Agent memory from BigQuery Agent Analytics traces
source : BigQuery test-project-0728-467323.bqaa_agent_memory_demo.analyst_events
user   : maya.chen   current session: an-20261007t0834-d3-maya-2   now: 2026-10-07T10:03:35Z
read   : 9 sessions, 11 traces from one Client.list_traces() call
         TraceFilter(user_id='maya.chen', start_time=2026-09-07T10:03:35Z)

== 1. Short-term memory ==
Sessions (ShortTermMemory.list_sessions):
  an-20261007t0834-d5-maya-2  2026-10-07T09:32:50Z  day 2026-10-07  2 messages  Prep me for the assortment review: the top three brands per category ...
  an-20261007t0834-d5-maya-1  2026-10-07T09:12:17Z  day 2026-10-07  2 messages  How did my categories do yesterday?
  an-20261007t0834-d4-maya-1  2026-10-07T09:00:58Z  day 2026-10-06  2 messages  Add Swim to my categories from now on. What was the return rate for m...
  an-20261007t0834-d3-maya-2  2026-10-07T08:59:42Z  day 2026-10-05  2 messages  How did my categories do last month?
  an-20261007t0834-d3-maya-1  2026-10-07T08:54:51Z  day 2026-10-05  2 messages  Morning. How did my categories do over the weekend?
  an-20261007t0834-d2-maya-2  2026-10-07T08:52:11Z  day 2026-10-02  4 messages  We review the holiday assortment on November 12. For Dresses, which p...
  an-20261007t0834-d2-maya-1  2026-10-07T08:42:50Z  day 2026-10-02  2 messages  Morning! How did my categories do yesterday?
  an-20261007t0834-d1-maya-2  2026-10-07T08:40:49Z  day 2026-10-01  2 messages  Which five brands drove most of my Jeans net revenue last month?
  an-20261007t0834-d1-maya-1  2026-10-07T08:35:09Z  day 2026-10-01  4 messages  Hi, I'm Maya Chen. I lead merchandising for women's Jeans and Dresses...
Conversation an-20261007t0834-d3-maya-2 (ShortTermMemory.get_conversation):
  [user] How did my categories do last month?
  [assistant] In September 2026, your categories generated **$39,355.24** in net revenue across **389 units sold**, up **+20.78%** compared to August:

| Category | Sep 2026 Net Revenue | Units Sold | Aug 2026 Net Revenue | MoM Change ($) | MoM Change (%) |
| :--- | ---: | ---: | ---: | ---: | ---: |
| **Dresses** | $22,212.13 | 217 | $16,203.41 | +$6,008.72 | +37.08% |
| **Jeans** | $17,143.11 | 172 | $16,381.15 | +$761.96 | +4.65% |
| **Total** | **$39,355.24** | **389** | **$32,584.56** | **+$6,770.68** | **+20.78%** |

*Definitions & Filters:* Net revenue sums `sale_price` excluding `Cancelled` and `Returned` items for `department = 'Women'` across `2026-09-01` to `2026-09-30` compared against `2026-08-01` to `2026-08-31`.

== 2. Long-term memory ==
Preference history (ADK user: state from STATE_DELTA rows):
  my_categories = Women's Jeans and Dresses 2026-10-07T08:35:19Z -> 2026-10-07T09:01:11Z  an-20261007t0834-d1-maya-1
  revenue_definition = net revenue (excludes cancelled and returned) 2026-10-07T08:36:10Z -> current               an-20261007t0834-d1-maya-1
  price_bands = under $50, $50 to $100, and over $100 2026-10-07T08:54:06Z -> current               an-20261007t0834-d2-maya-2
  my_categories = Women's Jeans, Dresses, and Swim 2026-10-07T09:01:11Z -> current               an-20261007t0834-d4-maya-1
Current preferences: my_categories = Women's Jeans, Dresses, and Swim, price_bands = under $50, $50 to $100, and over $100, revenue_definition = net revenue (excludes cancelled and returned)
Facts extracted from the conversations (memory_consolidation):
  Maya Chen leads merchandising for Jeans.  [an-20261007t0834-d1-maya-1/76594e3850c0741d]
  Maya Chen leads merchandising for Dresses.  [an-20261007t0834-d1-maya-1/76594e3850c0741d]
  Maya Chen defines net revenue as revenue excluding cancelled and returned items.  [an-20261007t0834-d1-maya-1/319b97d996cc9a60]
  Maya Chen manages the Jeans product category.  [an-20261007t0834-d1-maya-2/e361e7c7c833b4ee]
  The holiday assortment review takes place on 2026-11-12.  [an-20261007t0834-d2-maya-2/a23127466193f30f]
  Maya Chen tracks the Swim category.  [an-20261007t0834-d4-maya-1/fbf146d30881af92]
Entities extracted from messages:
  Maya Chen      PERSON    8 mentions    an-20261007t0834-d1-maya-1, an-20261007t0834-d1-maya-2, an-20261007t0834-d2-maya-2, an-20261007t0834-d3-maya-1, an-20261007t0834-d3-maya-2, an-20261007t0834-d4-maya-1, an-20261007t0834-d5-maya-2
  net revenue    METRIC    3 mentions    an-20261007t0834-d1-maya-1, an-20261007t0834-d1-maya-2, an-20261007t0834-d5-maya-2
  Dresses        PRODUCT_CATEGORY 2 mentions    an-20261007t0834-d1-maya-1, an-20261007t0834-d2-maya-2
  Jeans          PRODUCT_CATEGORY 2 mentions    an-20261007t0834-d1-maya-1, an-20261007t0834-d1-maya-2
  assortment review EVENT     1 mention     an-20261007t0834-d5-maya-2
  Swim           PRODUCT_CATEGORY 1 mention     an-20261007t0834-d4-maya-1
  Holiday Assortment Review EVENT     1 mention     an-20261007t0834-d2-maya-2

== 3. Reasoning memory ==
Traces (ReasoningMemory.list_traces):
  e-34a2ee15-bbc1-4560-aa3a-e876c9b5955a  an-20261007t0834-d5-maya-2  answered              7 tool calls  Prep me for the assortment review: the top th...
  e-1425e956-e7a0-4144-85c3-ee7dc0476e35  an-20261007t0834-d5-maya-1  answered              2 tool calls  How did my categories do yesterday?
  e-692b8209-7cc3-4841-9143-a770cf8502f1  an-20261007t0834-d4-maya-1  answered              8 tool calls  Add Swim to my categories from now on. What w...
  e-ed762708-7f15-453d-81b7-0eb34a4bd529  an-20261007t0834-d3-maya-2  answered              2 tool calls  How did my categories do last month?
  e-ee24d37a-891a-4ede-8b40-9435bea58e4b  an-20261007t0834-d3-maya-1  answered              3 tool calls  Morning. How did my categories do over the we...
  e-2b812139-4df0-4bc3-aa74-5efb4f0b5586  an-20261007t0834-d2-maya-2  answered              2 tool calls  Use these price bands from now on: under $50,...
  e-19d4d0cd-5cfc-428a-ac7f-1872c6ff723b  an-20261007t0834-d2-maya-2  answered              6 tool calls  We review the holiday assortment on November ...
  e-d761aa28-b04d-4f1a-a28b-ada79bf629fe  an-20261007t0834-d2-maya-1  answered              4 tool calls  Morning! How did my categories do yesterday?
  e-5c397a4d-414b-47ed-9741-85fb488e6ba6  an-20261007t0834-d1-maya-2  answered              7 tool calls  Which five brands drove most of my Jeans net ...
  e-8d7055f2-357d-4361-8b64-057ccea1beea  an-20261007t0834-d1-maya-1  answered              2 tool calls  Please always use net revenue: leave out canc...
  e-4873ea6b-8c92-4f26-8dd0-35e1e18903bd  an-20261007t0834-d1-maya-1  answered              8 tool calls  Hi, I'm Maya Chen. I lead merchandising for w...
Trace e-34a2ee15-bbc1-4560-aa3a-e876c9b5955a (session an-20261007t0834-d5-maya-2): answered
  task   : Prep me for the assortment review: the top three brands per category by net revenue last month, split by my price bands.
  step 1 : call: recall_memory
           recall_memory  success  4609 ms
  step 2 : call: run_sql
           run_sql  success  1215 ms
  step 3 : call: run_sql
           run_sql  success  1210 ms
  step 4 : call: run_sql
           run_sql  success  1674 ms
  step 5 : call: run_sql
           run_sql  success  1098 ms
  step 6 : call: run_sql
           run_sql  success  1144 ms
  step 7 : call: run_sql
           run_sql  success  1125 ms
  outcome: Here are the top three brands per category by net revenue for September 2026, split across your price bands:

### Top 3 Brands per Category (Net Revenue by Price Band)

| Category | Brand | Under $50 | $50 to $100 | Over $100 | Total Net Revenue |
| :--- | :--- | ---: | ---: | ---: | ---: |
| **Dresses** | **BCBGMAXAZRIA** | — | — | $4,125.99 | **$4,125.99** |
| | **Kiyonna** | — | — | $980.00 | **$980.00** |
| | **Jax** | — | $79.00 | $759.98 | **$838.98** |
| **Jeans** | **7 For All Mankind** | — | — | $2,199.15 | **$2,199.15** |
| | **True Religion** | — | — | $1,273.00 | **$1,273.00** |
| | **Not Your Daughter's Jeans** | — | $335.75 | $878.00 | **$1,213.75** |
| **Swim** | **Speedo** | $266.70 | $1,131.46 | — | **$1,398.16** |
| | **Miraclesuit** | — | — | $1,382.00 | **$1,382.00** |
| | **Swim 365** | — | $864.90 | — | **$864.90** |

---

### Top 3 Brands Within Each Price Band

If you also need the assortment leaders by individual price tier:

| Category | Under $50 | $50 to $100 | Over $100 |
| :--- | :--- | :--- | :--- |
| **Dresses** | 1. Allegra K ($333.79)<br>2. Hot from Hollywood ($289.93)<br>3. Lindy Bop ($281.94) | 1. KAMALIKULTURE ($440.89)<br>2. Jessica Howard ($430.98)<br>3. US Fairytailes ($299.97) | 1. BCBGMAXAZRIA ($4,125.99)<br>2. Kiyonna ($980.00)<br>3. Parker ($792.00) |
| **Jeans** | 1. Levi's ($705.84)<br>2. Lee ($159.80)<br>3. Woman Within ($144.96) | 1. Lucky Brand ($508.98)<br>2. KUT from the Kloth ($407.53)<br>3. Silver Jeans ($407.00) | 1. 7 For All Mankind ($2,199.15)<br>2. True Religion ($1,273.00)<br>3. Joe's Jeans ($1,094.22) |
| **Swim** | 1. Roxy ($267.68)<br>2. Speedo ($266.70)<br>3. Body Glove ($230.00) | 1. Speedo ($1,131.46)<br>2. Swim 365 ($864.90)<br>3. Luli Fama ($320.00) | 1. Miraclesuit ($1,382.00)<br>2. UjENA ($534.00)<br>3. Magic Suit ($412.00) |

*Filters & Definitions: Women's department (`department = 'Women'`), categories Dresses, Jeans, and Swim; orders created between 2026-09-01 and 2026-09-30; net revenue excludes `Cancelled` and `Returned` items; price bands are Under $50 (< $50), $50 to $100 ($50–$100), and Over $100 (> $100) based on item sale price.*
  metrics: latency_ms=100743 llm_calls=8 tool_calls=7 tool_errors=0 total_tokens=87856
Tool stats (ReasoningMemory.get_tool_stats):
  tool              calls  ok  failed  success   avg_ms
  run_sql              33  33       0     100%   1318.0
  recall_memory         9   9       0     100%   3895.8
  describe_table        5   5       0     100%    423.2
  save_preference       4   4       0     100%      1.2
Similar past tasks for 'How did my categories do last month?' (by embedding, successful only):
  0.90  e-1425e956-e7a0-4144-85c3-ee7dc0476e35  How did my categories do yesterday?
  0.83  e-d761aa28-b04d-4f1a-a28b-ada79bf629fe  Morning! How did my categories do yesterday?
  0.81  e-ee24d37a-891a-4ede-8b40-9435bea58e4b  Morning. How did my categories do over the weekend?

== 4. get_context() for the next model call ==
# Memory for user maya.chen

## Short-term: current conversation (session an-20261007t0834-d3-maya-2)
- user: How did my categories do last month? [an-20261007t0834-d3-maya-2/7a99a92fac65a106]
- assistant: In September 2026, your categories generated **$39,355.24** in net revenue across **389 units sold**, up **+20.78%** compared to August:

| Category | Sep 2026 Net Revenue | Units Sold | Aug 2026 Net Revenue | MoM Change ($) | MoM Change (%) |
| :--- | ---: | ---: | ---: | ---: | ---: |
| **Dresses** | $22,212.13 | 217 | $16,203.41 | +$6,008.72 | +37.08% |
| **Jeans** | $17,143.11 | 172 | $16,381.15 | +$761.96 | +4.65% |
| **Total** | **$39,355.24** | **389** | **$32,584.56** | **+$6,770.68** | **+20.78%** |

*Definitions & Filters:* Net revenue sums `sale_price` excluding `Cancelled` and `Returned` items for `department = 'Women'` across `2026-09-01` to `2026-09-30` compared against `2026-08-01` to `2026-08-31`. [an-20261007t0834-d3-maya-2/37eaa4f47a8348e7]

## Long-term: user preferences (ADK user: state)
- my_categories = Women's Jeans, Dresses, and Swim (since 2026-10-07T09:01:11Z; replaced Women's Jeans and Dresses) [an-20261007t0834-d4-maya-1/fbf146d30881af92]
- price_bands = under $50, $50 to $100, and over $100 (since 2026-10-07T08:54:06Z) [an-20261007t0834-d2-maya-2/8e5fde9b5587d53d]
- revenue_definition = net revenue (excludes cancelled and returned) (since 2026-10-07T08:36:10Z) [an-20261007t0834-d1-maya-1/319b97d996cc9a60]

## Long-term: facts from earlier conversations
- Maya Chen leads merchandising for Jeans. [an-20261007t0834-d1-maya-1/76594e3850c0741d]
- Maya Chen leads merchandising for Dresses. [an-20261007t0834-d1-maya-1/76594e3850c0741d]
- Maya Chen defines net revenue as revenue excluding cancelled and returned items. [an-20261007t0834-d1-maya-1/319b97d996cc9a60]
- Maya Chen manages the Jeans product category. [an-20261007t0834-d1-maya-2/e361e7c7c833b4ee]
- The holiday assortment review takes place on 2026-11-12. [an-20261007t0834-d2-maya-2/a23127466193f30f]
- Maya Chen tracks the Swim category. [an-20261007t0834-d4-maya-1/fbf146d30881af92]

## Long-term: entities from conversations and tool calls
- Maya Chen (PERSON): 8 mentions [+2 earlier, an-20261007t0834-d2-maya-2/a23127466193f30f, an-20261007t0834-d2-maya-2/8e5fde9b5587d53d, an-20261007t0834-d3-maya-1/c58437256ee8b23b, an-20261007t0834-d3-maya-2/7a99a92fac65a106, an-20261007t0834-d4-maya-1/fbf146d30881af92, an-20261007t0834-d5-maya-2/c2e25ce0bbe0bb14]
- net revenue (METRIC): 3 mentions [an-20261007t0834-d1-maya-1/319b97d996cc9a60, an-20261007t0834-d1-maya-2/e361e7c7c833b4ee, an-20261007t0834-d5-maya-2/c2e25ce0bbe0bb14]
- Dresses (PRODUCT_CATEGORY): 2 mentions [an-20261007t0834-d1-maya-1/76594e3850c0741d, an-20261007t0834-d2-maya-2/a23127466193f30f]
- Jeans (PRODUCT_CATEGORY): 2 mentions [an-20261007t0834-d1-maya-1/76594e3850c0741d, an-20261007t0834-d1-maya-2/e361e7c7c833b4ee]
- assortment review (EVENT): 1 mention [an-20261007t0834-d5-maya-2/c2e25ce0bbe0bb14]
- Swim (PRODUCT_CATEGORY): 1 mention [an-20261007t0834-d4-maya-1/fbf146d30881af92]

## Reasoning: similar past tasks that succeeded
- 0.90 trace e-1425e956-e7a0-4144-85c3-ee7dc0476e35: "How did my categories do yesterday?" -> recall_memory, run_sql -> "Yesterday (Tuesday, October 6, 2026), your categories generated **$10,871.87** in net revenue across **113 items sold**:

| Category | Net Revenue | Items Sold | Net Orders | Cancelled (Items / Value) | Returned (Items / Value) |
| :--- | ---: | ---: | ---: | :--- | :--- |
| **Jeans** | $4,599.55 | 41 | 41 | 2 ($198.00) | 2 ($214.99) |
| **Dresses** | $3,869.24 | 39 | 38 | 5 ($243.51) | 11 ($68..." [an-20261007t0834-d5-maya-1/d9ad07f711e242d2]
  reuse: run_sql({"purpose": "Get yesterday's performance (net revenue, orders, units sold, cancellations, and returns) for Women's Jeans, Dresses, and Swim", "sql": "SELECT \n    p.category,\n    COUNT(DISTINCT CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.order_id END) AS net_orders,\n    COUNT(CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.id END) AS net_items_sold,\n    ROUND(SUM(CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.sale_price ELSE 0 END), 2) AS net_revenue,\n    COUNT(CASE WHEN oi.status = 'Cancelled' THEN oi.id END) AS cancelled_items,\n    ROUND(SUM(CASE WHEN oi.status = 'Cancelled' THEN oi.sale_price ELSE 0 END), 2) AS cancelled_revenue,\n    COUNT(CASE WHEN oi.status = 'Returned' THEN oi.id END) AS returned_items,\n    ROUND(SUM(CASE WHEN oi.status = 'Returned' THEN oi.sale_price ELSE 0 END), 2) AS returned_revenue\nFROM `bigquery-public-data.thelook_ecommerce.order_items` oi\nJOIN `bigquery-public-data.thelook_ecommerce.products` p\n    ON oi.product_id = p.id\nWHERE p.department = 'Women'\n  AND p.category IN ('Jeans', 'Dresses', 'Swim')\n  AND oi.created_at >= '2026-10-06'\n  AND oi.created_at < '2026-10-07'\nGROUP BY p.category\nUNION ALL\nSELECT \n    'Total' AS category,\n    COUNT(DISTINCT CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.order_id END) AS net_orders,\n    COUNT(CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.id END) AS net_items_sold,\n    ROUND(SUM(CASE WHEN oi.status NOT IN ('Can...) [an-20261007t0834-d5-maya-1/6eae20ba803c4517]
- 0.83 trace e-d761aa28-b04d-4f1a-a28b-ada79bf629fe: "Morning! How did my categories do yesterday?" -> recall_memory, run_sql -> "Yesterday (Thursday, 2026-10-01), your categories generated **$1,744.38** in net revenue across 19 items sold:

| Category | Net Revenue | Items Sold | Orders | Cancelled Items | Cancelled Value |
| :--- | ---: | ---: | ---: | ---: | ---: |
| **Dresses** | $1,198.67 | 10 | 10 | 1 | $55.00 |
| **Jeans** | $545.71 | 9 | 9 | 1 | $198.00 |
| **Total** | **$1,744.38** | **19** | **19** | **2** | **$..." [an-20261007t0834-d2-maya-1/2435abaadb944141]
  reuse: run_sql({"purpose": "Get clean net and gross metrics for yesterday", "sql": "SELECT \n    p.category,\n    COUNT(DISTINCT CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.order_id END) AS net_orders,\n    COUNT(CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.id END) AS net_items_sold,\n    ROUND(SUM(CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.sale_price ELSE 0 END), 2) AS net_revenue,\n    COUNT(CASE WHEN oi.status = 'Cancelled' THEN oi.id END) AS cancelled_items,\n    ROUND(SUM(CASE WHEN oi.status = 'Cancelled' THEN oi.sale_price ELSE 0 END), 2) AS cancelled_revenue\nFROM `bigquery-public-data.thelook_ecommerce.order_items` oi\nJOIN `bigquery-public-data.thelook_ecommerce.products` p\n    ON oi.product_id = p.id\nWHERE p.department = 'Women'\n  AND p.category IN ('Jeans', 'Dresses')\n  AND oi.created_at >= '2026-10-01'\n  AND oi.created_at < '2026-10-02'\nGROUP BY p.category\nUNION ALL\nSELECT \n    'Total' AS category,\n    COUNT(DISTINCT CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.order_id END) AS net_orders,\n    COUNT(CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.id END) AS net_items_sold,\n    ROUND(SUM(CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.sale_price ELSE 0 END), 2) AS net_revenue,\n    COUNT(CASE WHEN oi.status = 'Cancelled' THEN oi.id END) AS cancelled_items,\n    ROUND(SUM(CASE WHEN oi.status = 'Cancelled' THEN oi.sale_price ELSE 0 END), 2) AS cancelled_revenue\nFROM `bigquer...) [an-20261007t0834-d2-maya-1/5263ec7c8d7443ca]
- 0.81 trace e-ee24d37a-891a-4ede-8b40-9435bea58e4b: "Morning. How did my categories do over the weekend?" -> recall_memory, run_sql -> "Over the weekend (Saturday, Oct 3 – Sunday, Oct 4, 2026), your categories generated **$4,992.09** in net revenue across **64** items sold:

| Category | Net Revenue | Items Sold | Orders | Cancelled Items (Value) | Returned Items (Value) |
| :--- | ---: | ---: | ---: | ---: | ---: |
| **Jeans** | $3,038.33 | 35 | 34 | 7 ($521.48) | 4 ($205.88) |
| **Dresses** | $1,953.76 | 29 | 29 | 8 ($557.67)..." [an-20261007t0834-d3-maya-1/b62d516109804736]
  reuse: run_sql({"purpose": "Daily breakdown for the weekend by category", "sql": "SELECT \n    DATE(oi.created_at) AS sale_date,\n    p.category,\n    COUNT(CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.id END) AS net_items_sold,\n    ROUND(SUM(CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.sale_price ELSE 0 END), 2) AS net_revenue\nFROM `bigquery-public-data.thelook_ecommerce.order_items` oi\nJOIN `bigquery-public-data.thelook_ecommerce.products` p\n    ON oi.product_id = p.id\nWHERE p.department = 'Women'\n  AND p.category IN ('Jeans', 'Dresses')\n  AND oi.created_at >= '2026-10-03'\n  AND oi.created_at < '2026-10-05'\nGROUP BY 1, 2\nORDER BY 1, 2"}) [an-20261007t0834-d3-maya-1/4d610dec97cf4999]
- 0.72 trace e-4873ea6b-8c92-4f26-8dd0-35e1e18903bd: "Hi, I'm Maya Chen. I lead merchandising for women's Jeans and Dresses. How much revenue did my categories bring in last month?" -> recall_memory, save_preference, describe_table, run_sql -> "In September 2026, your categories generated **$39,355.24** in net revenue (**$45,957.48** gross):

| Category | Net Revenue | Gross Revenue |
| :--- | ---: | ---: |
| **Dresses** | $22,212.13 | $26,344.67 |
| **Jeans** | $17,143.11 | $19,612.81 |
| **Total** | **$39,355.24** | **$45,957.48** |

*Definitions & Filters:* Order items created between `2026-09-01` and `2026-09-30` for `department =..." [an-20261007t0834-d1-maya-1/8acf0ed54a9b48aa]
  reuse: run_sql({"purpose": "Calculate net and gross revenue by category for September 2026", "sql": "SELECT \n    p.category,\n    ROUND(SUM(CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.sale_price ELSE 0 END), 2) AS net_revenue,\n    ROUND(SUM(CASE WHEN oi.status != 'Cancelled' THEN oi.sale_price ELSE 0 END), 2) AS gross_revenue,\n    ROUND(SUM(CASE WHEN oi.status = 'Returned' THEN oi.sale_price ELSE 0 END), 2) AS returned_amount,\n    ROUND(SUM(CASE WHEN oi.status = 'Cancelled' THEN oi.sale_price ELSE 0 END), 2) AS cancelled_amount\nFROM `bigquery-public-data.thelook_ecommerce.order_items` oi\nJOIN `bigquery-public-data.thelook_ecommerce.products` p\n    ON oi.product_id = p.id\nWHERE p.department = 'Women'\n  AND p.category IN ('Jeans', 'Dresses')\n  AND oi.created_at >= '2026-09-01'\n  AND oi.created_at < '2026-10-01'\nGROUP BY p.category\nUNION ALL\nSELECT \n    'Total' AS category,\n    ROUND(SUM(CASE WHEN oi.status NOT IN ('Cancelled', 'Returned') THEN oi.sale_price ELSE 0 END), 2) AS net_revenue,\n    ROUND(SUM(CASE WHEN oi.status != 'Cancelled' THEN oi.sale_price ELSE 0 END), 2) AS gross_revenue,\n    ROUND(SUM(CASE WHEN oi.status = 'Returned' THEN oi.sale_price ELSE 0 END), 2) AS returned_amount,\n    ROUND(SUM(CASE WHEN oi.status = 'Cancelled' THEN oi.sale_price ELSE 0 END), 2) AS cancelled_amount\nFROM `bigquery-public-data.thelook_ecommerce.order_items` oi\nJOIN `bigquery-public-data.thelook_ecommerce.products` p\n    ON oi.product_id = p.id\nWHERE p.departmen...) [an-20261007t0834-d1-maya-1/7de571d627e34c31]
- 0.72 trace e-34a2ee15-bbc1-4560-aa3a-e876c9b5955a: "Prep me for the assortment review: the top three brands per category by net revenue last month, split by my price bands." -> recall_memory, run_sql -> "Here are the top three brands per category by net revenue for September 2026, split across your price bands:

### Top 3 Brands per Category (Net Revenue by Price Band)

| Category | Brand | Under $50 | $50 to $100 | Over $100 | Total Net Revenue |
| :--- | :--- | ---: | ---: | ---: | ---: |
| **Dresses** | **BCBGMAXAZRIA** | — | — | $4,125.99 | **$4,125.99** |
| | **Kiyonna** | — | — | $980.00 ..." [an-20261007t0834-d5-maya-2/a190ee3850bd459c]
  reuse: run_sql({"purpose": "Check if sale_price = 50 or 100 affects top 3 brands", "sql": "SELECT \n  p.category, p.brand, oi.sale_price\nFROM `bigquery-public-data.thelook_ecommerce.order_items` oi\nJOIN `bigquery-public-data.thelook_ecommerce.products` p\n  ON oi.product_id = p.id\nWHERE p.department = 'Women'\n  AND p.category IN ('Jeans', 'Dresses', 'Swim')\n  AND oi.status NOT IN ('Cancelled', 'Returned')\n  AND oi.created_at >= '2026-09-01'\n  AND oi.created_at < '2026-10-01'\n  AND oi.sale_price IN (50.0, 100.0)"}) [an-20261007t0834-d5-maya-2/8b669de2757d4f66]
- 0.71 trace e-692b8209-7cc3-4841-9143-a770cf8502f1: "Add Swim to my categories from now on. What was the return rate for my categories last month?" -> recall_memory, run_sql, save_preference, describe_table -> "I have updated your saved categories to **Women's Jeans, Dresses, and Swim**.

In September 2026, the overall return rate for your categories was **12.52%** by item volume (84 of 671 fulfilled items) and **13.97%** by revenue ($8,504.55 of $60,890.25 gross revenue):

| Category | Returned Items | Gross Items | Item Return Rate | Returned Value | Gross Revenue | Value Return Rate |
| :--- | ---:..." [an-20261007t0834-d4-maya-1/2e07276a386449e8]
  reuse: run_sql({"purpose": "Check returns with returned_at in September 2026", "sql": "SELECT \n    p.category,\n    COUNT(oi.id) AS returned_items_in_sep,\n    ROUND(SUM(oi.sale_price), 2) AS returned_revenue_in_sep\nFROM `bigquery-public-data.thelook_ecommerce.order_items` oi\nJOIN `bigquery-public-data.thelook_ecommerce.products` p\n    ON oi.product_id = p.id\nWHERE p.department = 'Women'\n  AND p.category IN ('Jeans', 'Dresses', 'Swim')\n  AND oi.returned_at >= '2026-09-01'\n  AND oi.returned_at < '2026-10-01'\nGROUP BY p.category"}) [an-20261007t0834-d4-maya-1/a2bd8fb84dc04836]

## Reasoning: tools that failed before
- (none)
