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Two illustrative (question -> TaskList) pairs that teach the OUTPUT SHAPE:
stages, dependency edges, ordered tool-call chains, inline QueryIR,
"${t<id>}" placeholders, and the assumed data-flow convention — `retrieve_data`
pulls rows, then a composite `analyze_*` tool consumes them via a `data` placeholder
referencing the upstream result's column aliases (Pattern A; the tool team may
instead pick self-fetch by `source_id`, in which case these examples are reshaped
to match — see registry.py). They reference a hypothetical sales catalog
(`src_sales` / `t_orders`); these ids are part of the illustration and are not
validated against the user's real catalog. v1 is descriptive/diagnostic — no
modeling tasks.
See AGENT_ARCHITECTURE_CONTEXT_new.md §7.3 (Examples A and B).
"""
from __future__ import annotations
from .schemas import Task, TaskList, ToolCall
# --------------------------------------------------------------------------- #
# Example A — exploratory, no modeling.
# "Which product categories drove last quarter's revenue?"
# Shows: retrieve_data pulls rows -> analyze_aggregate sums revenue per
# category in one call (no manual per-category queries).
# --------------------------------------------------------------------------- #
_EXAMPLE_A = TaskList(
plan_id="example_a",
goal_restated="Identify which product categories contributed most to last quarter's revenue.",
assumptions=["'last quarter' = 2026-01-01 to 2026-03-31."],
open_questions=[],
tasks=[
Task(
id="t1",
stage="data_understanding",
objective="Confirm the sales source exposes category, revenue, and order date.",
tool_calls=[ToolCall(tool="check_data", args={"source_id": "src_sales"})],
expected_output="source_shape",
success_criteria="Produced the orders table schema; the 3 needed columns are present.",
depends_on=[],
estimated_cost="low",
),
Task(
id="t2",
stage="data_preparation",
objective="Pull last quarter's order-level category and revenue rows.",
tool_calls=[
ToolCall(
tool="retrieve_data",
args={
"ir": {
"source_id": "src_sales",
"table_id": "t_orders",
"select": [
{"kind": "column", "column_id": "c_category", "alias": "category"},
{"kind": "column", "column_id": "c_revenue", "alias": "revenue"},
],
"filters": [
{
"column_id": "c_order_date",
"op": "between",
"value": ["2026-01-01", "2026-03-31"],
"value_type": "date",
}
],
"limit": 10000,
}
},
)
],
expected_output="quarter_rows",
success_criteria="Produced last quarter's order rows with category and revenue.",
depends_on=["t1"],
estimated_cost="medium",
),
Task(
id="t3",
stage="evaluation",
objective="Sum revenue per category for the quarter.",
tool_calls=[
ToolCall(
tool="analyze_aggregate",
args={
"data": "${t2}",
"aggregations": {"revenue": ["sum"]},
"group_by": ["category"],
},
)
],
expected_output="category_revenue",
success_criteria="Produced total revenue per category, one row each.",
depends_on=["t2"],
estimated_cost="low",
),
],
)
# --------------------------------------------------------------------------- #
# Example B — descriptive / trend.
# "How has monthly revenue trended by region this year, and what's unusual?"
# --------------------------------------------------------------------------- #
_EXAMPLE_B = TaskList(
plan_id="example_b",
goal_restated="Describe this year's monthly revenue trend and flag unusual months.",
assumptions=["'this year' starts 2026-01-01."],
open_questions=["'Unusual' is interpreted as months far from the typical monthly revenue."],
tasks=[
Task(
id="t1",
stage="data_understanding",
objective="Confirm the sales source exposes order date, revenue, and region.",
tool_calls=[ToolCall(tool="check_data", args={"source_id": "src_sales"})],
expected_output="source_shape",
success_criteria="Produced the orders table schema; the needed columns are present.",
depends_on=[],
estimated_cost="low",
),
Task(
id="t2",
stage="data_preparation",
objective="Pull this year's order dates, revenue, and region.",
tool_calls=[
ToolCall(
tool="retrieve_data",
args={
"ir": {
"source_id": "src_sales",
"table_id": "t_orders",
"select": [
{
"kind": "column",
"column_id": "c_order_date",
"alias": "order_date",
},
{"kind": "column", "column_id": "c_revenue", "alias": "revenue"},
{"kind": "column", "column_id": "c_region", "alias": "region"},
],
"filters": [
{
"column_id": "c_order_date",
"op": ">=",
"value": "2026-01-01",
"value_type": "date",
}
],
"limit": 10000,
}
},
)
],
expected_output="ytd_rows",
success_criteria="Produced this year's order-level rows with date, revenue, region.",
depends_on=["t1"],
estimated_cost="medium",
),
Task(
id="t3",
stage="evaluation",
objective="Bucket revenue into months and summarize the trend and movement.",
tool_calls=[
ToolCall(
tool="analyze_trend",
args={
"data": "${t2}",
"date_column": "order_date",
"value_column": "revenue",
"freq": "month",
"agg": "sum",
},
)
],
expected_output="monthly_trend",
success_criteria=(
"Produced a per-month revenue series with direction and change rate to "
"flag months above/below the typical level."
),
depends_on=["t2"],
estimated_cost="low",
),
],
)
# --------------------------------------------------------------------------- #
# Example C — mixed structured + unstructured.
# "Revenue dipped in Q1 — what happened?"
# Shows: a structured branch (query -> analyze_trend) runs alongside an
# INDEPENDENT retrieve_knowledge branch that pulls qualitative context. Note
# retrieve_knowledge takes a natural-language `query` (NOT a `${t<id>}` data
# placeholder — it is a source, not a consumer) and can run in parallel; the
# Assembler folds the document context into the explanation.
# --------------------------------------------------------------------------- #
_EXAMPLE_C = TaskList(
plan_id="example_c",
goal_restated="Explain Q1's revenue dip using both the numbers and the qualitative record.",
assumptions=["'Q1' = 2026-01-01 to 2026-03-31."],
open_questions=[],
tasks=[
Task(
id="t1",
stage="data_understanding",
objective="Confirm the sales source exposes order date and revenue.",
tool_calls=[ToolCall(tool="check_data", args={"source_id": "src_sales"})],
expected_output="source_shape",
success_criteria="Produced the orders table schema; date and revenue columns present.",
depends_on=[],
estimated_cost="low",
),
Task(
id="t2",
stage="data_preparation",
objective="Pull Q1 order dates and revenue.",
tool_calls=[
ToolCall(
tool="retrieve_data",
args={
"ir": {
"source_id": "src_sales",
"table_id": "t_orders",
"select": [
{
"kind": "column",
"column_id": "c_order_date",
"alias": "order_date",
},
{"kind": "column", "column_id": "c_revenue", "alias": "revenue"},
],
"filters": [
{
"column_id": "c_order_date",
"op": "between",
"value": ["2026-01-01", "2026-03-31"],
"value_type": "date",
}
],
"limit": 10000,
}
},
)
],
expected_output="q1_rows",
success_criteria="Produced Q1 order rows with date and revenue.",
depends_on=["t1"],
estimated_cost="medium",
),
Task(
id="t3",
stage="evaluation",
objective="Summarize the Q1 monthly revenue trend to locate the dip.",
tool_calls=[
ToolCall(
tool="analyze_trend",
args={
"data": "${t2}",
"date_column": "order_date",
"value_column": "revenue",
"freq": "month",
"agg": "sum",
},
)
],
expected_output="q1_trend",
success_criteria="Produced a per-month revenue series showing where revenue fell.",
depends_on=["t2"],
estimated_cost="low",
),
Task(
id="t4",
stage="data_understanding",
objective="Retrieve qualitative context on Q1 operational events behind a dip.",
tool_calls=[
ToolCall(
tool="retrieve_knowledge",
args={
"query": "operational issues, outages, or notable events in Q1 2026",
"top_k": 5,
},
)
],
expected_output="q1_context_chunks",
success_criteria="Produced relevant document chunks about Q1 operations.",
depends_on=[],
estimated_cost="low",
),
],
)
# --------------------------------------------------------------------------- #
# Example D — group-by aggregation (analyze_aggregate arg shape).
# "What is the average and total order value per region?"
# Shows the EXACT analyze_aggregate args: `aggregations` is an OBJECT mapping each
# column to a LIST of functions ({"revenue": ["mean", "sum"]}), and `group_by` is a
# SEPARATE array — NOT a nested list of metric specs. Supported funcs: sum, mean,
# count, min, max, median, nunique.
# --------------------------------------------------------------------------- #
_EXAMPLE_D = TaskList(
plan_id="example_d",
goal_restated="Report the average and total order value for each region.",
assumptions=[],
open_questions=[],
tasks=[
Task(
id="t1",
stage="data_understanding",
objective="Confirm the sales source exposes region and revenue.",
tool_calls=[ToolCall(tool="check_data", args={"source_id": "src_sales"})],
expected_output="source_shape",
success_criteria="Produced the orders table schema; region and revenue present.",
depends_on=[],
estimated_cost="low",
),
Task(
id="t2",
stage="data_preparation",
objective="Pull order-level region and revenue.",
tool_calls=[
ToolCall(
tool="retrieve_data",
args={
"ir": {
"source_id": "src_sales",
"table_id": "t_orders",
"select": [
{"kind": "column", "column_id": "c_region", "alias": "region"},
{"kind": "column", "column_id": "c_revenue", "alias": "revenue"},
],
"limit": 10000,
}
},
)
],
expected_output="region_rows",
success_criteria="Produced order rows with region and revenue.",
depends_on=["t1"],
estimated_cost="medium",
),
Task(
id="t3",
stage="evaluation",
objective="Aggregate mean and total revenue per region.",
tool_calls=[
ToolCall(
tool="analyze_aggregate",
args={
"data": "${t2}",
"aggregations": {"revenue": ["mean", "sum"]},
"group_by": ["region"],
},
)
],
expected_output="region_aggregates",
success_criteria="Produced one row per region with mean and total revenue.",
depends_on=["t2"],
estimated_cost="low",
),
],
)
# --------------------------------------------------------------------------- #
# Example E — non-date filters (value_type is the ELEMENT type, never a container).
# "Total revenue for the East and West regions, counting orders of at least 100."
# Shows: an `in` filter over a list of strings uses value_type "string" (NOT
# "list"); a numeric comparison uses "decimal"; and every filter carries a
# value_type copied from the column's catalog [data_type].
# --------------------------------------------------------------------------- #
_EXAMPLE_E = TaskList(
plan_id="example_e",
goal_restated="Total revenue for the East and West regions, orders of at least 100.",
assumptions=["'at least 100' filters order revenue >= 100."],
open_questions=[],
tasks=[
Task(
id="t1",
stage="data_understanding",
objective="Confirm the sales source exposes region and revenue.",
tool_calls=[ToolCall(tool="check_data", args={"source_id": "src_sales"})],
expected_output="source_shape",
success_criteria="Produced the orders table schema; region and revenue present.",
depends_on=[],
estimated_cost="low",
),
Task(
id="t2",
stage="data_preparation",
objective="Pull East/West order rows of at least 100 with region and revenue.",
tool_calls=[
ToolCall(
tool="retrieve_data",
args={
"ir": {
"source_id": "src_sales",
"table_id": "t_orders",
"select": [
{"kind": "column", "column_id": "c_region", "alias": "region"},
{"kind": "column", "column_id": "c_revenue", "alias": "revenue"},
],
"filters": [
{
"column_id": "c_region",
"op": "in",
"value": ["East", "West"],
"value_type": "string",
},
{
"column_id": "c_revenue",
"op": ">=",
"value": 100,
"value_type": "decimal",
},
],
"limit": 10000,
}
},
)
],
expected_output="filtered_rows",
success_criteria="Produced East/West order rows above the revenue threshold.",
depends_on=["t1"],
estimated_cost="medium",
),
Task(
id="t3",
stage="evaluation",
objective="Sum revenue per region.",
tool_calls=[
ToolCall(
tool="analyze_aggregate",
args={
"data": "${t2}",
"aggregations": {"revenue": ["sum"]},
"group_by": ["region"],
},
)
],
expected_output="region_revenue",
success_criteria="Produced total revenue per region, one row each.",
depends_on=["t2"],
estimated_cost="low",
),
],
)
# --------------------------------------------------------------------------- #
# Example F — descriptive statistics (analyze_descriptive).
# "Give me the summary statistics for order revenue and quantity."
# Shows: retrieve_data pulls the numeric columns -> analyze_descriptive summarizes
# them. The `data` comes from the retrieve_data task (t2), NEVER from the check_data
# inspection step (t1) — check_data returns column metadata, not data rows.
# --------------------------------------------------------------------------- #
_EXAMPLE_F = TaskList(
plan_id="example_f",
goal_restated="Summarize the distribution of order revenue and quantity.",
assumptions=[],
open_questions=[],
tasks=[
Task(
id="t1",
stage="data_understanding",
objective="Confirm the sales source exposes order revenue and quantity.",
tool_calls=[ToolCall(tool="check_data", args={"source_id": "src_sales"})],
expected_output="source_shape",
success_criteria="Produced the orders table schema; the needed columns are present.",
depends_on=[],
estimated_cost="low",
),
Task(
id="t2",
stage="data_preparation",
objective="Pull the order revenue and quantity rows.",
tool_calls=[
ToolCall(
tool="retrieve_data",
args={
"ir": {
"source_id": "src_sales",
"table_id": "t_orders",
"select": [
{"kind": "column", "column_id": "c_revenue", "alias": "revenue"},
{"kind": "column", "column_id": "c_quantity", "alias": "quantity"},
],
"limit": 10000,
}
},
)
],
expected_output="order_rows",
success_criteria="Produced order-level rows with revenue and quantity.",
depends_on=["t1"],
estimated_cost="medium",
),
Task(
id="t3",
stage="evaluation",
objective="Summarize the distribution of revenue and quantity.",
tool_calls=[
ToolCall(
tool="analyze_descriptive",
args={
# `data` references t2 (retrieve_data rows), NOT t1 (check_data).
"data": "${t2}",
# column refs are the retrieve_data output aliases.
"column_ids": ["revenue", "quantity"],
},
)
],
expected_output="summary_stats",
success_criteria=(
"Produced mean/median/std/quartiles for revenue and quantity, above/below "
"the typical range."
),
depends_on=["t2"],
estimated_cost="low",
),
],
)
EXAMPLES: list[tuple[str, TaskList]] = [
("Which product categories drove last quarter's revenue?", _EXAMPLE_A),
("How has monthly revenue trended by region this year, and what's unusual?", _EXAMPLE_B),
("Revenue dipped in Q1 — what happened?", _EXAMPLE_C),
("What is the average and total order value per region?", _EXAMPLE_D),
("Total revenue for the East and West regions, counting orders of at least 100.", _EXAMPLE_E),
("Give me the summary statistics for order revenue and quantity.", _EXAMPLE_F),
]
def render_examples() -> str:
"""Render the few-shots as text for the planner prompt."""
blocks: list[str] = []
for i, (question, plan) in enumerate(EXAMPLES, start=1):
blocks.append(
f"## Example {i}\n\n"
f"Question:\n{question}\n\n"
f"TaskList:\n{plan.model_dump_json(indent=2)}"
)
return "\n\n".join(blocks)
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