_exceeding_retention DESC NULLS FIRST, a.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

   WITH policy AS (
     SELECT entity_table, MIN(retention_days) AS retention_days, MIN(action_on_expiry) AS action_on_expiry
     FROM retention_policies
     WHERE entity_table IN ('events','job_runs','page_views','system_health_checks')
     GROUP BY entity_table
   ),
   events_stats AS (
     SELECT 'events' AS entity_table,
            COUNT(*) AS total_rows,
            (DATE '2026-01-05' - CAST(MIN(t0.occurred_at) AS DATE)) AS oldest_age_days,
            COUNT(*) FILTER (WHERE t0.occurred_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'events'))) AS exceeding_rows
     FROM events t0
   ),
   job_runs_stats AS (
     SELECT 'job_runs' AS entity_table,
            COUNT(*) AS total_rows,
            (DATE '2026-01-05' - CAST(MIN(t0.queued_at) AS DATE)) AS oldest_age_days,
            COUNT(*) FILTER (WHERE t0.queued_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'job_runs'))) AS exceeding_rows
     FROM job_runs t0
   ),
   page_views_stats AS (
     SELECT 'page_views' AS entity_table,
            COUNT(*) AS total_rows,
            (DATE '2026-01-05' - CAST(MIN(t0.viewed_at) AS DATE)) AS oldest_age_days,
            COUNT(*) FILTER (WHERE t0.viewed_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'page_views'))) AS exceeding_rows
     FROM page_views t0
   ),
   system_health_checks_stats AS (
     SELECT 'system_health_checks' AS entity_table,
            COUNT(*) AS total_rows,
            (DATE '2026-01-05' - CAST(MIN(t0.checked_at) AS DATE)) AS oldest_age_days,
            COUNT(*) FILTER (WHERE t0.checked_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'system_health_checks'))) AS exceeding_rows
     FROM system_health_checks t0
   ),
   all_stats AS (
     SELECT * FROM events_stats
     UNION ALL SELECT * FROM job_runs_stats
     UNION ALL SELECT * FROM page_views_stats
     UNION ALL SELECT * FROM system_health_checks_stats
   )
   SELECT
     a.entity_table AS entity_table,
     COALESCE(p.retention_days, 0) AS stated_retention_days,
     COALESCE(p.action_on_expiry, 'unknown') AS action_on_expiry,
     a.total_rows AS total_rows,
     a.oldest_age_days AS oldest_row_age_days,
     a.exceeding_rows AS rows_exceeding_retention,
     100.0 * a.exceeding_rows / NULLIF(a.total_rows, 0) AS pct_exceeding_retention
   FROM all_stats a
   LEFT JOIN policy p ON p.entity_table = a.entity_table
   ORDER BY pct_exceeding_retention DESC NULLS FIRST, a.entity_table ASC NULLS LAST

   Scale factor 1000, c8a.24xlarge, after the last load. q325 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

   Bea's whole session, 7 turns

   • The request, as Bea received it

     Report request from Priyanka Desai (analyst for the Data Protection Officer):
     
     Title: Retention reality check beyond email: events, job_runs, page_views, system_health_checks
     Question: Beyond the confirmed email_log failure, how much data in the other four policy-covered tables is currently sitting past its stated retention window?
     Grain: one row per entity_table: events, job_runs, page_views, system_health_checks
     Columns:
     - entity_table
     - stated retention_days (from retention_policies, confirmed uniform across tenants last week)
     - action_on_expiry
     - total rows currently in the live table (platform-wide)
     - oldest row age in days (as of 2026-01-05)
     - rows exceeding stated retention (count)
     - pct of rows exceeding stated retention
     Details: Source: retention_policies for the stated retention_days/action_on_expiry per table (one value each, already confirmed uniform across all 120 tenants last week), joined against each live table using its natural timestamp: events.occurred_at, job_runs.queued_at, page_views.viewed_at, system_health_checks.checked_at. Use the literal date 2026-01-05 for all age math (never CURRENT_DATE/now()). This mirrors last week's email_log/audit_log checks but at a compact one-row-per-table summary so all four fit on one page, giving a complete current picture of retention-window violations across every policy-covered table, not just the one confirmed so far. Order by pct of rows exceeding retention, descending (worst first).
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Bea calls dry_run

     WITH policy AS (
       SELECT entity_table, MIN(retention_days) AS retention_days, MIN(action_on_expiry) AS action_on_expiry
       FROM retention_policies
       WHERE entity_table IN ('events','job_runs','page_views','system_health_checks')
       GROUP BY entity_table
     ),
     events_stats AS (
       SELECT 'events' AS entity_table,
              COUNT(*) AS total_rows,
              (DATE '2026-01-05' - CAST(MIN(t0.occurred_at) AS DATE)) AS oldest_age_days,
              COUNT(*) FILTER (WHERE t0.occurred_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'events'))) AS exceeding_rows
       FROM events t0
     ),
     job_runs_stats AS (
       SELECT 'job_runs' AS entity_table,
              COUNT(*) AS total_rows,
              (DATE '2026-01-05' - CAST(MIN(t0.queued_at) AS DATE)) AS oldest_age_days,
              COUNT(*) FILTER (WHERE t0.queued_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'job_runs'))) AS exceeding_rows
       FROM job_runs t0
     ),
     page_views_stats AS (
       SELECT 'page_views' AS entity_table,
              COUNT(*) AS total_rows,
              (DATE '2026-01-05' - CAST(MIN(t0.viewed_at) AS DATE)) AS oldest_age_days,
              COUNT(*) FILTER (WHERE t0.viewed_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'page_views'))) AS exceeding_rows
       FROM page_views t0
     ),
     system_health_checks_stats AS (
       SELECT 'system_health_checks' AS entity_table,
              COUNT(*) AS total_rows,
              (DATE '2026-01-05' - CAST(MIN(t0.checked_at) AS DATE)) AS oldest_age_days,
              COUNT(*) FILTER (WHERE t0.checked_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'system_health_checks'))) AS exceeding_rows
       FROM system_health_checks t0
     ),
     all_stats AS (
       SELECT * FROM events_stats
       UNION ALL SELECT * FROM job_runs_stats
       UNION ALL SELECT * FROM page_views_stats
       UNION ALL SELECT * FROM system_health_checks_stats
     )
     SELECT
       a.entity_table AS entity_table,
       COALESCE(p.retention_days, 0) AS stated_retention_days,
       COALESCE(p.action_on_expiry, 'unknown') AS action_on_expiry,
       a.total_rows AS total_rows,
       a.oldest_age_days AS oldest_row_age_days,
       a.exceeding_rows AS rows_exceeding_retention,
       ROUND(100.0 * a.exceeding_rows / NULLIF(a.total_rows, 0), 2) AS pct_exceeding_retention
     FROM all_stats a
     LEFT JOIN policy p ON p.entity_table = a.entity_table
     ORDER BY pct_exceeding_retention DESC, a.entity_table ASC

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: entity_table, stated_retention_days, action_on_expiry, total_rows, oldest_row_age_days, rows_exceeding_retention, pct_exceeding_retention

   • BeaQuery compiles cleanly and returns the expected one-row-per-table grain with all requested columns. Submitting.
   • Bea calls submit

     WITH policy AS (
       SELECT entity_table, MIN(retention_days) AS retention_days, MIN(action_on_expiry) AS action_on_expiry
       FROM retention_policies
       WHERE entity_table IN ('events','job_runs','page_views','system_health_checks')
       GROUP BY entity_table
     ),
     events_stats AS (
       SELECT 'events' AS entity_table,
              COUNT(*) AS total_rows,
              (DATE '2026-01-05' - CAST(MIN(t0.occurred_at) AS DATE)) AS oldest_age_days,
              COUNT(*) FILTER (WHERE t0.occurred_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'events'))) AS exceeding_rows
       FROM events t0
     ),
     job_runs_stats AS (
       SELECT 'job_runs' AS entity_table,
              COUNT(*) AS total_rows,
              (DATE '2026-01-05' - CAST(MIN(t0.queued_at) AS DATE)) AS oldest_age_days,
              COUNT(*) FILTER (WHERE t0.queued_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'job_runs'))) AS exceeding_rows
       FROM job_runs t0
     ),
     page_views_stats AS (
       SELECT 'page_views' AS entity_table,
              COUNT(*) AS total_rows,
              (DATE '2026-01-05' - CAST(MIN(t0.viewed_at) AS DATE)) AS oldest_age_days,
              COUNT(*) FILTER (WHERE t0.viewed_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'page_views'))) AS exceeding_rows
       FROM page_views t0
     ),
     system_health_checks_stats AS (
       SELECT 'system_health_checks' AS entity_table,
              COUNT(*) AS total_rows,
              (DATE '2026-01-05' - CAST(MIN(t0.checked_at) AS DATE)) AS oldest_age_days,
              COUNT(*) FILTER (WHERE t0.checked_at < (DATE '2026-01-05' - (SELECT retention_days FROM policy WHERE policy.entity_table = 'system_health_checks'))) AS exceeding_rows
       FROM system_health_checks t0
     ),
     all_stats AS (
       SELECT * FROM events_stats
       UNION ALL SELECT * FROM job_runs_stats
       UNION ALL SELECT * FROM page_views_stats
       UNION ALL SELECT * FROM system_health_checks_stats
     )
     SELECT
       a.entity_table AS entity_table,
       COALESCE(p.retention_days, 0) AS stated_retention_days,
       COALESCE(p.action_on_expiry, 'unknown') AS action_on_expiry,
       a.total_rows AS total_rows,
       a.oldest_age_days AS oldest_row_age_days,
       a.exceeding_rows AS rows_exceeding_retention,
       ROUND(100.0 * a.exceeding_rows / NULLIF(a.total_rows, 0), 2) AS pct_exceeding_retention
     FROM all_stats a
     LEFT JOIN policy p ON p.entity_table = a.entity_table
     ORDER BY pct_exceeding_retention DESC, a.entity_table ASC

   • The warehouse answers

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

   • BeaSubmitted. The report gives one row per policy-covered table (`events`, `job_runs`, `page_views`, `system_health_checks`) with stated retention days, action on expiry, total rows, oldest row age, count and percentage of rows past the retention window — ordered worst-first by percentage exceeding, exactly as requested.

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

   Bea's transcript (275 KB) · Priyanka Desai's transcript (894 KB) · Ines Laurent's transcript (263 KB) · the night's plan (5 KB) · the night's reports (4 KB) · the week's memo (3 KB)