feat(guardrails): forward agent_input to deterministic pre-evaluation [AL-410]

src/uipath_langchain/agent/guardrails/guardrail_nodes.py

@@ -4,6 +4,7 @@
 from typing import Any, Callable
 
 from langgraph.types import Command
+from pydantic import BaseModel
 from uipath.core.guardrails import (
     DeterministicGuardrail,
     DeterministicGuardrailsService,
@@ -26,18 +27,35 @@
     get_message_content,
 )
 from uipath_langchain.agent.react.types import AgentGuardrailsGraphState
+from uipath_langchain.agent.react.utils import extract_input_data_from_state
 
 from ..exceptions import AgentRuntimeError, AgentRuntimeErrorCode
 
 logger = logging.getLogger(__name__)
 
 
+def _resolve_agent_input(
+    state: AgentGuardrailsGraphState,
+    input_schema: type[BaseModel] | None,
+) -> dict[str, Any] | None:
+    if input_schema is None:
+        return None
+    try:
+        return extract_input_data_from_state(state, input_schema)
+    except Exception:
+        # The state may not yet carry agent-input fields (e.g., very early
+        # subgraphs, or schemas whose required fields aren't seeded yet); fall
+        # back to "no agent_input available" rather than crashing the run.
+        return None
+
+
 def _evaluate_deterministic_guardrail(
     state: AgentGuardrailsGraphState,
     guardrail: DeterministicGuardrail,
     execution_stage: ExecutionStage,
     input_data_extractor: Callable[[AgentGuardrailsGraphState], dict[str, Any]],
     output_data_extractor: Callable[[AgentGuardrailsGraphState], dict[str, Any]] | None,
+    input_schema: type[BaseModel] | None = None,
 ):
     """Evaluate deterministic guardrail.
 
@@ -47,6 +65,10 @@ def _evaluate_deterministic_guardrail(
         execution_stage: The execution stage (PRE_EXECUTION or POST_EXECUTION).
         input_data_extractor: Function to extract input data from state.
         output_data_extractor: Function to extract output data from state (optional).
+        input_schema: Optional input schema; when provided, the agent's
+            validated input parameters are extracted from state and passed to
+            pre-execution evaluation so rules can reference
+            ``FieldSource.AGENT_INPUT``.
 
     Returns:
         The guardrail evaluation result.
@@ -56,7 +78,9 @@ def _evaluate_deterministic_guardrail(
 
     if execution_stage == ExecutionStage.PRE_EXECUTION:
         return service.evaluate_pre_deterministic_guardrail(
-            input_data=input_data, guardrail=guardrail
+            input_data=input_data,
+            guardrail=guardrail,
+            agent_input=_resolve_agent_input(state, input_schema),
         )
     else:  # POST_EXECUTION
         output_data = output_data_extractor(state) if output_data_extractor else {}
@@ -150,6 +174,7 @@ def _create_guardrail_node(
     | None = None,
     tool_name: str | None = None,
     tool_type: str | None = None,
+    input_schema: type[BaseModel] | None = None,
 ) -> tuple[str, Callable[[AgentGuardrailsGraphState], Any]]:
     """Private factory for guardrail evaluation nodes.
 
@@ -195,6 +220,7 @@ async def node(
                     execution_stage,
                     input_data_extractor,
                     output_data_extractor,
+                    input_schema,
                 )
             elif isinstance(guardrail, BuiltInValidatorGuardrail):
                 # Generate and store payload for observability
@@ -314,6 +340,7 @@ def create_tool_guardrail_node(
     failure_node: str,
     tool_name: str,
     tool_type: str | None = None,
+    input_schema: type[BaseModel] | None = None,
 ) -> tuple[str, Callable[[AgentGuardrailsGraphState], Any]]:
     """Create a guardrail node for TOOL scope guardrails.
 
@@ -324,6 +351,8 @@ def create_tool_guardrail_node(
         failure_node: Node to route to on validation fail.
         tool_name: Name of the tool to extract arguments from.
         tool_type: Optional type of the tool (e.g., "process", "escalation", "mcp").
+        input_schema: Optional agent input schema; enables rules to reference
+            ``FieldSource.AGENT_INPUT`` during pre-execution evaluation.
 
     Returns:
         A tuple of (node_name, node_function) for the guardrail evaluation node.
@@ -375,4 +404,5 @@ def _output_data_extractor(state: AgentGuardrailsGraphState) -> dict[str, Any]:
         _output_data_extractor,
         tool_name,
         tool_type,
+        input_schema,
     )

src/uipath_langchain/agent/react/guardrails/guardrails_subgraph.py

@@ -452,7 +452,10 @@ def create_tool_guardrails_subgraph(
         scope=GuardrailScope.TOOL,
         execution_stages=[ExecutionStage.PRE_EXECUTION, ExecutionStage.POST_EXECUTION],
         node_factory=partial(
-            create_tool_guardrail_node, tool_name=tool_name, tool_type=tool_type
+            create_tool_guardrail_node,
+            tool_name=tool_name,
+            tool_type=tool_type,
+            input_schema=input_schema,
         ),
         input_schema=input_schema,
     )

tests/agent/guardrails/test_guardrail_nodes.py

@@ -498,7 +498,7 @@ async def test_evaluate_deterministic_guardrail_pre_execution(self, monkeypatch)
 
         assert result.result == GuardrailValidationResultType.PASSED
         mock_service.evaluate_pre_deterministic_guardrail.assert_called_once_with(
-            input_data={"test": "data"}, guardrail=guardrail
+            input_data={"test": "data"}, guardrail=guardrail, agent_input=None
         )
 
     @pytest.mark.asyncio
@@ -544,6 +544,110 @@ async def test_evaluate_deterministic_guardrail_post_execution(self, monkeypatch
             guardrail=guardrail,
         )
 
+    @pytest.mark.asyncio
+    async def test_evaluate_deterministic_guardrail_pre_passes_agent_input_when_schema_supplied(
+        self, monkeypatch
+    ):
+        """When input_schema is supplied, the agent input dict is extracted from
+        state and forwarded to evaluate_pre_deterministic_guardrail."""
+        from pydantic import BaseModel
+        from uipath.core.guardrails import DeterministicGuardrail
+
+        from uipath_langchain.agent.guardrails.guardrail_nodes import (
+            _evaluate_deterministic_guardrail,
+        )
+        from uipath_langchain.agent.react.utils import (
+            create_guardrails_state_with_input,
+        )
+
+        class AgentInputSchema(BaseModel):
+            user_identity: str
+            role: str
+
+        mock_result = GuardrailValidationResult(
+            result=GuardrailValidationResultType.PASSED, reason=""
+        )
+        mock_service = MagicMock()
+        mock_service.evaluate_pre_deterministic_guardrail.return_value = mock_result
+        monkeypatch.setattr(
+            "uipath_langchain.agent.guardrails.guardrail_nodes.DeterministicGuardrailsService",
+            lambda: mock_service,
+        )
+
+        guardrail = MagicMock(spec=DeterministicGuardrail)
+        state_cls = create_guardrails_state_with_input(AgentInputSchema)
+        state = state_cls(messages=[], user_identity="U157877", role="admin")
+        input_extractor = MagicMock(return_value={"target_user_id": "U157878"})
+        output_extractor = MagicMock()
+
+        result = _evaluate_deterministic_guardrail(
+            state,
+            guardrail,
+            ExecutionStage.PRE_EXECUTION,
+            input_extractor,
+            output_extractor,
+            AgentInputSchema,
+        )
+
+        assert result.result == GuardrailValidationResultType.PASSED
+        mock_service.evaluate_pre_deterministic_guardrail.assert_called_once_with(
+            input_data={"target_user_id": "U157878"},
+            guardrail=guardrail,
+            agent_input={"user_identity": "U157877", "role": "admin"},
+        )
+
+    @pytest.mark.asyncio
+    async def test_evaluate_deterministic_guardrail_post_does_not_pass_agent_input(
+        self, monkeypatch
+    ):
+        """Even with input_schema supplied, post-execution does NOT receive
+        agent_input — agent-input rules are pre-execution only by design."""
+        from pydantic import BaseModel
+        from uipath.core.guardrails import DeterministicGuardrail
+
+        from uipath_langchain.agent.guardrails.guardrail_nodes import (
+            _evaluate_deterministic_guardrail,
+        )
+        from uipath_langchain.agent.react.utils import (
+            create_guardrails_state_with_input,
+        )
+
+        class AgentInputSchema(BaseModel):
+            user_identity: str
+
+        mock_service = MagicMock()
+        mock_service.evaluate_post_deterministic_guardrail.return_value = (
+            GuardrailValidationResult(
+                result=GuardrailValidationResultType.PASSED, reason=""
+            )
+        )
+        monkeypatch.setattr(
+            "uipath_langchain.agent.guardrails.guardrail_nodes.DeterministicGuardrailsService",
+            lambda: mock_service,
+        )
+
+        guardrail = MagicMock(spec=DeterministicGuardrail)
+        state_cls = create_guardrails_state_with_input(AgentInputSchema)
+        state = state_cls(messages=[], user_identity="U157877")
+
+        _evaluate_deterministic_guardrail(
+            state,
+            guardrail,
+            ExecutionStage.POST_EXECUTION,
+            MagicMock(return_value={"input": "data"}),
+            MagicMock(return_value={"output": "data"}),
+            AgentInputSchema,
+        )
+
+        mock_service.evaluate_post_deterministic_guardrail.assert_called_once_with(
+            input_data={"input": "data"},
+            output_data={"output": "data"},
+            guardrail=guardrail,
+        )
+        # agent_input must NOT be present in the post call
+        kwargs = mock_service.evaluate_post_deterministic_guardrail.call_args.kwargs
+        assert "agent_input" not in kwargs
+
     @pytest.mark.asyncio
     async def test_evaluate_builtin_guardrail(self, monkeypatch):
         """Test built-in guardrail evaluation."""