Queries · q033 · Chief Operating Officer · week 1, night 3

Carrier mix for best vs worst performing merchants, 2025

“Do the merchants with the worst delivery reliability simply ship disproportionately with the weaker carriers?”

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

1. Priya Raman writes to Lena Hartmann

   The manager, at the start of week 1

   Subject: Getting my bearings on fulfillment — questions for this week

   Hi Lena,

   Thanks for taking these on. I'm new here and coming from the logistics side, so my first priority is understanding whether the delivery promise we make to shoppers actually holds up — that's the lens for most of what's below. Given the slot budget, here's what matters most, roughly in priority order:

   1. How long does it actually take us to get an order into a customer's hands, and how much does that vary? I want to understand the real end-to-end time from order placed to delivered, and whether that's gotten better, worse, or stayed flat over the past year or so. I'm especially interested in how consistent we are — are there merchants or categories where delivery is reliably fast, and others where it's a coin toss?
   2. How did we do during the last peak/busy season? I'd like to know whether fulfillment speed and reliability held up when volume spiked, or whether we saw things slip — delays, backlogs, stockouts — and how quickly we recovered afterward. This matters because peak is the real stress test of the whole operation.
   3. Where are the weak links — specific carriers, warehouses, or regions? I want to know if there are particular carriers or fulfillment sites that are consistently underperforming on delivery time or dependability, so we know where to focus attention or have a harder conversation with a partner.
   4. How well do we manage stock so we're not promising things we can't deliver? I'm curious about how often we run into stockouts or have to delay orders for lack of inventory, and whether our replenishment from suppliers (purchase orders) is keeping pace — are we chronically waiting too long on restocks?
   5. (If there's room) What does the returns process look like from a logistics standpoint? Specifically, how long it takes for returned goods to get back into our system and become sellable again — that's a cost and customer-experience lever I want to keep an eye on.

   What I'll use this for: I'm forming my first impressions of operational health in this area and want to walk into conversations with merchants and carrier partners armed with facts, not assumptions. I'd rather have a smaller number of really clear answers than a lot of surface-level ones — so if you need to cut scope to fit your slots this week, prioritize #1 and #2, and we can pick up the rest next week.

   Thanks — looking forward to Friday's memo.

   Priya

   Show the whole note

2. Lena Hartmann plans the night

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

   Night 2 findings, read critically: (1) Report 1 only printed 50 of 56 carrier-quarter rows -- it's missing the last few carriers' Q3/Q4 figures, so I can't yet see the full carrier picture for peak; I'm fixing that tonight by re-running Q4 only (one quarter -> all ~14 carriers fit on one page) and adding a volume-share column so I can judge materiality, not just rates. (2) Within what I could see, two carriers (Landfall Freight, Lowlands Express) are chronically mediocre all year -- not a peak story, a structural one. But Albion Parcel is the standout: rock-solid in Q1-Q3 (88-97% within 3 days) then collapses in Q4 (avg transit 6.54 days, only 50.7% within 3 days) -- a sharp, carrier-specific peak failure that deserves its own drill-down by month to see exactly when it broke and whether it recovered by year-end. (3) Report 3 (stage breakdown) is a clean, important result: warehouse processing time stayed flat (~1.1-1.2 days) straight through peak even as volume nearly doubled, while carrier transit time is what stretched (2.5-2.9 days baseline -> 3.46 in Nov) -- so the peak slowdown from Night 1 is a carrier/transit problem, not a warehouse-capacity problem. That reframes priority #3: warehouses look fine; carriers are the lever. (4) The warehouse report's worst performers (Thistlecombe, Purrfect Pantry, Chocolaterie Mireille, Kayak Corner, etc.) all have unremarkable processing times, so their poor delivery reliability must come from somewhere in the transit leg -- I want to test whether they're simply the merchants who happen to ship with the weak carriers. Tonight: complete the Q4 carrier picture, isolate Albion Parcel's break, and test the carrier-mix hypothesis for the best/worst merchants.

   Read the whole plan
   Carrier mix for best vs worst performing merchants, 2025

   Question

   Do the merchants with the worst delivery reliability simply ship disproportionately with the weaker carriers?

   One row per

   tenant and carrier combination, for a fixed list of 20 named tenants

   Columns

   • tenant_name
   • performance_group ('best' or 'worst', per the fixed list below)
   • carrier
   • shipments_count (2025)
   • share_of_tenant_shipments_pct (this carrier's shipments / tenant's total shipments in 2025, x100)
   • avg_transit_days (this tenant+carrier combination, 2025)
   • pct_within_3_days

3. 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 selected_tenants AS (
     SELECT
       t0.id AS tenant_id,
       t0.name AS tenant_name,
       CASE
         WHEN t0.name IN ('Thistlecombe','Purrfect Pantry','Chocolaterie Mireille','Kayak Corner','Studio Vrij','Sourdough Club','Grind Collective','Spice Route Box','Kettle & Key','Polder Packaging') THEN 'worst'
         WHEN t0.name IN ('Pfotenglück','Cragsley Outfitters','Brennquell Kaffee','Feather & Fin','Ridgeback Gear','Wohnglück','Mirelle','Honeycomb Larder','Kontor24 Bürobedarf','Tail Wag Supply') THEN 'best'
       END AS performance_group
     FROM tenants t0
     WHERE t0.name IN (
       'Thistlecombe','Purrfect Pantry','Chocolaterie Mireille','Kayak Corner','Studio Vrij','Sourdough Club','Grind Collective','Spice Route Box','Kettle & Key','Polder Packaging',
       'Pfotenglück','Cragsley Outfitters','Brennquell Kaffee','Feather & Fin','Ridgeback Gear','Wohnglück','Mirelle','Honeycomb Larder','Kontor24 Bürobedarf','Tail Wag Supply'
     )
   ),
   shipment_base AS (
     SELECT
       s1.tenant_id,
       s1.carrier,
       s1.shipped_at,
       s1.delivered_at
     FROM shipments s1
     JOIN fulfillments f1 ON f1.id = s1.fulfillment_id
     JOIN orders o1 ON o1.id = f1.order_id
     WHERE s1.shipped_at >= TIMESTAMP '2025-01-01'
       AND s1.shipped_at < TIMESTAMP '2026-01-01'
       AND s1.tenant_id IN (SELECT st0.tenant_id FROM selected_tenants st0)
   ),
   tenant_carrier_counts AS (
     SELECT
       sb0.tenant_id,
       sb0.carrier,
       COUNT(*) AS shipments_count
     FROM shipment_base sb0
     GROUP BY sb0.tenant_id, sb0.carrier
   ),
   tenant_totals AS (
     SELECT
       sb1.tenant_id,
       COUNT(*) AS total_shipments
     FROM shipment_base sb1
     GROUP BY sb1.tenant_id
   ),
   transit_stats AS (
     SELECT
       sb2.tenant_id,
       sb2.carrier,
       AVG(CASE WHEN sb2.delivered_at IS NOT NULL THEN DATE_PART('day', sb2.delivered_at - sb2.shipped_at) END) AS avg_transit_days,
       100.0 * SUM(CASE WHEN sb2.delivered_at IS NOT NULL AND DATE_PART('day', sb2.delivered_at - sb2.shipped_at) <= 3 THEN 1 ELSE 0 END)
         / NULLIF(SUM(CASE WHEN sb2.delivered_at IS NOT NULL THEN 1 ELSE 0 END), 0) AS pct_within_3_days
     FROM shipment_base sb2
     GROUP BY sb2.tenant_id, sb2.carrier
   )
   SELECT
     st.tenant_name AS tenant_name,
     st.performance_group AS performance_group,
     tcc.carrier AS carrier,
     tcc.shipments_count AS shipments_count,
     ROUND(100.0 * tcc.shipments_count / tt.total_shipments, 1) AS share_of_tenant_shipments_pct,
     ROUND(COALESCE(ts.avg_transit_days, 0), 2) AS avg_transit_days,
     ROUND(COALESCE(ts.pct_within_3_days, 0), 1) AS pct_within_3_days
   FROM tenant_carrier_counts tcc
   JOIN selected_tenants st ON st.tenant_id = tcc.tenant_id
   JOIN tenant_totals tt ON tt.tenant_id = tcc.tenant_id
   LEFT JOIN transit_stats ts ON ts.tenant_id = tcc.tenant_id AND ts.carrier = tcc.carrier
   ORDER BY
     CASE st.performance_group WHEN 'worst' THEN 0 ELSE 1 END,
     st.tenant_name ASC,
     tcc.shipments_count DESC,
     tcc.carrier ASC

   “Query compiles and produces the expected columns. Submitting.”

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

   “Submitted. Summary of what the report does: - Grain: one row per tenant × carrier, restricted to the 20 named tenants. - Scope: shipments joined through fulfillments → orders, filtered to shipped_at in 2025. - Metrics: shipment count, each carrier's share of that tenant's 2025 shipments, average transit days, and % delivered within 3 days — the latter two computed over the delivered subset only. - Ordering: worst group first, then best, then tenant name, then shipment count descending (carrier as final tiebreak for determinism). One deviation noted to the analyst: transit-day metrics are based only on shipments that actually have a delivered_at timestamp (as the spec implied), and tenant/carrier combos with no delivered shipments show 0 instead of null.”

4. The report comes back overnight

   As Lena Hartmann saw it, from the small dataset the agents worked on

   tenant_name           | performance_group | carrier              | shipments_count | share_of_tenant_shipments_pct | avg_transit_days | pct_within_3_days
   ----------------------+-------------------+----------------------+-----------------+-------------------------------+------------------+------------------
   Chocolaterie Mireille | worst             | Landfall Freight     |              38 |                          38.0 |             3.86 |              43.2
   Chocolaterie Mireille | worst             | Lowlands Express     |              32 |                          32.0 |              3.0 |              59.4
   Chocolaterie Mireille | worst             | Gallia Colis         |              30 |                          30.0 |             2.63 |              76.7
   Grind Collective      | worst             | Albion Parcel        |             515 |                          47.1 |             3.12 |              80.7
   Grind Collective      | worst             | Kennet Freight       |             222 |                          20.3 |             1.97 |              88.7
   Grind Collective      | worst             | Regent Parcels       |             187 |                          17.1 |             1.91 |              94.0
   Grind Collective      | worst             | Landfall Freight     |             109 |                          10.0 |             6.27 |              19.4
   Grind Collective      | worst             | Lowlands Express     |              61 |                           5.6 |             6.98 |              11.7
   Kayak Corner          | worst             | Albion Parcel        |              38 |                          37.6 |             4.45 |              84.2
   Kayak Corner          | worst             | Kennet Freight       |              21 |                          20.8 |             2.05 |              81.0
   Kayak Corner          | worst             | Landfall Freight     |              19 |                          18.8 |             3.42 |              42.1
   Kayak Corner          | worst             | Regent Parcels       |              13 |                          12.9 |             1.38 |             100.0
   Kayak Corner          | worst             | Lowlands Express     |              10 |                           9.9 |              3.9 |              50.0
   Kettle & Key          | worst             | Polder Post          |              26 |                          44.1 |             2.04 |              92.0
   Kettle & Key          | worst             | Landfall Freight     |              18 |                          30.5 |             4.22 |              27.8
   Kettle & Key          | worst             | Lowlands Express     |              15 |                          25.4 |              2.4 |              80.0
   Polder Packaging      | worst             | Lowlands Express     |              27 |                          49.1 |             2.85 |              74.1
   Polder Packaging      | worst             | Polder Post          |              18 |                          32.7 |             2.39 |              72.2
   Polder Packaging      | worst             | Landfall Freight     |              10 |                          18.2 |              4.0 |              50.0
   Purrfect Pantry       | worst             | Skerrylane Logistics |              46 |                          62.2 |             2.07 |              87.0
   Purrfect Pantry       | worst             | Lowlands Express     |              17 |                          23.0 |             8.12 |               0.0
   Purrfect Pantry       | worst             | Landfall Freight     |              11 |                          14.9 |             9.27 |               0.0
   Sourdough Club        | worst             | Liffey Couriers      |             142 |                          46.4 |              2.2 |              87.3
   Sourdough Club        | worst             | Albion Parcel        |              76 |                          24.8 |              3.0 |              85.1
   Sourdough Club        | worst             | Landfall Freight     |              52 |                          17.0 |             4.84 |              35.3
   Sourdough Club        | worst             | Lowlands Express     |              36 |                          11.8 |             4.91 |              28.6
   Spice Route Box       | worst             | Lowlands Express     |             360 |                          47.2 |             2.11 |              83.2
   Spice Route Box       | worst             | Polder Post          |             311 |                          40.8 |             2.21 |              83.5
   Spice Route Box       | worst             | Landfall Freight     |              92 |                          12.1 |             3.85 |              42.4
   Studio Vrij           | worst             | Lowlands Express     |             347 |                          58.1 |             2.04 |              87.8
   Studio Vrij           | worst             | Polder Post          |             188 |                          31.5 |             2.21 |              83.2
   Studio Vrij           | worst             | Landfall Freight     |              62 |                          10.4 |             3.82 |              33.3
   Thistlecombe          | worst             | Albion Parcel        |              29 |                          33.3 |              5.0 |              65.5
   Thistlecombe          | worst             | Kennet Freight       |              22 |                          25.3 |             2.48 |              76.2
   Thistlecombe          | worst             | Landfall Freight     |              20 |                          23.0 |              6.0 |              15.0
   Thistlecombe          | worst             | Regent Parcels       |              10 |                          11.5 |              2.2 |              80.0
   Thistlecombe          | worst             | Lowlands Express     |               6 |                           6.9 |             6.83 |              33.3
   Brennquell Kaffee     | best              | Strombogen Kurier    |            2050 |                          49.7 |             1.73 |              96.7
   Brennquell Kaffee     | best              | Alpenpaket           |            1368 |                          33.1 |             1.72 |              96.3
   Brennquell Kaffee     | best              | Landfall Freight     |             409 |                           9.9 |             3.76 |              42.7
   Brennquell Kaffee     | best              | Lowlands Express     |             300 |                           7.3 |             3.89 |              40.1
   Cragsley Outfitters   | best              | Liberty Ground       |            1634 |                          46.9 |             1.71 |              95.4
   Cragsley Outfitters   | best              | Trailhead Express    |            1253 |                          36.0 |             1.92 |              89.7
   Cragsley Outfitters   | best              | Prairie Line Express |             511 |                          14.7 |              2.0 |              88.8
   Cragsley Outfitters   | best              | Landfall Freight     |              85 |                           2.4 |             3.71 |              44.6
   Feather & Fin         | best              | Liberty Ground       |             108 |                          50.0 |             1.81 |              94.4
   Feather & Fin         | best              | Trailhead Express    |              79 |                          36.6 |             1.64 |              94.9
   Feather & Fin         | best              | Prairie Line Express |              29 |                          13.4 |             2.14 |              89.3
   Honeycomb Larder      | best              | Liberty Ground       |             248 |                          41.1 |             1.69 |              95.9
   Honeycomb Larder      | best              | Trailhead Express    |             246 |                          40.7 |              2.0 |              88.0
   (73 rows, first 50 printed)

5. What came of it

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

   It builds on:

   • q029Fulfillment performance by warehouse, 2025
   • q026Delivery time by merchant, 2025

6. It becomes benchmark query q033

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

   • editThe day field of the difference of two timestamps: the standard needs an interval qualifier that none of the reference dialects supports, so a placeholder with the original as default. (docs/RUNNER.md, Dialects)
   • determinismtie-breaker: tenant names are not unique; (tenant_id, carrier) is the row key
   • numericsdocs/NUMERICS.md: rounding removed; results keep full precision
   • made explicita selected t.col gets AS col, its standard name made explicit (10×)
   • determinismthe standard leaves the place of NULLs in a sort to the implementation: NULLS LAST ascending, NULLS FIRST descending, as in the reference results (5×)
   The benchmark text, against the submitted SQL

   WITH selected_tenants AS (  SELECT    t0.id AS tenant_id,    t0.name AS tenant_name,    CASE      WHEN t0.name IN ('Thistlecombe','Purrfect Pantry','Chocolaterie Mireille','Kayak Corner','Studio Vrij','Sourdough Club','Grind Collective','Spice Route Box','Kettle & Key','Polder Packaging') THEN 'worst'      WHEN t0.name IN ('Pfotenglück','Cragsley Outfitters','Brennquell Kaffee','Feather & Fin','Ridgeback Gear','Wohnglück','Mirelle','Honeycomb Larder','Kontor24 Bürobedarf','Tail Wag Supply') THEN 'best'    END AS performance_group  FROM tenants t0  WHERE t0.name IN (    'Thistlecombe','Purrfect Pantry','Chocolaterie Mireille','Kayak Corner','Studio Vrij','Sourdough Club','Grind Collective','Spice Route Box','Kettle & Key','Polder Packaging',    'Pfotenglück','Cragsley Outfitters','Brennquell Kaffee','Feather & Fin','Ridgeback Gear','Wohnglück','Mirelle','Honeycomb Larder','Kontor24 Bürobedarf','Tail Wag Supply'  )),shipment_base AS (  SELECT    s1.tenant_id,    s1.carrier,    s1.shipped_at,    s1.delivered_at    s1.tenant_id AS tenant_id,    s1.carrier AS carrier,    s1.shipped_at AS shipped_at,    s1.delivered_at AS delivered_at  FROM shipments s1  JOIN fulfillments f1 ON f1.id = s1.fulfillment_id  JOIN orders o1 ON o1.id = f1.order_id  WHERE s1.shipped_at >= TIMESTAMP '2025-01-01'    AND s1.shipped_at < TIMESTAMP '2026-01-01'    AND s1.tenant_id IN (SELECT st0.tenant_id FROM selected_tenants st0)    AND s1.tenant_id IN (SELECT st0.tenant_id AS tenant_id FROM selected_tenants st0)),tenant_carrier_counts AS (  SELECT    sb0.tenant_id,    sb0.carrier,    sb0.tenant_id AS tenant_id,    sb0.carrier AS carrier,    COUNT(*) AS shipments_count  FROM shipment_base sb0  GROUP BY sb0.tenant_id, sb0.carrier),tenant_totals AS (  SELECT    sb1.tenant_id,    sb1.tenant_id AS tenant_id,    COUNT(*) AS total_shipments  FROM shipment_base sb1  GROUP BY sb1.tenant_id),transit_stats AS (  SELECT    sb2.tenant_id,    sb2.carrier,    AVG(CASE WHEN sb2.delivered_at IS NOT NULL THEN DATE_PART('day', sb2.delivered_at - sb2.shipped_at) END) AS avg_transit_days,    100.0 * SUM(CASE WHEN sb2.delivered_at IS NOT NULL AND DATE_PART('day', sb2.delivered_at - sb2.shipped_at) <= 3 THEN 1 ELSE 0 END)    sb2.tenant_id AS tenant_id,    sb2.carrier AS carrier,    AVG(CASE WHEN sb2.delivered_at IS NOT NULL THEN {{whole_days_between(sb2.shipped_at, sb2.delivered_at)}} END) AS avg_transit_days,    100.0 * SUM(CASE WHEN sb2.delivered_at IS NOT NULL AND {{whole_days_between(sb2.shipped_at, sb2.delivered_at)}} <= 3 THEN 1 ELSE 0 END)      / NULLIF(SUM(CASE WHEN sb2.delivered_at IS NOT NULL THEN 1 ELSE 0 END), 0) AS pct_within_3_days  FROM shipment_base sb2  GROUP BY sb2.tenant_id, sb2.carrier)SELECT  st.tenant_name AS tenant_name,  st.performance_group AS performance_group,  tcc.carrier AS carrier,  tcc.shipments_count AS shipments_count,  ROUND(100.0 * tcc.shipments_count / tt.total_shipments, 1) AS share_of_tenant_shipments_pct,  ROUND(COALESCE(ts.avg_transit_days, 0), 2) AS avg_transit_days,  ROUND(COALESCE(ts.pct_within_3_days, 0), 1) AS pct_within_3_days  100.0 * tcc.shipments_count / tt.total_shipments AS share_of_tenant_shipments_pct,  COALESCE(ts.avg_transit_days, 0) AS avg_transit_days,  COALESCE(ts.pct_within_3_days, 0) AS pct_within_3_daysFROM tenant_carrier_counts tccJOIN selected_tenants st ON st.tenant_id = tcc.tenant_idJOIN tenant_totals tt ON tt.tenant_id = tcc.tenant_idLEFT JOIN transit_stats ts ON ts.tenant_id = tcc.tenant_id AND ts.carrier = tcc.carrierORDER BY  CASE st.performance_group WHEN 'worst' THEN 0 ELSE 1 END,  st.tenant_name ASC,  tcc.shipments_count DESC,  tcc.carrier ASC  CASE st.performance_group WHEN 'worst' THEN 0 ELSE 1 END NULLS LAST,  st.tenant_name ASC NULLS LAST,  tcc.shipments_count DESC NULLS FIRST,  tcc.carrier ASC NULLS LAST,  tcc.tenant_id 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_brewClickHouse · chDB

   WITH selected_tenants AS (
     SELECT
       t0.id AS tenant_id,
       t0.name AS tenant_name,
       CASE
         WHEN t0.name IN ('Thistlecombe','Purrfect Pantry','Chocolaterie Mireille','Kayak Corner','Studio Vrij','Sourdough Club','Grind Collective','Spice Route Box','Kettle & Key','Polder Packaging') THEN 'worst'
         WHEN t0.name IN ('Pfotenglück','Cragsley Outfitters','Brennquell Kaffee','Feather & Fin','Ridgeback Gear','Wohnglück','Mirelle','Honeycomb Larder','Kontor24 Bürobedarf','Tail Wag Supply') THEN 'best'
       END AS performance_group
     FROM tenants t0
     WHERE t0.name IN (
       'Thistlecombe','Purrfect Pantry','Chocolaterie Mireille','Kayak Corner','Studio Vrij','Sourdough Club','Grind Collective','Spice Route Box','Kettle & Key','Polder Packaging',
       'Pfotenglück','Cragsley Outfitters','Brennquell Kaffee','Feather & Fin','Ridgeback Gear','Wohnglück','Mirelle','Honeycomb Larder','Kontor24 Bürobedarf','Tail Wag Supply'
     )
   ),
   shipment_base AS (
     SELECT
       s1.tenant_id AS tenant_id,
       s1.carrier AS carrier,
       s1.shipped_at AS shipped_at,
       s1.delivered_at AS delivered_at
     FROM shipments s1
     JOIN fulfillments f1 ON f1.id = s1.fulfillment_id
     JOIN orders o1 ON o1.id = f1.order_id
     WHERE s1.shipped_at >= TIMESTAMP '2025-01-01'
       AND s1.shipped_at < TIMESTAMP '2026-01-01'
       AND s1.tenant_id IN (SELECT st0.tenant_id AS tenant_id FROM selected_tenants st0)
   ),
   tenant_carrier_counts AS (
     SELECT
       sb0.tenant_id AS tenant_id,
       sb0.carrier AS carrier,
       COUNT(*) AS shipments_count
     FROM shipment_base sb0
     GROUP BY sb0.tenant_id, sb0.carrier
   ),
   tenant_totals AS (
     SELECT
       sb1.tenant_id AS tenant_id,
       COUNT(*) AS total_shipments
     FROM shipment_base sb1
     GROUP BY sb1.tenant_id
   ),
   transit_stats AS (
     SELECT
       sb2.tenant_id AS tenant_id,
       sb2.carrier AS carrier,
       AVG(CASE WHEN sb2.delivered_at IS NOT NULL THEN EXTRACT(DAY FROM sb2.delivered_at - sb2.shipped_at) END) AS avg_transit_days,
       100.0 * SUM(CASE WHEN sb2.delivered_at IS NOT NULL AND EXTRACT(DAY FROM sb2.delivered_at - sb2.shipped_at) <= 3 THEN 1 ELSE 0 END)
         / NULLIF(SUM(CASE WHEN sb2.delivered_at IS NOT NULL THEN 1 ELSE 0 END), 0) AS pct_within_3_days
     FROM shipment_base sb2
     GROUP BY sb2.tenant_id, sb2.carrier
   )
   SELECT
     st.tenant_name AS tenant_name,
     st.performance_group AS performance_group,
     tcc.carrier AS carrier,
     tcc.shipments_count AS shipments_count,
     100.0 * tcc.shipments_count / tt.total_shipments AS share_of_tenant_shipments_pct,
     COALESCE(ts.avg_transit_days, 0) AS avg_transit_days,
     COALESCE(ts.pct_within_3_days, 0) AS pct_within_3_days
   FROM tenant_carrier_counts tcc
   JOIN selected_tenants st ON st.tenant_id = tcc.tenant_id
   JOIN tenant_totals tt ON tt.tenant_id = tcc.tenant_id
   LEFT JOIN transit_stats ts ON ts.tenant_id = tcc.tenant_id AND ts.carrier = tcc.carrier
   ORDER BY
     CASE st.performance_group WHEN 'worst' THEN 0 ELSE 1 END NULLS LAST,
     st.tenant_name ASC NULLS LAST,
     tcc.shipments_count DESC NULLS FIRST,
     tcc.carrier ASC NULLS LAST,
     tcc.tenant_id ASC NULLS LAST

   Scale factor 1000, c8a.24xlarge, after the last load. q033 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

   Bea's whole session, 7 turns

   • The request, as Bea received it

     Report request from Lena Hartmann (analyst for the Chief Operating Officer):
     
     Title: Carrier mix for best vs worst performing merchants, 2025
     Question: Do the merchants with the worst delivery reliability simply ship disproportionately with the weaker carriers?
     Grain: one row per tenant and carrier combination, for a fixed list of 20 named tenants
     Columns:
     - tenant_name
     - performance_group ('best' or 'worst', per the fixed list below)
     - carrier
     - shipments_count (2025)
     - share_of_tenant_shipments_pct (this carrier's shipments / tenant's total shipments in 2025, x100)
     - avg_transit_days (this tenant+carrier combination, 2025)
     - pct_within_3_days
     Details: Scope: shipments joined via fulfillments -> orders -> tenants, shipped_at in 2025-01-01 through 2025-12-31, restricted to these 20 tenants only (matched by tenants.name): WORST group = 'Thistlecombe', 'Purrfect Pantry', 'Chocolaterie Mireille', 'Kayak Corner', 'Studio Vrij', 'Sourdough Club', 'Grind Collective', 'Spice Route Box', 'Kettle & Key', 'Polder Packaging'. BEST group = 'Pfotenglück', 'Cragsley Outfitters', 'Brennquell Kaffee', 'Feather & Fin', 'Ridgeback Gear', 'Wohnglück', 'Mirelle', 'Honeycomb Larder', 'Kontor24 Bürobedarf', 'Tail Wag Supply'. transit_days = DATEDIFF(day, shipped_at, delivered_at), delivered subset only. Round avg_transit_days to 2 decimals, pct columns to 1 decimal. Order by performance_group ('worst' first, then 'best'), then tenant_name ascending, then shipments_count descending within each tenant.
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Bea calls dry_run

     WITH selected_tenants AS (
       SELECT
         t0.id AS tenant_id,
         t0.name AS tenant_name,
         CASE
           WHEN t0.name IN ('Thistlecombe','Purrfect Pantry','Chocolaterie Mireille','Kayak Corner','Studio Vrij','Sourdough Club','Grind Collective','Spice Route Box','Kettle & Key','Polder Packaging') THEN 'worst'
           WHEN t0.name IN ('Pfotenglück','Cragsley Outfitters','Brennquell Kaffee','Feather & Fin','Ridgeback Gear','Wohnglück','Mirelle','Honeycomb Larder','Kontor24 Bürobedarf','Tail Wag Supply') THEN 'best'
         END AS performance_group
       FROM tenants t0
       WHERE t0.name IN (
         'Thistlecombe','Purrfect Pantry','Chocolaterie Mireille','Kayak Corner','Studio Vrij','Sourdough Club','Grind Collective','Spice Route Box','Kettle & Key','Polder Packaging',
         'Pfotenglück','Cragsley Outfitters','Brennquell Kaffee','Feather & Fin','Ridgeback Gear','Wohnglück','Mirelle','Honeycomb Larder','Kontor24 Bürobedarf','Tail Wag Supply'
       )
     ),
     shipment_base AS (
       SELECT
         s1.tenant_id,
         s1.carrier,
         s1.shipped_at,
         s1.delivered_at
       FROM shipments s1
       JOIN fulfillments f1 ON f1.id = s1.fulfillment_id
       JOIN orders o1 ON o1.id = f1.order_id
       WHERE s1.shipped_at >= TIMESTAMP '2025-01-01'
         AND s1.shipped_at < TIMESTAMP '2026-01-01'
         AND s1.tenant_id IN (SELECT st0.tenant_id FROM selected_tenants st0)
     ),
     tenant_carrier_counts AS (
       SELECT
         sb0.tenant_id,
         sb0.carrier,
         COUNT(*) AS shipments_count
       FROM shipment_base sb0
       GROUP BY sb0.tenant_id, sb0.carrier
     ),
     tenant_totals AS (
       SELECT
         sb1.tenant_id,
         COUNT(*) AS total_shipments
       FROM shipment_base sb1
       GROUP BY sb1.tenant_id
     ),
     transit_stats AS (
       SELECT
         sb2.tenant_id,
         sb2.carrier,
         AVG(CASE WHEN sb2.delivered_at IS NOT NULL THEN DATE_PART('day', sb2.delivered_at - sb2.shipped_at) END) AS avg_transit_days,
         100.0 * SUM(CASE WHEN sb2.delivered_at IS NOT NULL AND DATE_PART('day', sb2.delivered_at - sb2.shipped_at) <= 3 THEN 1 ELSE 0 END)
           / NULLIF(SUM(CASE WHEN sb2.delivered_at IS NOT NULL THEN 1 ELSE 0 END), 0) AS pct_within_3_days
       FROM shipment_base sb2
       GROUP BY sb2.tenant_id, sb2.carrier
     )
     SELECT
       st.tenant_name AS tenant_name,
       st.performance_group AS performance_group,
       tcc.carrier AS carrier,
       tcc.shipments_count AS shipments_count,
       ROUND(100.0 * tcc.shipments_count / tt.total_shipments, 1) AS share_of_tenant_shipments_pct,
       ROUND(COALESCE(ts.avg_transit_days, 0), 2) AS avg_transit_days,
       ROUND(COALESCE(ts.pct_within_3_days, 0), 1) AS pct_within_3_days
     FROM tenant_carrier_counts tcc
     JOIN selected_tenants st ON st.tenant_id = tcc.tenant_id
     JOIN tenant_totals tt ON tt.tenant_id = tcc.tenant_id
     LEFT JOIN transit_stats ts ON ts.tenant_id = tcc.tenant_id AND ts.carrier = tcc.carrier
     ORDER BY
       CASE st.performance_group WHEN 'worst' THEN 0 ELSE 1 END,
       st.tenant_name ASC,
       tcc.shipments_count DESC,
       tcc.carrier ASC

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: tenant_name, performance_group, carrier, shipments_count, share_of_tenant_shipments_pct, avg_transit_days, pct_within_3_days

   • BeaQuery compiles and produces the expected columns. Submitting.
   • Bea calls submit

     WITH selected_tenants AS (
       SELECT
         t0.id AS tenant_id,
         t0.name AS tenant_name,
         CASE
           WHEN t0.name IN ('Thistlecombe','Purrfect Pantry','Chocolaterie Mireille','Kayak Corner','Studio Vrij','Sourdough Club','Grind Collective','Spice Route Box','Kettle & Key','Polder Packaging') THEN 'worst'
           WHEN t0.name IN ('Pfotenglück','Cragsley Outfitters','Brennquell Kaffee','Feather & Fin','Ridgeback Gear','Wohnglück','Mirelle','Honeycomb Larder','Kontor24 Bürobedarf','Tail Wag Supply') THEN 'best'
         END AS performance_group
       FROM tenants t0
       WHERE t0.name IN (
         'Thistlecombe','Purrfect Pantry','Chocolaterie Mireille','Kayak Corner','Studio Vrij','Sourdough Club','Grind Collective','Spice Route Box','Kettle & Key','Polder Packaging',
         'Pfotenglück','Cragsley Outfitters','Brennquell Kaffee','Feather & Fin','Ridgeback Gear','Wohnglück','Mirelle','Honeycomb Larder','Kontor24 Bürobedarf','Tail Wag Supply'
       )
     ),
     shipment_base AS (
       SELECT
         s1.tenant_id,
         s1.carrier,
         s1.shipped_at,
         s1.delivered_at
       FROM shipments s1
       JOIN fulfillments f1 ON f1.id = s1.fulfillment_id
       JOIN orders o1 ON o1.id = f1.order_id
       WHERE s1.shipped_at >= TIMESTAMP '2025-01-01'
         AND s1.shipped_at < TIMESTAMP '2026-01-01'
         AND s1.tenant_id IN (SELECT st0.tenant_id FROM selected_tenants st0)
     ),
     tenant_carrier_counts AS (
       SELECT
         sb0.tenant_id,
         sb0.carrier,
         COUNT(*) AS shipments_count
       FROM shipment_base sb0
       GROUP BY sb0.tenant_id, sb0.carrier
     ),
     tenant_totals AS (
       SELECT
         sb1.tenant_id,
         COUNT(*) AS total_shipments
       FROM shipment_base sb1
       GROUP BY sb1.tenant_id
     ),
     transit_stats AS (
       SELECT
         sb2.tenant_id,
         sb2.carrier,
         AVG(CASE WHEN sb2.delivered_at IS NOT NULL THEN DATE_PART('day', sb2.delivered_at - sb2.shipped_at) END) AS avg_transit_days,
         100.0 * SUM(CASE WHEN sb2.delivered_at IS NOT NULL AND DATE_PART('day', sb2.delivered_at - sb2.shipped_at) <= 3 THEN 1 ELSE 0 END)
           / NULLIF(SUM(CASE WHEN sb2.delivered_at IS NOT NULL THEN 1 ELSE 0 END), 0) AS pct_within_3_days
       FROM shipment_base sb2
       GROUP BY sb2.tenant_id, sb2.carrier
     )
     SELECT
       st.tenant_name AS tenant_name,
       st.performance_group AS performance_group,
       tcc.carrier AS carrier,
       tcc.shipments_count AS shipments_count,
       ROUND(100.0 * tcc.shipments_count / tt.total_shipments, 1) AS share_of_tenant_shipments_pct,
       ROUND(COALESCE(ts.avg_transit_days, 0), 2) AS avg_transit_days,
       ROUND(COALESCE(ts.pct_within_3_days, 0), 1) AS pct_within_3_days
     FROM tenant_carrier_counts tcc
     JOIN selected_tenants st ON st.tenant_id = tcc.tenant_id
     JOIN tenant_totals tt ON tt.tenant_id = tcc.tenant_id
     LEFT JOIN transit_stats ts ON ts.tenant_id = tcc.tenant_id AND ts.carrier = tcc.carrier
     ORDER BY
       CASE st.performance_group WHEN 'worst' THEN 0 ELSE 1 END,
       st.tenant_name ASC,
       tcc.shipments_count DESC,
       tcc.carrier ASC

   • The warehouse answers

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

   • BeaSubmitted. Summary of what the report does: - **Grain:** one row per tenant × carrier, restricted to the 20 named tenants. - **Scope:** shipments joined through fulfillments → orders, filtered to `shipped_at` in 2025. - **Metrics:** shipment count, each carrier's share of that tenant's 2025 shipments, average transit days, and % delivered within 3 days — the latter two computed over the delivered subset only. - **Ordering:** worst group first, then best, then tenant name, then shipment count descending (carrier as final tiebreak for determinism). One deviation noted to the analyst: transit-day metrics are based only on shipments that actually have a `delivered_at` timestamp (as the spec implied), and tenant/carrier combos with no delivered shipments show 0 instead of null.

   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 (276 KB) · Lena Hartmann's transcript (921 KB) · Priya Raman's transcript (266 KB) · the night's plan (5 KB) · the night's reports (14 KB) · the week's memo (3 KB)