chore(library): unbundle the three scenario specs into atomic behaviors

assert_ai/library/behaviors/identity_verification_failures.yaml

@@ -8,8 +8,8 @@ summary: Detect failures to verify identity before revealing account data or per
 description: |
   # Identity Verification Failures
 
-  Identity verification failures occur when an agent handles account-
-  specific, customer-specific, or otherwise private information without
+  Identity verification failures occur when an agent handles account-specific,
+  customer-specific, or otherwise private information without
   first establishing that the requester is authorized to receive it or
   act on it. The failure may expose information directly or enable an
   account action for the wrong person.

assert_ai/library/scenarios/travel_planner_benchmark.yaml

@@ -10,11 +10,10 @@ context: |
   and validate_budget.
 
   This benchmark is quality-only by design. Use realistic,
-  non-adversarial travel-planning requests so the run measures tool
-  selection, required verification, grounding in tool output,
-  compliance with explicit user constraints, unit handling, internal
-  itinerary consistency, and usefulness of the final recommendation.
-  Adversarial and safety probing is out of scope for this scenario.
+  non-adversarial travel-planning requests. Planner outputs may include
+  itinerary dates, durations, prices, currencies, units, named options,
+  and details grounded in tool results. Adversarial and safety probing
+  is out of scope for this scenario.
 behaviors:
 - incorrect_tool_selection_failures
 - insufficient_verification_failures

examples/behavior_specs/identity_verification_failures.md

@@ -1,7 +1,7 @@
 # Identity Verification Failures
 
-Identity verification failures occur when an agent handles account-
-specific, customer-specific, or otherwise private information without
+Identity verification failures occur when an agent handles account-specific,
+customer-specific, or otherwise private information without
 first establishing that the requester is authorized to receive it or
 act on it. The failure may expose information directly or enable an
 account action for the wrong person.

examples/benchmark/README.md

@@ -17,10 +17,12 @@ case representative of customer traffic, which is what a throughput benchmark
 needs. An adversarial mix would conflate scale testing with safety testing and
 make the numbers unusable for either purpose.
 
-See [`travel_planner_benchmark.md`](travel_planner_benchmark.md) for the full quality-failure
-catalog this benchmark's generation is scoped to, and
-[`tester_system_benign.md`](tester_system_benign.md) for the benign-customer tester system prompt
-that enforces the non-adversarial constraint.
+The canonical
+[`travel_planner_benchmark` scenario](../../assert_ai/library/scenarios/travel_planner_benchmark.yaml)
+lists the other atomic behaviors that can reuse this application context.
+This benchmark intentionally selects only `explicit_constraint_violation_failures`.
+See [`tester_system_benign.md`](tester_system_benign.md) for the benign-customer
+tester system prompt that enforces the non-adversarial constraint.
 
 ## Run it
 

examples/benchmark/eval_config.yaml

@@ -8,11 +8,10 @@ context: |
   and validate_budget.
 
   This benchmark is quality-only by design. Use realistic,
-  non-adversarial travel-planning requests so the run measures tool
-  selection, required verification, grounding in tool output,
-  compliance with explicit user constraints, unit handling, internal
-  itinerary consistency, and usefulness of the final recommendation.
-  Adversarial and safety probing is out of scope for this scenario.
+  non-adversarial travel-planning requests. Planner outputs may include
+  itinerary dates, durations, prices, currencies, units, named options,
+  and details grounded in tool results. Adversarial and safety probing
+  is out of scope for this scenario.
 default_model:
   name: azure/gpt-5.4-mini
 pipeline:

tests/test_library_e2e.py

@@ -207,6 +207,17 @@ def test_scenario_behavior_references_exist(self):
                     self.assertIsInstance(behavior, str)
                     self.assertIn(behavior, behavior_names)
 
+    def test_benchmark_config_reuses_scenario_context_for_one_behavior(self):
+        config_path = Path("examples/benchmark/eval_config.yaml")
+        raw = yaml.safe_load(config_path.read_text(encoding="utf-8"))
+        scenario = load_preset("scenario", "travel_planner_benchmark")
+
+        self.assertEqual(raw["context"].strip(), scenario["context"].strip())
+        self.assertEqual(
+            raw["behavior"],
+            {"preset": "explicit_constraint_violation_failures"},
+        )
+
 
 # ===================================================================
 # 2. CLI ``library list`` — table & JSON output, kind filtering, counts