fix(agentic): report exhausted responses clearly

aieng-forecasting/aieng/forecasting/methods/README.md

@@ -111,5 +111,5 @@ from aieng.forecasting.methods.agentic import (
 | `agentic/outputs.py` | `ContinuousAgentForecastOutput` | Canonical continuous forecasting output schema. Declares `modality = "continuous"`, requires one forecast per task horizon and the standard quantile grid, then converts to `ContinuousForecast` payloads. |
 | `agentic/outputs.py` | `DiscreteAgentForecastOutput` | Binary event output schema (`modality = "discrete"`): one probability plus `reasoning` / `key_signals` metadata, converted to a `BinaryForecast` payload. |
 | `agentic/outputs.py` | `CategoricalAgentForecastOutput` | Ordered-categorical output schema (`modality = "categorical"`): one `{label, probability}` row per task category, validated against `task.categories` and converted to a `CategoricalForecast` payload. |
-| `agentic/predictor.py` | `AgentPredictor` | Track 1 `Predictor` that builds prompts, runs an ADK agent through `AdkTextRunner`, validates structured JSON, and converts it to `Prediction` objects. Accepts an optional injected runner for tests or custom observability. |
+| `agentic/predictor.py` | `AgentPredictor` | Track 1 `Predictor` that builds prompts, runs an ADK agent through `AdkTextRunner`, validates structured JSON, and converts it to `Prediction` objects. Empty agent responses raise a descriptive error before JSON parsing. Accepts an optional injected runner for tests or custom observability. |
 | `agentic/predictor.py` | `ForecastPromptBuilder` | Protocol for task-specific prompt builders that turn `(task, context)` into the text passed to the agent. |

aieng-forecasting/aieng/forecasting/methods/agentic/predictor.py

@@ -287,6 +287,9 @@ def predict(self, task: ForecastingTask, context: ForecastContext) -> list[Predi
         # be swapped in without breaking the parse layer.
         output_str = strip_markdown_fence(output_str)
 
+        if not output_str.strip():
+            raise ValueError("Agent returned an empty response; the output token budget may have been exhausted")
+
         # Validate the output against the output schema; tolerate JSON
         # responses that ``model_validate_json`` cannot parse but
         # ``json.loads`` + ``model_validate`` can.

aieng-forecasting/tests/aieng/forecasting/methods/agentic/test_predictor.py

@@ -287,6 +287,13 @@ def fail_validate_json(*_args: Any, **_kwargs: Any) -> Any:
 class TestPredictErrorHandling:
     """``predict()`` swallows conversion errors but propagates schema errors."""
 
+    def test_empty_response_raises_descriptive_error(self) -> None:
+        """An exhausted agent response reports the agent failure, not a JSON error."""
+        predictor, _ = _make_predictor(response="  ")
+
+        with pytest.raises(ValueError, match="empty response"):
+            predictor.predict(_task([1]), _context())
+
     def test_horizon_mismatch_returns_empty_list_and_logs(self, caplog: pytest.LogCaptureFixture) -> None:
         """Output that validates but fails to_predictions yields ``[]`` and logs."""
         # Output covers horizon 1, task asks for [1, 2]; conversion will raise.

implementations/energy_oil_forecasting/README.md

@@ -112,6 +112,10 @@ notebook 05).
 | Role per task | `tasks.py` | Prompt builders, `build_wti_news_predictor(task)` |
 | Learning agent | `adaptive_agent/` | Persistent, mutable strategy state updated via self-directed study (notebooks 05–06) |
 
+The WTI code-execution preset reserves 32K output tokens for tool work and the
+final structured forecast. If an agent still returns no text, `AgentPredictor`
+reports an empty response directly instead of surfacing a JSON parsing error.
+
 ---
 
 ## Data Source & Setup

implementations/energy_oil_forecasting/analyst_agent/agent.py

@@ -488,7 +488,7 @@ def build_wti_news_config(
 def build_wti_code_exec_config(
     model: str = LITE_MODEL,
     search_model: str = LITE_MODEL,
-    max_output_tokens: int = 16_384,
+    max_output_tokens: int = 32_768,
     verifier_model: str = ADVANCED_MODEL,
     verifier_max_attempts: int = 3,
     verifier_confidence_threshold: int = 8,
@@ -511,11 +511,11 @@ def build_wti_code_exec_config(
         Model for the context-retrieval (web-search) sub-tool. Defaults to
         the lite model (``gemini-3.1-flash-lite-preview``) independently of ``model`` so that Gemini
         handles Google Search even when the analyst uses a different provider.
-    max_output_tokens : int, default=16_384
+    max_output_tokens : int, default=32_768
         Maximum tokens per model response.  The default is set well above
         LiteLLM's OpenAI-compatible endpoint default of 4096, which is not
-        enough for Claude to write a complete ``run_code`` Python script in a
-        single function call — causing repeated retries with empty arguments.
+        enough for an analyst to run tools and return the final structured
+        forecast in one response.
     verifier_model : str
         Model for the independent temporal-leakage verifier that audits each
         ``search_web`` result against ``cutoff_date`` before it is returned.

implementations/getting_started/concierge_agent/context/artifacts/implementations__energy_oil_forecasting__analyst_agent__agent.py.md

@@ -493,7 +493,7 @@ def build_wti_news_config(
 def build_wti_code_exec_config(
     model: str = LITE_MODEL,
     search_model: str = LITE_MODEL,
-    max_output_tokens: int = 16_384,
+    max_output_tokens: int = 32_768,
     verifier_model: str = ADVANCED_MODEL,
     verifier_max_attempts: int = 3,
     verifier_confidence_threshold: int = 8,
@@ -516,11 +516,11 @@ def build_wti_code_exec_config(
         Model for the context-retrieval (web-search) sub-tool. Defaults to
         the lite model (``gemini-3.1-flash-lite-preview``) independently of ``model`` so that Gemini
         handles Google Search even when the analyst uses a different provider.
-    max_output_tokens : int, default=16_384
+    max_output_tokens : int, default=32_768
         Maximum tokens per model response.  The default is set well above
         LiteLLM's OpenAI-compatible endpoint default of 4096, which is not
-        enough for Claude to write a complete ``run_code`` Python script in a
-        single function call — causing repeated retries with empty arguments.
+        enough for an analyst to run tools and return the final structured
+        forecast in one response.
     verifier_model : str
         Model for the independent temporal-leakage verifier that audits each
         ``search_web`` result against ``cutoff_date`` before it is returned.

implementations/tests/energy_oil_forecasting/test_analyst_agent.py

@@ -0,0 +1,10 @@
+"""Focused tests for the WTI analyst capability presets."""
+
+from energy_oil_forecasting.analyst_agent import build_wti_code_exec_config
+
+
+def test_code_execution_has_headroom_for_tool_use_and_final_output() -> None:
+    """Code execution must leave room for both sandbox work and forecast JSON."""
+    config = build_wti_code_exec_config()
+
+    assert config.max_output_tokens == 32_768