Queries · q061 · Head of Merchant Success · week 1, night 2

Test

“Test question”

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

1. Samuel Whitcombe writes to Arjun Patel

   The manager, at the start of week 1

   Subject: What I need for merchant QBRs this month

   Hi Arjun,

   I'm diving into merchant health this week ahead of January's quarterly business reviews with our biggest accounts. I know you've only got a handful of report slots a night, so here's what matters most to me, roughly in priority order:

   1. Who are our biggest and most important merchants, and how are they actually doing? I want a sense of the size of each merchant's business with us (sales volume, order activity) and whether it's growing, flat, or shrinking lately. This is the backbone for every QBR conversation.
   2. Which merchants look like they're at risk right now? I'm thinking about warning signs like slowing sales, rising refunds or cancellations, unpaid invoices, or a drop-off in shopper activity. I want a shortlist I can proactively reach out to before they churn.
   3. Which merchants are thriving, and is there a pattern? I'd like to know who's growing fastest and whether there's anything they have in common (plan tier, vertical, region) that we could point to as a playbook for others.
   4. How healthy is each merchant's own customer base? For our top merchants, I want to understand whether their shoppers are coming back and buying again, or whether they're mostly one-and-done. This tells me how defensible their business is, which matters for how I advise them.
   5. Are merchants getting good support from us, and does it affect how they feel about us? I'd like a read on ticket volume and resolution experience for our top merchants, since I'll likely get asked about this directly in reviews.

   For context: these go straight into prep notes for one-on-one merchant reviews, so I care most about being able to point to specific merchants by name and tell a clear story about each one's trajectory. Broad trends across all 120 merchants are useful background, but the top merchants are what I'll be sitting across the table from this month.

   Let me know if you want me to rank these differently given the slot constraints — happy to cut the list if needed.

   Thanks, Samuel

   Show the whole note

2. Arjun Patel plans the night

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

   test

   Read the whole plan
   Test

   Question

   Test question

   One row per

   tenant

   Columns

   • a
   • b

3. Grace 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.id as tenant_id,
       t.name as a,
       count(o.id) as b
   from tenants t
   left join orders o on o.tenant_id = t.id
   group by t.id, t.name
   order by t.id

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

   “Submitted. Note: since the request only contained placeholder content ("Test"/"a"/"b") with no real business question, I delivered a minimal one-row-per-tenant report (tenant name, lifetime order count) and flagged in the note that Arjun should resend the actual question and metric definitions for a meaningful report.”

4. The report comes back overnight

   As Arjun Patel saw it, from the small dataset the agents worked on

   tenant_id | a                             |    b
   ----------+-------------------------------+-----
           1 | Grind Collective              | 2269
           2 | Marrowby & Vane               | 4064
           3 | Juniper Apothecary            |  405
           4 | Hus & Hem                     |  167
           5 | Fennel & Flint                |  824
           6 | Willow & Wren                 |  317
           7 | Cragsley Outfitters           | 8965
           8 | Castell Fasteners             |  394
           9 | Honeycomb Larder              | 1450
          10 | Wohnglück                     |  330
          11 | Ballycorran Industrial Supply |  853
          12 | Spice Route Box               | 1872
          13 | Rue Lafayette Studio          |  410
          14 | OfficeHub Nordic              |  443
          15 | Velo Noord                    |   98
          16 | Ostwind Mode                  |  284
          17 | Ampfeld Elektronik            | 1058
          18 | Brennquell Kaffee             | 9996
          19 | Kontor24 Bürobedarf           |  637
          20 | Sourdough Club                |  843
          21 | Kettle & Key                  |  128
          22 | Midland Safety Wear           |  367
          23 | Mirelle                       |  102
          24 | Fern & Fable                  |  116
          25 | Studio Vrij                   | 1451
          26 | Velvet Ash                    |  355
          27 | Ampere & Co.                  |  159
          28 | Atelier Nouveau               |  196
          29 | Cinder Peak Sports Co.        |  759
          30 | Linden & Loom                 |  686
          31 | Hound Hamper                  |  120
          32 | Oakhurst Tailoring            |  317
          33 | Fell & Fjell                  |  201
          34 | Paperplane Press              |  135
          35 | Wiesgrund Wohnen              |  763
          36 | Fjordline Wear                |  141
          37 | Pfotenglück                   |  341
          38 | Coastline Interiors           |  350
          39 | Kayak Corner                  |  333
          40 | Petalcourt Beauty             | 1540
          41 | The Balm Room                 |  219
          42 | Gratlinie Outdoor             |  577
          43 | Roast Republic                |  109
          44 | Whisker Works                 |  401
          45 | Maison Clair                  |  100
          46 | Feather & Fin                 |  411
          47 | Tail Wag Supply               |  221
          48 | Ridgeback Gear                |  247
          49 | Stoneware Studio              |   54
          50 | Buchstabe                     |   64
   (120 rows, first 50 printed)

5. It becomes benchmark query q061

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

   • 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

   select    t.id as tenant_id,    t.name as a,    count(o.id) as bfrom tenants tleft join orders o on o.tenant_id = t.idgroup by t.id, t.nameorder by t.idorder by t.id NULLS LAST

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

   DuckDB · Hyper · GizmoSQL · CedarDB · pg_duckdb · PostgreSQL · DataFusion · Firebolt · ClickHouse · chDB · clickhouse_brew

   select
       t.id as tenant_id,
       t.name as a,
       count(o.id) as b
   from tenants t
   left join orders o on o.tenant_id = t.id
   group by t.id, t.name
   order by t.id NULLS LAST

   Scale factor 1000, c8a.24xlarge, after the last load. q061 on the start page, with every configuration.

6. 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

   Grace's whole session, 6 turns

   • The request, as Grace received it

     Report request from Arjun Patel (analyst for the Head of Merchant Success):
     
     Title: Test
     Question: Test question
     Grain: one row per tenant
     Columns:
     - a
     - b
     Details: test details
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Grace calls dry_run

     select
         t.id as tenant_id,
         t.name as a,
         count(o.id) as b
     from tenants t
     left join orders o on o.tenant_id = t.id
     group by t.id, t.name
     order by t.id

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: tenant_id, a, b

   • Grace calls submit

     select
         t.id as tenant_id,
         t.name as a,
         count(o.id) as b
     from tenants t
     left join orders o on o.tenant_id = t.id
     group by t.id, t.name
     order by t.id

   • The warehouse answers

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

   • GraceSubmitted. Note: since the request only contained placeholder content ("Test"/"a"/"b") with no real business question, I delivered a minimal one-row-per-tenant report (tenant name, lifetime order count) and flagged in the note that Arjun should resend the actual question and metric definitions for a meaningful report.

   Not shown here: 239 KB of context the agent was given, its instructions and the schema documentation; it is in the raw transcript.

   Grace's transcript (255 KB) · Arjun Patel's transcript (881 KB) · Samuel Whitcombe's transcript (261 KB) · the night's plan (1 KB) · the night's reports (2 KB) · the week's memo (3 KB)