Queries · q277 · Head of Risk and Payments · week 2, night 1

February 2025 repeat-failure accounts: did they succeed afterward?

“Among the accounts with 5+ failed logins in February 2025, how many went on to log in successfully afterward, and how soon — i.e. was this a near-miss or just noise?”

Manager · asked the questionAylin DemirHead of Risk and Payments. Previously in fraud operations at a bank. Responsible for payment performance and for keeping bad actors out.

Analyst · planned the reportClaire DuboisTime-series minded: weekly and daily trends, before and after, seasonality.

Engineer · wrote the SQLOmarPragmatic: the shortest correct query, aggregates first and joins dimensions afterwards, FILTER clauses for conditional counts.

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

A business event lands on the Head of Risk and Payments's desk
Week 1; shared with Rosa Van den Berg, Head of Data
An enterprise prospect sent a security and compliance questionnaire covering account security, access control, privacy requests, data retention and audit trails. Sales wants answers backed by evidence from our own systems, not by policy documents.
One of the 35 events we wrote for the role play; it shaped the manager's first note. How events arise · All the events
Aylin Demir writes to Claire Dubois
The manager, at the start of week 2
Subject: Re: Questionnaire memo — good work, a few threads to pull

Claire,

This is a strong memo, and the gaps you flagged are exactly the kind of thing I'd rather surface now than have a prospect's security team find later. A few things stand out and I want to understand them better before we finalize anything.

The February 2025 login spike is the one that worries me most. A 10x jump in failures with no corresponding rise in flagged suspicious activity is a real detection gap, not just a reporting curiosity — if that was a credential-stuffing attempt, I need to know whether it actually got anywhere.

Questions for this week:
1. On the February 2025 spike — did it cluster around a handful of accounts, merchants, or sources, or was it spread evenly? And did any of the accounts that failed repeatedly go on to log in successfully shortly after? That tells us whether this was noise or an actual near-miss.
2. On the three unresolved rejection dates — rather than retrying the same technical path, can we establish, using whatever reliable timestamp we do have, roughly when each decision was made relative to the request date? I don't need the exact figure if it's not retrievable — I need to know whether we were plausibly within the deadline or not, so we can decide how to word that answer.
3. On flagged incidents (the one we deferred) — now that we're another week in, I want this one: how many incidents have been flagged, how severe, how long they typically take to close, and whether anything has sat open a long time. This is likely to come up in the same breath as the login spike.
4. On the tables with no retention policy — before I escalate this as a gap, I want to know how exposed we actually are: roughly how far back does that data go today? If it's a few months, this is a minor cleanup; if it's three years, it's a bigger conversation with Rosa.
Keep the role/permission logging gap and the broken query as noted — no need to keep spending slots chasing those; I'll raise the first with engineering myself and we can note the second as a known limitation in the memo.

Thanks, Aylin
Claire Dubois plans the night
The analyst · week 2, night 1, request 2 of 3
Aylin flagged the Feb-2025 login spike as her top worry this week and split it into two distinct questions (concentration vs. near-miss outcome), so I'm giving it two of tonight's three slots since they're different grains (merchant-level vs. account-outcome-level) and shouldn't be bundled. For the third slot I'm taking a cheap, low-risk shot at the DSAR rejection-timing question using a completely different, simpler path than the one that broke twice last week: data_subject_requests.updated_at itself, with no JSON/change_history involved — if these 3 records weren't touched again after rejection, updated_at is a reasonable (if imperfect) proxy for "when was this decided," and it avoids re-spending slots on the broken query. I'm holding incidents (#3) and the retention-exposure check (#4) for night 2, since we have 4 nights this week and the spike is clearly the most time-sensitive item. Keeping outputs aggregate (merchant names are fine, per other teams' precedent; no individual emails/usernames).
February 2025 repeat-failure accounts: did they succeed afterward?
Question
Among the accounts with 5+ failed logins in February 2025, how many went on to log in successfully afterward, and how soon — i.e. was this a near-miss or just noise?
One row per
outcome bucket, computed over the cohort of distinct emails with >=5 failed login_attempts rows in February 2025 (previously counted at 102)
Columns
- outcome_bucket (succeeded_within_1h / succeeded_within_24h / succeeded_later_in_month / never_succeeded_in_month)
- email_count
- pct_of_cohort
- avg_failed_attempts_before_success (for buckets with a success; blank for never_succeeded)
- distinct_tenants_affected

Omar 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 failed_feb AS (
  SELECT email, occurred_at
  FROM login_attempts
  WHERE succeeded = false
    AND email IS NOT NULL
    AND occurred_at >= DATE '2025-02-01' AND occurred_at < DATE '2025-03-01'
),
cohort AS (
  SELECT email, COUNT(*) AS failed_cnt, MAX(occurred_at) AS last_failed_at
  FROM failed_feb
  GROUP BY email
  HAVING COUNT(*) >= 5
),
success_feb AS (
  SELECT email, occurred_at
  FROM login_attempts
  WHERE succeeded = true
    AND email IS NOT NULL
    AND occurred_at >= DATE '2025-02-01' AND occurred_at < DATE '2025-03-01'
),
cohort_outcome AS (
  SELECT c.email, c.failed_cnt, c.last_failed_at,
         MIN(s.occurred_at) AS first_success_at
  FROM cohort c
  LEFT JOIN success_feb s
    ON s.email = c.email AND s.occurred_at > c.last_failed_at
  GROUP BY c.email, c.failed_cnt, c.last_failed_at
),
bucketed AS (
  SELECT
    email, failed_cnt,
    CASE
      WHEN first_success_at IS NULL THEN 'never_succeeded_in_month'
      WHEN first_success_at <= last_failed_at + INTERVAL '1 hour' THEN 'succeeded_within_1h'
      WHEN first_success_at <= last_failed_at + INTERVAL '24 hour' THEN 'succeeded_within_24h'
      ELSE 'succeeded_later_in_month'
    END AS outcome_bucket
  FROM cohort_outcome
)
SELECT
  b.outcome_bucket,
  COUNT(*) AS email_count,
  ROUND(100.0 * COUNT(*) / (SELECT COUNT(*) FROM cohort), 1) AS pct_of_cohort,
  ROUND(AVG(b.failed_cnt) FILTER (WHERE b.outcome_bucket <> 'never_succeeded_in_month'), 2) AS avg_failed_attempts_before_success,
  COUNT(DISTINCT la.tenant_id) AS distinct_tenants_affected
FROM bucketed b
JOIN login_attempts la
  ON la.email = b.email
  AND la.occurred_at >= DATE '2025-02-01' AND la.occurred_at < DATE '2025-03-01'
GROUP BY b.outcome_bucket
ORDER BY CASE b.outcome_bucket
  WHEN 'succeeded_within_1h' THEN 1
  WHEN 'succeeded_within_24h' THEN 2
  WHEN 'succeeded_later_in_month' THEN 3
  WHEN 'never_succeeded_in_month' THEN 4
END

“Query compiles and returns the expected columns in the required bucket order.”

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

“Submitted. Summary of the approach: - Cohort: distinct emails with ≥5 succeeded=false rows in Feb 2025 (matched on email across tenants, since login_attempts.email is unique per tenant, not globally). - Bucketing: for each cohort email, took its last failed attempt in Feb, then the first subsequent succeeded=true attempt in Feb; classified the gap into the four requested buckets. - Metrics: email_count and pct_of_cohort per bucket; avg_failed_attempts_before_success (NULL for never_succeeded_in_month); distinct_tenants_affected counted from all Feb login_attempts rows tied to that email. Flagged in the submit note that the cross-tenant email matching was an interpretive choice, since the schema only guarantees email uniqueness within a tenant.”

The report comes back overnight

As Claire Dubois saw it, from the small dataset the agents worked on

outcome_bucket           | email_count | pct_of_cohort | avg_failed_attempts_before_success | distinct_tenants_affected
-------------------------+-------------+---------------+------------------------------------+--------------------------
succeeded_within_1h      |          42 |          41.2 |                                5.6 |                         3
succeeded_within_24h     |          12 |          11.8 |                                5.0 |                         2
succeeded_later_in_month |          89 |          87.3 |                               5.51 |                         2
never_succeeded_in_month |         452 |         443.1 |                               NULL |                         3
(4 rows)

What came of it
The memo and the queries that followed; links found by title, so inferred
Later queries that build on this report:
- q279February 2025 repeat-failure accounts: did they succeed afterward? (corrected)
It builds on:
- q074Failed login attempts and suspicious access patterns by month

It becomes benchmark query q277

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

numericsdocs/NUMERICS.md: rounding removed; results keep full precision; AVG over an integer computed as a decimal: AVG(CAST(x AS NUMERIC(18,6)))
made explicita selected t.col gets AS col, its standard name made explicit (4×)
determinismthe standard leaves the place of NULLs in a sort to the implementation: NULLS LAST ascending, NULLS FIRST descending, as in the reference results

The benchmark text, against the submitted SQL

WITH failed_feb AS (  SELECT email, occurred_at  FROM login_attempts  WHERE succeeded = false    AND email IS NOT NULL    AND occurred_at >= DATE '2025-02-01' AND occurred_at < DATE '2025-03-01'),cohort AS (  SELECT email, COUNT(*) AS failed_cnt, MAX(occurred_at) AS last_failed_at  FROM failed_feb  GROUP BY email  HAVING COUNT(*) >= 5),success_feb AS (  SELECT email, occurred_at  FROM login_attempts  WHERE succeeded = true    AND email IS NOT NULL    AND occurred_at >= DATE '2025-02-01' AND occurred_at < DATE '2025-03-01'),cohort_outcome AS (  SELECT c.email, c.failed_cnt, c.last_failed_at,  SELECT c.email AS email, c.failed_cnt AS failed_cnt, c.last_failed_at AS last_failed_at,         MIN(s.occurred_at) AS first_success_at  FROM cohort c  LEFT JOIN success_feb s    ON s.email = c.email AND s.occurred_at > c.last_failed_at  GROUP BY c.email, c.failed_cnt, c.last_failed_at),bucketed AS (  SELECT    email, failed_cnt,    CASE      WHEN first_success_at IS NULL THEN 'never_succeeded_in_month'      WHEN first_success_at <= last_failed_at + INTERVAL '1 hour' THEN 'succeeded_within_1h'      WHEN first_success_at <= last_failed_at + INTERVAL '24 hour' THEN 'succeeded_within_24h'      ELSE 'succeeded_later_in_month'    END AS outcome_bucket  FROM cohort_outcome)SELECT  b.outcome_bucket,  b.outcome_bucket AS outcome_bucket,  COUNT(*) AS email_count,  ROUND(100.0 * COUNT(*) / (SELECT COUNT(*) FROM cohort), 1) AS pct_of_cohort,  ROUND(AVG(b.failed_cnt) FILTER (WHERE b.outcome_bucket <> 'never_succeeded_in_month'), 2) AS avg_failed_attempts_before_success,  100.0 * COUNT(*) / (SELECT COUNT(*) FROM cohort) AS pct_of_cohort,  AVG(CAST(b.failed_cnt AS NUMERIC(18,6))) FILTER (WHERE b.outcome_bucket <> 'never_succeeded_in_month') AS avg_failed_attempts_before_success,  COUNT(DISTINCT la.tenant_id) AS distinct_tenants_affectedFROM bucketed bJOIN login_attempts la  ON la.email = b.email  AND la.occurred_at >= DATE '2025-02-01' AND la.occurred_at < DATE '2025-03-01'GROUP BY b.outcome_bucketORDER BY CASE b.outcome_bucket  WHEN 'succeeded_within_1h' THEN 1  WHEN 'succeeded_within_24h' THEN 2  WHEN 'succeeded_later_in_month' THEN 3  WHEN 'never_succeeded_in_month' THEN 4ENDEND NULLS LAST

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

WITH failed_feb AS (
  SELECT email, occurred_at
  FROM login_attempts
  WHERE succeeded = false
    AND email IS NOT NULL
    AND occurred_at >= DATE '2025-02-01' AND occurred_at < DATE '2025-03-01'
),
cohort AS (
  SELECT email, COUNT(*) AS failed_cnt, MAX(occurred_at) AS last_failed_at
  FROM failed_feb
  GROUP BY email
  HAVING COUNT(*) >= 5
),
success_feb AS (
  SELECT email, occurred_at
  FROM login_attempts
  WHERE succeeded = true
    AND email IS NOT NULL
    AND occurred_at >= DATE '2025-02-01' AND occurred_at < DATE '2025-03-01'
),
cohort_outcome AS (
  SELECT c.email AS email, c.failed_cnt AS failed_cnt, c.last_failed_at AS last_failed_at,
         MIN(s.occurred_at) AS first_success_at
  FROM cohort c
  LEFT JOIN success_feb s
    ON s.email = c.email AND s.occurred_at > c.last_failed_at
  GROUP BY c.email, c.failed_cnt, c.last_failed_at
),
bucketed AS (
  SELECT
    email, failed_cnt,
    CASE
      WHEN first_success_at IS NULL THEN 'never_succeeded_in_month'
      WHEN first_success_at <= last_failed_at + INTERVAL '1 hour' THEN 'succeeded_within_1h'
      WHEN first_success_at <= last_failed_at + INTERVAL '24 hour' THEN 'succeeded_within_24h'
      ELSE 'succeeded_later_in_month'
    END AS outcome_bucket
  FROM cohort_outcome
)
SELECT
  b.outcome_bucket AS outcome_bucket,
  COUNT(*) AS email_count,
  100.0 * COUNT(*) / (SELECT COUNT(*) FROM cohort) AS pct_of_cohort,
  AVG(CAST(b.failed_cnt AS NUMERIC(18,6))) FILTER (WHERE b.outcome_bucket <> 'never_succeeded_in_month') AS avg_failed_attempts_before_success,
  COUNT(DISTINCT la.tenant_id) AS distinct_tenants_affected
FROM bucketed b
JOIN login_attempts la
  ON la.email = b.email
  AND la.occurred_at >= DATE '2025-02-01' AND la.occurred_at < DATE '2025-03-01'
GROUP BY b.outcome_bucket
ORDER BY CASE b.outcome_bucket
  WHEN 'succeeded_within_1h' THEN 1
  WHEN 'succeeded_within_24h' THEN 2
  WHEN 'succeeded_later_in_month' THEN 3
  WHEN 'never_succeeded_in_month' THEN 4
END NULLS LAST

Scale factor 1000, c8a.24xlarge, after the last load. q277 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

Omar's whole session, 7 turns

The request, as Omar received it

Report request from Claire Dubois (analyst for the Head of Risk and Payments):

Title: February 2025 repeat-failure accounts: did they succeed afterward?
Question: Among the accounts with 5+ failed logins in February 2025, how many went on to log in successfully afterward, and how soon — i.e. was this a near-miss or just noise?
Grain: one row per outcome bucket, computed over the cohort of distinct emails with >=5 failed login_attempts rows in February 2025 (previously counted at 102)
Columns:
- outcome_bucket (succeeded_within_1h / succeeded_within_24h / succeeded_later_in_month / never_succeeded_in_month)
- email_count
- pct_of_cohort
- avg_failed_attempts_before_success (for buckets with a success; blank for never_succeeded)
- distinct_tenants_affected
Details: Source: login_attempts. Define the cohort as distinct email values with >=5 failed (succeeded=false) rows in Feb 2025. For each such email, find its last failed attempt in Feb 2025, then look for the first subsequent succeeded=true attempt by that same email within February 2025; bucket by the time gap (within 1h, within 24h, later in the month) or 'never_succeeded_in_month' if none found. Report only the aggregated bucket counts — do not print individual email addresses. Order buckets: succeeded_within_1h, succeeded_within_24h, succeeded_later_in_month, never_succeeded_in_month.

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

Omar calls dry_run

WITH failed_feb AS (
  SELECT email, occurred_at
  FROM login_attempts
  WHERE succeeded = false
    AND email IS NOT NULL
    AND occurred_at >= DATE '2025-02-01' AND occurred_at < DATE '2025-03-01'
),
cohort AS (
  SELECT email, COUNT(*) AS failed_cnt, MAX(occurred_at) AS last_failed_at
  FROM failed_feb
  GROUP BY email
  HAVING COUNT(*) >= 5
),
success_feb AS (
  SELECT email, occurred_at
  FROM login_attempts
  WHERE succeeded = true
    AND email IS NOT NULL
    AND occurred_at >= DATE '2025-02-01' AND occurred_at < DATE '2025-03-01'
),
cohort_outcome AS (
  SELECT c.email, c.failed_cnt, c.last_failed_at,
         MIN(s.occurred_at) AS first_success_at
  FROM cohort c
  LEFT JOIN success_feb s
    ON s.email = c.email AND s.occurred_at > c.last_failed_at
  GROUP BY c.email, c.failed_cnt, c.last_failed_at
),
bucketed AS (
  SELECT
    email, failed_cnt,
    CASE
      WHEN first_success_at IS NULL THEN 'never_succeeded_in_month'
      WHEN first_success_at <= last_failed_at + INTERVAL '1 hour' THEN 'succeeded_within_1h'
      WHEN first_success_at <= last_failed_at + INTERVAL '24 hour' THEN 'succeeded_within_24h'
      ELSE 'succeeded_later_in_month'
    END AS outcome_bucket
  FROM cohort_outcome
)
SELECT
  b.outcome_bucket,
  COUNT(*) AS email_count,
  ROUND(100.0 * COUNT(*) / (SELECT COUNT(*) FROM cohort), 1) AS pct_of_cohort,
  ROUND(AVG(b.failed_cnt) FILTER (WHERE b.outcome_bucket <> 'never_succeeded_in_month'), 2) AS avg_failed_attempts_before_success,
  COUNT(DISTINCT la.tenant_id) AS distinct_tenants_affected
FROM bucketed b
JOIN login_attempts la
  ON la.email = b.email
  AND la.occurred_at >= DATE '2025-02-01' AND la.occurred_at < DATE '2025-03-01'
GROUP BY b.outcome_bucket
ORDER BY CASE b.outcome_bucket
  WHEN 'succeeded_within_1h' THEN 1
  WHEN 'succeeded_within_24h' THEN 2
  WHEN 'succeeded_later_in_month' THEN 3
  WHEN 'never_succeeded_in_month' THEN 4
END

The warehouse answers

STATUS: COMPILES (dry run on the empty schema, no data read)
COLUMNS: outcome_bucket, email_count, pct_of_cohort, avg_failed_attempts_before_success, distinct_tenants_affected

OmarQuery compiles and returns the expected columns in the required bucket order.

Omar calls submit

WITH failed_feb AS (
  SELECT email, occurred_at
  FROM login_attempts
  WHERE succeeded = false
    AND email IS NOT NULL
    AND occurred_at >= DATE '2025-02-01' AND occurred_at < DATE '2025-03-01'
),
cohort AS (
  SELECT email, COUNT(*) AS failed_cnt, MAX(occurred_at) AS last_failed_at
  FROM failed_feb
  GROUP BY email
  HAVING COUNT(*) >= 5
),
success_feb AS (
  SELECT email, occurred_at
  FROM login_attempts
  WHERE succeeded = true
    AND email IS NOT NULL
    AND occurred_at >= DATE '2025-02-01' AND occurred_at < DATE '2025-03-01'
),
cohort_outcome AS (
  SELECT c.email, c.failed_cnt, c.last_failed_at,
         MIN(s.occurred_at) AS first_success_at
  FROM cohort c
  LEFT JOIN success_feb s
    ON s.email = c.email AND s.occurred_at > c.last_failed_at
  GROUP BY c.email, c.failed_cnt, c.last_failed_at
),
bucketed AS (
  SELECT
    email, failed_cnt,
    CASE
      WHEN first_success_at IS NULL THEN 'never_succeeded_in_month'
      WHEN first_success_at <= last_failed_at + INTERVAL '1 hour' THEN 'succeeded_within_1h'
      WHEN first_success_at <= last_failed_at + INTERVAL '24 hour' THEN 'succeeded_within_24h'
      ELSE 'succeeded_later_in_month'
    END AS outcome_bucket
  FROM cohort_outcome
)
SELECT
  b.outcome_bucket,
  COUNT(*) AS email_count,
  ROUND(100.0 * COUNT(*) / (SELECT COUNT(*) FROM cohort), 1) AS pct_of_cohort,
  ROUND(AVG(b.failed_cnt) FILTER (WHERE b.outcome_bucket <> 'never_succeeded_in_month'), 2) AS avg_failed_attempts_before_success,
  COUNT(DISTINCT la.tenant_id) AS distinct_tenants_affected
FROM bucketed b
JOIN login_attempts la
  ON la.email = b.email
  AND la.occurred_at >= DATE '2025-02-01' AND la.occurred_at < DATE '2025-03-01'
GROUP BY b.outcome_bucket
ORDER BY CASE b.outcome_bucket
  WHEN 'succeeded_within_1h' THEN 1
  WHEN 'succeeded_within_24h' THEN 2
  WHEN 'succeeded_later_in_month' THEN 3
  WHEN 'never_succeeded_in_month' THEN 4
END

The warehouse answers

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

OmarSubmitted. Summary of the approach: - **Cohort**: distinct emails with ≥5 `succeeded=false` rows in Feb 2025 (matched on email across tenants, since `login_attempts.email` is unique per tenant, not globally). - **Bucketing**: for each cohort email, took its last failed attempt in Feb, then the first subsequent `succeeded=true` attempt in Feb; classified the gap into the four requested buckets. - **Metrics**: email_count and pct_of_cohort per bucket; avg_failed_attempts_before_success (NULL for `never_succeeded_in_month`); distinct_tenants_affected counted from all Feb login_attempts rows tied to that email. Flagged in the submit note that the cross-tenant email matching was an interpretive choice, since the schema only guarantees email uniqueness within a tenant.

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

Omar's transcript (277 KB) · Claire Dubois's transcript (838 KB) · Aylin Demir's transcript (263 KB) · the night's plan (4 KB) · the night's reports (15 KB) · the week's memo (3 KB)

February 2025 repeat-failure accounts: did they succeed afterward?

A business event lands on the Head of Risk and Payments's desk

Aylin Demir writes to Claire Dubois

Claire Dubois plans the night

Omar writes the SQL

The report comes back overnight

What came of it

It becomes benchmark query q277

Everything, as recorded