A batch flow's input is a run parameter, not a missing value

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01UytviPMJbXzD8P84nLvXcq

backend/fluksio/flow/controller.py

@@ -798,7 +798,7 @@ class FlowController:
         nodes: list[Node] = []
         loaded: dict[str, LoadedNode] = {}
         initial_values: dict[str, Any] = {}
-        # Declared flow inputs, mapped to whether they start with a value.
+        # Declared flow inputs, mapped to whether a value will be there.
         flow_inputs: dict[str, bool] = {}
 
         for flow, draft in flows:
@@ -1465,14 +1465,22 @@ def with_settings(
 
 
 def _declared_inputs(flow: FlowDef) -> tuple[dict[str, bool], dict[str, Any]]:
-    """A flow's declared inputs, and the ones that start with a value."""
+    """A flow's declared inputs, and the ones that start with a value.
+
+    An input maps to whether a value will be there when the flow starts, which
+    for a batch flow is not the same as carrying an initial: it is started
+    explicitly and its inputs arrive with the run, so one without an initial is
+    a parameter the caller fills rather than a message nothing ever sets.
+    Reporting it as the latter had the health summary count an experiment as a
+    flow that cannot run while the Runs screen showed it running.
+    """
     declared: dict[str, bool] = {}
     initial: dict[str, Any] = {}
     for flow_input in flow.inputs:
         name = qualify(flow.name, flow_input.spec.name)
         if not name:
             continue
-        declared[name] = flow_input.initial is not None
+        declared[name] = flow_input.initial is not None or flow.mode == "batch"
         if flow_input.initial is not None:
             initial[name] = flow_input.initial
     return declared, initial

backend/fluksio/flow/pipeline.py

@@ -447,7 +447,8 @@ class Pipeline:
         """Report everything that would keep this graph from running.
 
         :param flow_inputs: Messages declared as inputs of a flow rather than
-            computed by it, mapped to whether they carry an initial value.
+            computed by it, mapped to whether a value will be there when the
+            flow starts — from an initial, or from the run that starts it.
         """
         declared = flow_inputs or {}
         issues: list[ValidationIssue] = []

backend/tests/flow/test_pipeline.py

@@ -1,9 +1,11 @@
 """The wiring fundamentals: name binding, fan-in, namespaces, validation."""
 
+from fluksio.flow.controller import _declared_inputs
 from fluksio.flow.events import EventBus
 from fluksio.flow.messages import DType, MessageSpec
 from fluksio.flow.nodes import Node
 from fluksio.flow.pipeline import Pipeline
+from fluksio.flow.schemas import FlowDef, FlowInput
 
 
 def spec(name: str, dtype: DType = DType.FLOAT, port: str = "") -> MessageSpec:
@@ -131,6 +133,25 @@ def test_a_flow_input_without_a_starting_value_is_reported():
     assert [issue.code for issue in issues] == ["missing_initial_value"]
 
 
+def test_a_batch_flows_input_is_a_run_parameter_not_a_missing_value():
+    """A flow that only runs when asked gets its inputs from the run.
+
+    Calling that a message nothing ever sets had the health summary count a
+    training flow as one that cannot run while the Runs screen showed it
+    running. A live flow, which nothing is going to start on its own, still
+    reports it.
+    """
+    node = make_node(
+        "n", "f", lambda setpoint, params: None, requires=[spec("setpoint")]
+    )
+    batch = FlowDef(name="f", mode="batch", inputs=[FlowInput(spec=spec("setpoint"))])
+    live = batch.model_copy(update={"mode": "live"})
+
+    assert Pipeline(nodes=[node]).validate(_declared_inputs(batch)[0]) == []
+    issues = Pipeline(nodes=[node]).validate(_declared_inputs(live)[0])
+    assert [issue.code for issue in issues] == ["missing_initial_value"]
+
+
 def test_a_failing_node_does_not_stop_its_siblings():
     ran = []