feat: GenAI Client(evals): Add candidate name to local ADK agent scraping

PiperOrigin-RevId: 881533088

Author: vertex-sdk-bot

tests/unit/vertexai/genai/replays/test_evaluate_instances.py

@@ -180,7 +180,7 @@ def test_run_inference_with_agent(client):
         agent="projects/977012026409/locations/us-central1/reasoningEngines/7188347537655332864",
         src=test_df,
     )
-    assert inference_result.candidate_name is None
+    assert inference_result.candidate_name == "agent_engine_0"
     assert inference_result.gcs_source is None
 
 

tests/unit/vertexai/genai/test_evals.py

@@ -1236,7 +1236,7 @@ def test_run_inference_with_agent_engine_and_session_inputs_dict(
                 }
             ),
         )
-        assert inference_result.candidate_name is None
+        assert inference_result.candidate_name == "agent_engine_0"
         assert inference_result.gcs_source is None
 
     @mock.patch.object(_evals_utils, "EvalDatasetLoader")
@@ -1315,7 +1315,7 @@ def test_run_inference_with_agent_engine_and_session_inputs_literal_string(
                 }
             ),
         )
-        assert inference_result.candidate_name is None
+        assert inference_result.candidate_name == "agent_engine_0"
         assert inference_result.gcs_source is None
 
     @mock.patch.object(_evals_utils, "EvalDatasetLoader")
@@ -1499,7 +1499,7 @@ def run_async_side_effect(*args, **kwargs):
             ),
             expected_df.sort_values(by="prompt").reset_index(drop=True),
         )
-        assert inference_result.candidate_name is None
+        assert inference_result.candidate_name == "mock_agent"
         assert inference_result.gcs_source is None
 
     def test_run_inference_with_litellm_string_prompt_format(

vertexai/_genai/_evals_common.py

@@ -1143,6 +1143,13 @@ def _execute_inference(
             candidate_name=candidate_name,
         )
     elif agent_engine or agent:
+        candidate_name = None
+        if agent_engine:
+            candidate_name = "agent_engine_0"
+        elif agent:
+            agent_config = types.evals.AgentConfig.from_agent(agent)
+            candidate_name = agent_config.agent_id or "agent_0"
+
         if (
             agent_engine
             and not isinstance(agent_engine, str)
@@ -1181,6 +1188,7 @@ def _execute_inference(
 
         evaluation_dataset = types.EvaluationDataset(
             eval_dataset_df=results_df,
+            candidate_name=candidate_name,
         )
     else:
         raise ValueError("Either model, agent_engine or agent must be provided.")