Queries · q038 · VP Customer Experience · week 1, night 1

Response & resolution times by merchant, 2025

“How much does support speed vary across merchants, and who is fastest/slowest?”

Manager · asked the questionDaniel AchterbergVP Customer Experience. Former head of support at a marketplace. Merchants run their helpdesk on Tidewick, so their shoppers' experience is partly Tidewick's reputation.

Analyst · planned the reportMateo RuizComparative: likes year-over-year and peer comparisons across merchants and countries.

Engineer · wrote the SQLBeaWrites SQL the way the company's BI tool generates it: verbose fully qualified aliases (t0, t1, ...), a subquery per measure joined back on the grain keys, COALESCE everywhere.

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

Daniel Achterberg writes to Mateo Ruiz
The manager, at the start of week 1
Subject: Where I'd like to start — support & shopper experience

Hi Mateo,

Good to have you on this with me. I'm still building my map of the business, so I want to keep this week broad — I'd rather get a solid first read on a few big questions than a deep dive on one. Here's what's on my mind, roughly in priority order:
1. How are we doing on response and resolution times, and is it getting better or worse? I keep hearing "we're slow" anecdotally but have no sense of the trend, or whether it varies a lot by merchant. I need a baseline I can put in front of the leadership team.
2. Where is the support load actually coming from? What kinds of problems are shoppers contacting merchants about most, and has that mix been shifting lately? I want to know if there's a theme we could fix at the platform level instead of merchant-by-merchant.
3. What does the returns and refunds experience look like from the shopper's side? How often shoppers end up wanting their money back, how long that takes to resolve, and whether that's a smooth process or a source of frustration. This feels close to our reputation.
4. How satisfied are shoppers overall, and where is satisfaction weakest? I'd like a sense of satisfaction levels and whether certain merchants or situations stand out as problem spots.
5. (If there's room this week) What are reviews and ratings telling us? Even a first look at how shoppers rate their purchases, and whether that lines up with what we see in support, would help me understand if there are blind spots in the ticket data.
What I need these for: I'm meeting merchant success and the exec team soon and want a credible, numbers-backed view of shopper experience — not just war stories. I also want to start spotting the two or three things that, if we fixed them, would move the needle most broadly across merchants, rather than chasing individual complaints.

Given the slot constraints, let's prioritize #1 through #3 this week if we can't fit all five — those feel most urgent to me. Happy to talk through any of this before you lock in requests.

Thanks, Daniel
Mateo Ruiz plans the night
The analyst · week 1, night 1, request 2 of 3
Daniel's top priority is #1 (response/resolution trend, and variance by merchant), so I'm spending 2 of tonight's 3 slots there: a platform-wide monthly trend (to see direction of travel) and a per-merchant snapshot for the latest full year (to see spread across merchants, since he likes peer comparisons). The third slot starts #2 (where load comes from) with a tag-based mix comparing the last two half-years to catch any shift. I'm deliberately holding off on YoY columns inside the trend report itself since the 36-month series already shows year-over-year pattern, and saving width for the core time metrics. Returns/refunds (#3), satisfaction (#4) and reviews (#5) are next on my list for later nights.
Response & resolution times by merchant, 2025
Question
How much does support speed vary across merchants, and who is fastest/slowest?
One row per
tenant (merchant), for tickets created in 2025
Columns
- merchant name
- tickets created
- avg first response hours
- median first response hours
- avg resolution hours
- median resolution hours
- SLA breach rate (%)

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)

SELECT 
  t_tenant.name AS merchant_name,
  t_counts.tickets_created AS tickets_created,
  ROUND(CAST(t_fr.avg_first_response_hours AS numeric), 2) AS avg_first_response_hours,
  ROUND(CAST(t_fr.median_first_response_hours AS numeric), 2) AS median_first_response_hours,
  ROUND(CAST(t_res.avg_resolution_hours AS numeric), 2) AS avg_resolution_hours,
  ROUND(CAST(t_res.median_resolution_hours AS numeric), 2) AS median_resolution_hours,
  ROUND(CAST(COALESCE(t_breach.breached_tickets, 0) AS numeric) / CAST(t_counts.tickets_created AS numeric) * 100.0, 2) AS sla_breach_rate_pct
FROM (
  SELECT t0.tenant_id AS tenant_id, COUNT(*) AS tickets_created
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
  GROUP BY t0.tenant_id
) t_counts
JOIN tenants t_tenant
  ON t_tenant.id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id,
    AVG(EXTRACT(EPOCH FROM (t0.first_response_at - t0.created_at)) / 3600.0) AS avg_first_response_hours,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (t0.first_response_at - t0.created_at)) / 3600.0) AS median_first_response_hours
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
    AND t0.first_response_at IS NOT NULL
  GROUP BY t0.tenant_id
) t_fr
  ON t_fr.tenant_id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id,
    AVG(EXTRACT(EPOCH FROM (t0.resolved_at - t0.created_at)) / 3600.0) AS avg_resolution_hours,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (t0.resolved_at - t0.created_at)) / 3600.0) AS median_resolution_hours
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
    AND t0.resolved_at IS NOT NULL
  GROUP BY t0.tenant_id
) t_res
  ON t_res.tenant_id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id, COUNT(DISTINCT t0.id) AS breached_tickets
  FROM tickets t0
  JOIN sla_breaches t1 ON t1.ticket_id = t0.id
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
  GROUP BY t0.tenant_id
) t_breach
  ON t_breach.tenant_id = t_counts.tenant_id
WHERE t_counts.tickets_created >= 20
ORDER BY t_counts.tickets_created DESC, t_tenant.name ASC
LIMIT 50

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

“Submitted. The query answers the request at the requested grain (one row per tenant, 2025 tickets, ≥20-ticket threshold), with avg/median first-response and resolution hours and SLA breach rate, ordered by ticket volume descending and capped at 50 rows per house standards.”

The report comes back overnight

As Mateo Ruiz saw it, from the small dataset the agents worked on

merchant_name                 | tickets_created | avg_first_response_hours | median_first_response_hours | avg_resolution_hours | median_resolution_hours | sla_breach_rate_pct
------------------------------+-----------------+--------------------------+-----------------------------+----------------------+-------------------------+--------------------
Cragsley Outfitters           |             400 |                    20.94 |                       10.57 |                39.14 |                   20.96 |               61.25
Marrowby & Vane               |             231 |                    47.24 |                       11.11 |                84.93 |                   20.25 |               61.47
Studio Vrij                   |             166 |                    60.29 |                       11.71 |               125.06 |                   25.93 |               56.63
Brennquell Kaffee             |             137 |                     5.60 |                        2.99 |                15.94 |                    8.83 |               32.12
Grind Collective              |             122 |                    36.20 |                        6.53 |                64.42 |                   20.24 |               45.08
Petalcourt Beauty             |             119 |                    31.37 |                        6.75 |                48.71 |                   22.50 |               57.14
Kibblewick                    |              72 |                    17.26 |                        4.65 |                40.77 |                   21.98 |                50.0
Spice Route Box               |              71 |                     7.54 |                        3.08 |                16.92 |                   14.07 |                33.8
Fennel & Flint                |              67 |                    15.35 |                        3.92 |                35.20 |                   14.66 |               38.81
Honeycomb Larder              |              58 |                    21.44 |                       16.00 |                32.80 |                   24.74 |               29.31
Ampfeld Elektronik            |              56 |                     8.71 |                        4.21 |                20.06 |                   13.84 |               32.14
Sourdough Club                |              47 |                    24.94 |                       14.52 |                32.65 |                   23.87 |               36.17
Ballycorran Industrial Supply |              44 |                     7.90 |                        5.40 |                24.50 |                   20.64 |               34.09
Cinder Peak Sports Co.        |              43 |                     5.56 |                        3.49 |                17.28 |                   11.82 |               34.88
Wiesgrund Wohnen              |              32 |                     3.47 |                        2.11 |                10.91 |                    6.34 |               21.88
Linden & Loom                 |              29 |                     4.69 |                        3.46 |                13.49 |                    9.71 |               37.93
Gratlinie Outdoor             |              27 |                     3.29 |                        2.52 |                10.84 |                    5.75 |               14.81
Oakhurst Tailoring            |              27 |                    27.17 |                       19.84 |                33.33 |                   31.07 |               11.11
Rue Lafayette Studio          |              27 |                     4.44 |                        2.79 |                12.21 |                    9.38 |               18.52
Coastline Interiors           |              21 |                    12.15 |                        9.08 |                16.90 |                   13.58 |                9.52
Thistlecombe                  |              21 |                     4.92 |                        4.24 |                17.93 |                   14.00 |                9.52
Kayak Corner                  |              20 |                    28.84 |                       17.73 |                37.59 |                   26.73 |                20.0
(22 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:
- q044Returns and refunds experience by merchant, 2025
- q046Shopper satisfaction (CSAT) by merchant, 2025
It builds on:
- q037Support response & resolution time trend, monthly

It becomes benchmark query q038

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

spellingfetch first
determinismtie-breaker: tenant names are not unique
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
numericsdocs/NUMERICS.md: EXTRACT(EPOCH FROM (b - a)) cast to NUMERIC(18,6) (4×)
placeholderpercentile cont (2×)
placeholderseconds between (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 (3×)
placeholderfetch first

The benchmark text, against the submitted SQL

SELECT   t_tenant.name AS merchant_name,  t_counts.tickets_created AS tickets_created,  ROUND(CAST(t_fr.avg_first_response_hours AS numeric), 2) AS avg_first_response_hours,  ROUND(CAST(t_fr.median_first_response_hours AS numeric), 2) AS median_first_response_hours,  ROUND(CAST(t_res.avg_resolution_hours AS numeric), 2) AS avg_resolution_hours,  ROUND(CAST(t_res.median_resolution_hours AS numeric), 2) AS median_resolution_hours,  ROUND(CAST(COALESCE(t_breach.breached_tickets, 0) AS numeric) / CAST(t_counts.tickets_created AS numeric) * 100.0, 2) AS sla_breach_rate_pct  t_fr.avg_first_response_hours AS avg_first_response_hours,  t_fr.median_first_response_hours AS median_first_response_hours,  t_res.avg_resolution_hours AS avg_resolution_hours,  t_res.median_resolution_hours AS median_resolution_hours,  CAST(COALESCE(t_breach.breached_tickets, 0) AS numeric(18,6)) / CAST(t_counts.tickets_created AS numeric(18,6)) * 100.0 AS sla_breach_rate_pctFROM (  SELECT t0.tenant_id AS tenant_id, COUNT(*) AS tickets_created  FROM tickets t0  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'  GROUP BY t0.tenant_id) t_countsJOIN tenants t_tenant  ON t_tenant.id = t_counts.tenant_idLEFT JOIN (  SELECT t0.tenant_id AS tenant_id,    AVG(EXTRACT(EPOCH FROM (t0.first_response_at - t0.created_at)) / 3600.0) AS avg_first_response_hours,    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (t0.first_response_at - t0.created_at)) / 3600.0) AS median_first_response_hours    AVG({{seconds_between(t0.created_at, t0.first_response_at)}} / 3600.0) AS avg_first_response_hours,    {{percentile_cont(0.5, {{seconds_between(t0.created_at, t0.first_response_at)}} / 3600.0)}} AS median_first_response_hours  FROM tickets t0  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'    AND t0.first_response_at IS NOT NULL  GROUP BY t0.tenant_id) t_fr  ON t_fr.tenant_id = t_counts.tenant_idLEFT JOIN (  SELECT t0.tenant_id AS tenant_id,    AVG(EXTRACT(EPOCH FROM (t0.resolved_at - t0.created_at)) / 3600.0) AS avg_resolution_hours,    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (t0.resolved_at - t0.created_at)) / 3600.0) AS median_resolution_hours    AVG({{seconds_between(t0.created_at, t0.resolved_at)}} / 3600.0) AS avg_resolution_hours,    {{percentile_cont(0.5, {{seconds_between(t0.created_at, t0.resolved_at)}} / 3600.0)}} AS median_resolution_hours  FROM tickets t0  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'    AND t0.resolved_at IS NOT NULL  GROUP BY t0.tenant_id) t_res  ON t_res.tenant_id = t_counts.tenant_idLEFT JOIN (  SELECT t0.tenant_id AS tenant_id, COUNT(DISTINCT t0.id) AS breached_tickets  FROM tickets t0  JOIN sla_breaches t1 ON t1.ticket_id = t0.id  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'  GROUP BY t0.tenant_id) t_breach  ON t_breach.tenant_id = t_counts.tenant_idWHERE t_counts.tickets_created >= 20ORDER BY t_counts.tickets_created DESC, t_tenant.name ASCLIMIT 50ORDER BY t_counts.tickets_created DESC NULLS FIRST, t_tenant.name ASC NULLS LAST, t_tenant.id NULLS LAST{{fetch_first(50)}}

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

SELECT 
  t_tenant.name AS merchant_name,
  t_counts.tickets_created AS tickets_created,
  t_fr.avg_first_response_hours AS avg_first_response_hours,
  t_fr.median_first_response_hours AS median_first_response_hours,
  t_res.avg_resolution_hours AS avg_resolution_hours,
  t_res.median_resolution_hours AS median_resolution_hours,
  CAST(COALESCE(t_breach.breached_tickets, 0) AS numeric(18,6)) / CAST(t_counts.tickets_created AS numeric(18,6)) * 100.0 AS sla_breach_rate_pct
FROM (
  SELECT t0.tenant_id AS tenant_id, COUNT(*) AS tickets_created
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
  GROUP BY t0.tenant_id
) t_counts
JOIN tenants t_tenant
  ON t_tenant.id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id,
    AVG(CAST(EXTRACT(EPOCH FROM (t0.first_response_at - t0.created_at)) AS NUMERIC(18,6)) / 3600.0) AS avg_first_response_hours,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(EXTRACT(EPOCH FROM (t0.first_response_at - t0.created_at)) AS NUMERIC(18,6)) / 3600.0) AS median_first_response_hours
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
    AND t0.first_response_at IS NOT NULL
  GROUP BY t0.tenant_id
) t_fr
  ON t_fr.tenant_id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id,
    AVG(CAST(EXTRACT(EPOCH FROM (t0.resolved_at - t0.created_at)) AS NUMERIC(18,6)) / 3600.0) AS avg_resolution_hours,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(EXTRACT(EPOCH FROM (t0.resolved_at - t0.created_at)) AS NUMERIC(18,6)) / 3600.0) AS median_resolution_hours
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
    AND t0.resolved_at IS NOT NULL
  GROUP BY t0.tenant_id
) t_res
  ON t_res.tenant_id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id, COUNT(DISTINCT t0.id) AS breached_tickets
  FROM tickets t0
  JOIN sla_breaches t1 ON t1.ticket_id = t0.id
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
  GROUP BY t0.tenant_id
) t_breach
  ON t_breach.tenant_id = t_counts.tenant_id
WHERE t_counts.tickets_created >= 20
ORDER BY t_counts.tickets_created DESC NULLS FIRST, t_tenant.name ASC NULLS LAST, t_tenant.id NULLS LAST
FETCH FIRST 50 ROWS ONLY

SELECT 
  t_tenant.name AS merchant_name,
  t_counts.tickets_created AS tickets_created,
  t_fr.avg_first_response_hours AS avg_first_response_hours,
  t_fr.median_first_response_hours AS median_first_response_hours,
  t_res.avg_resolution_hours AS avg_resolution_hours,
  t_res.median_resolution_hours AS median_resolution_hours,
  CAST(COALESCE(t_breach.breached_tickets, 0) AS numeric(18,6)) / CAST(t_counts.tickets_created AS numeric(18,6)) * 100.0 AS sla_breach_rate_pct
FROM (
  SELECT t0.tenant_id AS tenant_id, COUNT(*) AS tickets_created
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
  GROUP BY t0.tenant_id
) t_counts
JOIN tenants t_tenant
  ON t_tenant.id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id,
    AVG(CAST(EXTRACT(EPOCH FROM (t0.first_response_at - t0.created_at)) AS NUMERIC(18,6)) / 3600.0) AS avg_first_response_hours,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(EXTRACT(EPOCH FROM (t0.first_response_at - t0.created_at)) AS NUMERIC(18,6)) / 3600.0) AS median_first_response_hours
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
    AND t0.first_response_at IS NOT NULL
  GROUP BY t0.tenant_id
) t_fr
  ON t_fr.tenant_id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id,
    AVG(CAST(EXTRACT(EPOCH FROM (t0.resolved_at - t0.created_at)) AS NUMERIC(18,6)) / 3600.0) AS avg_resolution_hours,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(EXTRACT(EPOCH FROM (t0.resolved_at - t0.created_at)) AS NUMERIC(18,6)) / 3600.0) AS median_resolution_hours
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
    AND t0.resolved_at IS NOT NULL
  GROUP BY t0.tenant_id
) t_res
  ON t_res.tenant_id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id, COUNT(DISTINCT t0.id) AS breached_tickets
  FROM tickets t0
  JOIN sla_breaches t1 ON t1.ticket_id = t0.id
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
  GROUP BY t0.tenant_id
) t_breach
  ON t_breach.tenant_id = t_counts.tenant_id
WHERE t_counts.tickets_created >= 20
ORDER BY t_counts.tickets_created DESC NULLS FIRST, t_tenant.name ASC NULLS LAST, t_tenant.id NULLS LAST
LIMIT 50

SELECT 
  t_tenant.name AS merchant_name,
  t_counts.tickets_created AS tickets_created,
  t_fr.avg_first_response_hours AS avg_first_response_hours,
  t_fr.median_first_response_hours AS median_first_response_hours,
  t_res.avg_resolution_hours AS avg_resolution_hours,
  t_res.median_resolution_hours AS median_resolution_hours,
  CAST(COALESCE(t_breach.breached_tickets, 0) AS numeric(18,6)) / CAST(t_counts.tickets_created AS numeric(18,6)) * 100.0 AS sla_breach_rate_pct
FROM (
  SELECT t0.tenant_id AS tenant_id, COUNT(*) AS tickets_created
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
  GROUP BY t0.tenant_id
) t_counts
JOIN tenants t_tenant
  ON t_tenant.id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id,
    AVG(CAST(EXTRACT(EPOCH FROM (t0.first_response_at - t0.created_at)) AS NUMERIC(18,6)) / 3600.0) AS avg_first_response_hours,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(CAST(EXTRACT(EPOCH FROM (t0.first_response_at - t0.created_at)) AS NUMERIC(18,6)) / 3600.0 AS DOUBLE PRECISION)) AS median_first_response_hours
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
    AND t0.first_response_at IS NOT NULL
  GROUP BY t0.tenant_id
) t_fr
  ON t_fr.tenant_id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id,
    AVG(CAST(EXTRACT(EPOCH FROM (t0.resolved_at - t0.created_at)) AS NUMERIC(18,6)) / 3600.0) AS avg_resolution_hours,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(CAST(EXTRACT(EPOCH FROM (t0.resolved_at - t0.created_at)) AS NUMERIC(18,6)) / 3600.0 AS DOUBLE PRECISION)) AS median_resolution_hours
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
    AND t0.resolved_at IS NOT NULL
  GROUP BY t0.tenant_id
) t_res
  ON t_res.tenant_id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id, COUNT(DISTINCT t0.id) AS breached_tickets
  FROM tickets t0
  JOIN sla_breaches t1 ON t1.ticket_id = t0.id
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
  GROUP BY t0.tenant_id
) t_breach
  ON t_breach.tenant_id = t_counts.tenant_id
WHERE t_counts.tickets_created >= 20
ORDER BY t_counts.tickets_created DESC NULLS FIRST, t_tenant.name ASC NULLS LAST, t_tenant.id NULLS LAST
FETCH FIRST 50 ROWS ONLY

SELECT 
  t_tenant.name AS merchant_name,
  t_counts.tickets_created AS tickets_created,
  t_fr.avg_first_response_hours AS avg_first_response_hours,
  t_fr.median_first_response_hours AS median_first_response_hours,
  t_res.avg_resolution_hours AS avg_resolution_hours,
  t_res.median_resolution_hours AS median_resolution_hours,
  CAST(COALESCE(t_breach.breached_tickets, 0) AS numeric(18,6)) / CAST(t_counts.tickets_created AS numeric(18,6)) * 100.0 AS sla_breach_rate_pct
FROM (
  SELECT t0.tenant_id AS tenant_id, COUNT(*) AS tickets_created
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
  GROUP BY t0.tenant_id
) t_counts
JOIN tenants t_tenant
  ON t_tenant.id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id,
    AVG(CAST(divideDecimal(toDecimal128(dateDiff('microsecond', t0.created_at, t0.first_response_at), 0), toDecimal128(1000000, 0), 6) AS Nullable(Decimal(18, 6))) / 3600.0) AS avg_first_response_hours,
    quantileExactInclusive(0.5)(toFloat64(CAST(divideDecimal(toDecimal128(dateDiff('microsecond', t0.created_at, t0.first_response_at), 0), toDecimal128(1000000, 0), 6) AS Nullable(Decimal(18, 6))) / 3600.0)) AS median_first_response_hours
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
    AND t0.first_response_at IS NOT NULL
  GROUP BY t0.tenant_id
) t_fr
  ON t_fr.tenant_id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id,
    AVG(CAST(divideDecimal(toDecimal128(dateDiff('microsecond', t0.created_at, t0.resolved_at), 0), toDecimal128(1000000, 0), 6) AS Nullable(Decimal(18, 6))) / 3600.0) AS avg_resolution_hours,
    quantileExactInclusive(0.5)(toFloat64(CAST(divideDecimal(toDecimal128(dateDiff('microsecond', t0.created_at, t0.resolved_at), 0), toDecimal128(1000000, 0), 6) AS Nullable(Decimal(18, 6))) / 3600.0)) AS median_resolution_hours
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
    AND t0.resolved_at IS NOT NULL
  GROUP BY t0.tenant_id
) t_res
  ON t_res.tenant_id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id, COUNT(DISTINCT t0.id) AS breached_tickets
  FROM tickets t0
  JOIN sla_breaches t1 ON t1.ticket_id = t0.id
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
  GROUP BY t0.tenant_id
) t_breach
  ON t_breach.tenant_id = t_counts.tenant_id
WHERE t_counts.tickets_created >= 20
ORDER BY t_counts.tickets_created DESC NULLS FIRST, t_tenant.name ASC NULLS LAST, t_tenant.id NULLS LAST
FETCH FIRST 50 ROWS ONLY

Scale factor 1000, c8a.24xlarge, after the last load. q038 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

Bea's whole session, 6 turns

The request, as Bea received it

Report request from Mateo Ruiz (analyst for the VP Customer Experience):

Title: Response & resolution times by merchant, 2025
Question: How much does support speed vary across merchants, and who is fastest/slowest?
Grain: one row per tenant (merchant), for tickets created in 2025
Columns:
- merchant name
- tickets created
- avg first response hours
- median first response hours
- avg resolution hours
- median resolution hours
- SLA breach rate (%)
Details: Source: tickets joined to tenants (for name) and sla_breaches (for breach rate). Filter tickets.created_at in 2025-01-01 through 2025-12-31. Same hour definitions as the monthly trend report (first_response_at - created_at, resolved_at - created_at, both in hours, only over tickets where that timestamp is not null). SLA breach rate = tenant's distinct tickets with >=1 sla_breaches row / tenant's tickets created in 2025, as a percentage. Only include tenants with at least 20 tickets created in 2025 to avoid noisy small-sample rows. Order by tickets created descending, tie-break by merchant name ascending; report will show at most 50 rows so note total tenant count meeting the threshold and how many are printed.

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

Bea calls dry_run

SELECT 
  t_tenant.name AS merchant_name,
  t_counts.tickets_created AS tickets_created,
  ROUND(CAST(t_fr.avg_first_response_hours AS numeric), 2) AS avg_first_response_hours,
  ROUND(CAST(t_fr.median_first_response_hours AS numeric), 2) AS median_first_response_hours,
  ROUND(CAST(t_res.avg_resolution_hours AS numeric), 2) AS avg_resolution_hours,
  ROUND(CAST(t_res.median_resolution_hours AS numeric), 2) AS median_resolution_hours,
  ROUND(CAST(COALESCE(t_breach.breached_tickets, 0) AS numeric) / CAST(t_counts.tickets_created AS numeric) * 100.0, 2) AS sla_breach_rate_pct
FROM (
  SELECT t0.tenant_id AS tenant_id, COUNT(*) AS tickets_created
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
  GROUP BY t0.tenant_id
) t_counts
JOIN tenants t_tenant
  ON t_tenant.id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id,
    AVG(EXTRACT(EPOCH FROM (t0.first_response_at - t0.created_at)) / 3600.0) AS avg_first_response_hours,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (t0.first_response_at - t0.created_at)) / 3600.0) AS median_first_response_hours
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
    AND t0.first_response_at IS NOT NULL
  GROUP BY t0.tenant_id
) t_fr
  ON t_fr.tenant_id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id,
    AVG(EXTRACT(EPOCH FROM (t0.resolved_at - t0.created_at)) / 3600.0) AS avg_resolution_hours,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (t0.resolved_at - t0.created_at)) / 3600.0) AS median_resolution_hours
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
    AND t0.resolved_at IS NOT NULL
  GROUP BY t0.tenant_id
) t_res
  ON t_res.tenant_id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id, COUNT(DISTINCT t0.id) AS breached_tickets
  FROM tickets t0
  JOIN sla_breaches t1 ON t1.ticket_id = t0.id
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
  GROUP BY t0.tenant_id
) t_breach
  ON t_breach.tenant_id = t_counts.tenant_id
WHERE t_counts.tickets_created >= 20
ORDER BY t_counts.tickets_created DESC, t_tenant.name ASC
LIMIT 50

The warehouse answers

STATUS: COMPILES (dry run on the empty schema, no data read)
COLUMNS: merchant_name, tickets_created, avg_first_response_hours, median_first_response_hours, avg_resolution_hours, median_resolution_hours, sla_breach_rate_pct

Bea calls submit

SELECT 
  t_tenant.name AS merchant_name,
  t_counts.tickets_created AS tickets_created,
  ROUND(CAST(t_fr.avg_first_response_hours AS numeric), 2) AS avg_first_response_hours,
  ROUND(CAST(t_fr.median_first_response_hours AS numeric), 2) AS median_first_response_hours,
  ROUND(CAST(t_res.avg_resolution_hours AS numeric), 2) AS avg_resolution_hours,
  ROUND(CAST(t_res.median_resolution_hours AS numeric), 2) AS median_resolution_hours,
  ROUND(CAST(COALESCE(t_breach.breached_tickets, 0) AS numeric) / CAST(t_counts.tickets_created AS numeric) * 100.0, 2) AS sla_breach_rate_pct
FROM (
  SELECT t0.tenant_id AS tenant_id, COUNT(*) AS tickets_created
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
  GROUP BY t0.tenant_id
) t_counts
JOIN tenants t_tenant
  ON t_tenant.id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id,
    AVG(EXTRACT(EPOCH FROM (t0.first_response_at - t0.created_at)) / 3600.0) AS avg_first_response_hours,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (t0.first_response_at - t0.created_at)) / 3600.0) AS median_first_response_hours
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
    AND t0.first_response_at IS NOT NULL
  GROUP BY t0.tenant_id
) t_fr
  ON t_fr.tenant_id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id,
    AVG(EXTRACT(EPOCH FROM (t0.resolved_at - t0.created_at)) / 3600.0) AS avg_resolution_hours,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (t0.resolved_at - t0.created_at)) / 3600.0) AS median_resolution_hours
  FROM tickets t0
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
    AND t0.resolved_at IS NOT NULL
  GROUP BY t0.tenant_id
) t_res
  ON t_res.tenant_id = t_counts.tenant_id
LEFT JOIN (
  SELECT t0.tenant_id AS tenant_id, COUNT(DISTINCT t0.id) AS breached_tickets
  FROM tickets t0
  JOIN sla_breaches t1 ON t1.ticket_id = t0.id
  WHERE t0.created_at >= DATE '2025-01-01' AND t0.created_at < DATE '2026-01-01'
  GROUP BY t0.tenant_id
) t_breach
  ON t_breach.tenant_id = t_counts.tenant_id
WHERE t_counts.tickets_created >= 20
ORDER BY t_counts.tickets_created DESC, t_tenant.name ASC
LIMIT 50

The warehouse answers

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

BeaSubmitted. The query answers the request at the requested grain (one row per tenant, 2025 tickets, ≥20-ticket threshold), with avg/median first-response and resolution hours and SLA breach rate, ordered by ticket volume descending and capped at 50 rows per house standards.

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 (270 KB) · Mateo Ruiz's transcript (892 KB) · Daniel Achterberg's transcript (264 KB) · the night's plan (5 KB) · the night's reports (15 KB) · the week's memo (3 KB)

Response & resolution times by merchant, 2025

Daniel Achterberg writes to Mateo Ruiz

Mateo Ruiz plans the night

Bea writes the SQL

The report comes back overnight

What came of it

It becomes benchmark query q038

Everything, as recorded