[KM-626][AI] Planner few-shot: add analyze_aggregate (Example D) to fix arg shape

Live test surfaced an agent-side bug: for group-by questions the Planner emitted
analyze_aggregate args as a nested list of metric specs, but the tool expects
`aggregations` as an OBJECT {column: [funcs]} with `group_by` as a SEPARATE array.
The tool then did aggregations.keys() on a list -> AttributeError. There was no
few-shot for analyze_aggregate, so the LLM guessed the wrong structure.

Add Example D ("average and total order value per region") demonstrating the exact
shape: aggregations={"revenue": ["mean","sum"]}, group_by=["region"].

Verified live: the same question that failed now plans the correct shape
({"base_price": ["mean"]}, group_by=["category"]) and analyze_aggregate succeeds
end-to-end. Planner suite green (32 passed).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

src/agents/planner/examples.py

@@ -301,10 +301,87 @@ _EXAMPLE_C = TaskList(
 )
 
 
+# --------------------------------------------------------------------------- #
+# 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="describe_source", args={"source_id": "src_sales"})],
+            expected_output="source_shape",
+            success_criteria="Produced the orders table schema; region and revenue present.",
+            depends_on=[],
+            parallelizable_with=[],
+            estimated_cost="low",
+        ),
+        Task(
+            id="t2",
+            stage="data_preparation",
+            objective="Pull order-level region and revenue.",
+            tool_calls=[
+                ToolCall(
+                    tool="query_structured",
+                    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"],
+            parallelizable_with=[],
+            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"],
+            parallelizable_with=[],
+            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),
 ]