Add AI lab action guardrails

CHANGELOG.md

@@ -1008,3 +1008,11 @@ Added:
 - Segmentation readiness now shows raster dataset, model availability and tile manifest provenance state before submitting a run.
 - Added regression coverage for the AI Lab readiness UI contract and styling.
 - No API contracts, migrations, product capabilities, live provider fetching or AI/model dependency changes were introduced.
+
+## Sprint 104 AI Lab action guardrails (2026-06-24)
+
+- Added explicit action guardrails below Detection and Segmentation run-readiness panels.
+- Detection now distinguishes configured model state from UI-runnable state and blocks the explicit test/demo-only fixture detector in the normal run form.
+- Segmentation now distinguishes configured model state from UI-runnable state and blocks the explicit test/demo-only fixture segmenter in the normal run form.
+- Added regression coverage for AI Lab action guardrails and compact guardrail styling.
+- No API contracts, migrations, product capabilities, live provider fetching or AI/model dependency changes were introduced.

backend/tests/test_sprint104_ai_lab_action_guardrails.py

@@ -0,0 +1,40 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+
+ROOT = Path(__file__).resolve().parents[2]
+
+
+def test_detection_lab_distinguishes_configured_model_from_ui_runnable_action() -> None:
+    lab = (ROOT / "frontend" / "src" / "components" / "detection" / "DetectionLab.tsx").read_text(
+        encoding="utf-8"
+    )
+
+    assert "detectionModelUiRunnable" in lab
+    assert "selectedDetectionModelId !== 'manual-fixture-detector'" in lab
+    assert "detectionRunBlockedReason" in lab
+    assert "Fixture model is explicit test/demo-only" in lab
+    assert "Run action" in lab
+    assert "disabled={runningDetection || !detectionRunReady}" in lab
+
+
+def test_segmentation_lab_distinguishes_configured_model_from_ui_runnable_action() -> None:
+    lab = (ROOT / "frontend" / "src" / "components" / "segmentation" / "SegmentationLab.tsx").read_text(
+        encoding="utf-8"
+    )
+
+    assert "segmentationModelUiRunnable" in lab
+    assert "selectedSegmentationModelId !== 'fixture-segmenter'" in lab
+    assert "segmentationRunBlockedReason" in lab
+    assert "Fixture segmenter is explicit test/demo-only" in lab
+    assert "Run action" in lab
+    assert "disabled={runningSegmentation || !segmentationRunReady}" in lab
+
+
+def test_ai_lab_guardrail_styles_remain_compact() -> None:
+    css = (ROOT / "frontend" / "src" / "styles" / "app.css").read_text(encoding="utf-8")
+
+    assert ".lab-action-guardrail" in css
+    assert ".lab-action-guardrail-ready" in css
+    assert "overflow-wrap: anywhere;" in css

docs/CODEX_EXECUTION_LOG.md

@@ -3760,3 +3760,30 @@ Limitations:
 
 Next recommended pass:
 - Continue with V1 usability work that reduces operator confusion without expanding frozen product scope.
+
+## Sprint 104 AI Lab action guardrails (2026-06-24)
+
+Changed:
+- Added explicit action guardrails below the Detection Lab and Segmentation Lab run-readiness panels.
+- Detection now distinguishes `configured` model registry state from UI-runnable action state, blocking the explicit test/demo-only `manual-fixture-detector` in the normal workbench run form.
+- Segmentation now distinguishes `configured` model registry state from UI-runnable action state, blocking the explicit test/demo-only `fixture-segmenter` in the normal workbench run form.
+- Updated run button disabled conditions to use the new readiness/action state.
+- Added compact guardrail styling and regression coverage in `backend/tests/test_sprint104_ai_lab_action_guardrails.py`.
+- Updated `CHANGELOG.md` and `docs/TODO.md`.
+
+Tested:
+- Red step: `python -m pytest backend\tests\test_sprint104_ai_lab_action_guardrails.py -q` failed while the action guardrails and CSS contracts were absent.
+- `python -m pytest backend\tests\test_sprint104_ai_lab_action_guardrails.py -q` (`3 passed`)
+- `python -m pytest backend\tests\test_sprint103_ai_lab_run_readiness.py backend\tests\test_sprint104_ai_lab_action_guardrails.py -q` (`6 passed`)
+- `python -m compileall backend/app`
+- `cd frontend && npm run typecheck`
+- `cd frontend && npm run build`
+
+Open:
+- Full repository validation, commit, deploy and live browser verification are still pending for this pass.
+
+Limitations:
+- Frontend action-guardrail guidance only; no backend API, persistence, migration, provider fetching, AI dependency or model execution behavior changed.
+
+Next recommended pass:
+- After deploy validation, continue with V1 usability work that reduces operator confusion without expanding frozen product scope.

docs/TODO.md

@@ -370,3 +370,4 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Add useful default dataset context so Data, Map and Exports are immediately usable after project/demo load.
 - [x] Make raster tile handoff to Detection Lab auto-select the configured YOLO run form.
 - [x] Add AI Lab run-readiness checks for Detection and Segmentation before job submission.
+- [x] Add AI Lab action guardrails so explicit fixture models are not exposed as normal operator runs.

frontend/src/components/detection/DetectionLab.tsx

@@ -89,10 +89,24 @@ export function DetectionLab({
   const detectionHasDataset = selectedDetectionDatasetId.length > 0
   const detectionHasModel = selectedDetectionModel !== null
   const detectionModelReady = Boolean(selectedDetectionModel?.configured)
+  const detectionModelUiRunnable = detectionModelReady && selectedDetectionModelId !== 'manual-fixture-detector'
   const detectionHasTileManifest =
     !detectionRequiresTileManifest || detectionTileManifestPath.trim().length > 0
   const detectionRunReady =
-    Boolean(selectedProjectId) && detectionHasDataset && detectionHasModel && detectionModelReady && detectionHasTileManifest
+    Boolean(selectedProjectId) && detectionHasDataset && detectionHasModel && detectionModelUiRunnable && detectionHasTileManifest
+  const detectionRunBlockedReason = !selectedProjectId
+    ? 'Select or create a project first'
+    : !detectionHasDataset
+      ? 'Select a raster dataset'
+      : !detectionHasModel
+        ? 'Select a detection model'
+        : selectedDetectionModelId === 'manual-fixture-detector'
+          ? 'Fixture model is explicit test/demo-only'
+          : !detectionModelReady
+            ? selectedDetectionModel?.limitation_message ?? 'Selected model is not configured'
+            : !detectionHasTileManifest
+              ? 'Provide a raster tile manifest for configured YOLO'
+              : null
 
   return (
     <section className="workspace-panel ai-lab-shell detection-lab-shell">
@@ -191,6 +205,10 @@ export function DetectionLab({
               </div>
             </div>
           </div>
+          <div className={detectionRunReady ? 'lab-action-guardrail lab-action-guardrail-ready' : 'lab-action-guardrail'}>
+            <span>Run action</span>
+            <strong>{detectionRunReady ? 'Ready to submit a detection job' : detectionRunBlockedReason}</strong>
+          </div>
           {rasterDatasets.length === 0 ? (
             <div className="result-state result-state-empty">
               <strong>No raster datasets available for detection.</strong>
@@ -242,7 +260,7 @@ export function DetectionLab({
               />
             </label>
           ) : null}
-          <button className="primary-action" type="button" onClick={onRunDetection} disabled={runningDetection || !selectedProjectId || rasterDatasets.length === 0}>
+          <button className="primary-action" type="button" onClick={onRunDetection} disabled={runningDetection || !detectionRunReady}>
             Run detection
           </button>
         </div>

frontend/src/components/segmentation/SegmentationLab.tsx

@@ -90,8 +90,19 @@ export function SegmentationLab({
 }: SegmentationLabProps): JSX.Element {
   const segmentationHasDataset = selectedSegmentationDatasetId.length > 0
   const segmentationHasTileManifest = segmentationTileManifestPath.trim().length > 0
+  const segmentationModelUiRunnable =
+    selectedSegmentationModelConfigured && selectedSegmentationModelId !== 'fixture-segmenter'
   const segmentationRunReady =
-    Boolean(selectedProjectId) && segmentationHasDataset && selectedSegmentationModelConfigured
+    Boolean(selectedProjectId) && segmentationHasDataset && segmentationModelUiRunnable
+  const segmentationRunBlockedReason = !selectedProjectId
+    ? 'Select or create a project first'
+    : !segmentationHasDataset
+      ? 'Select a raster dataset'
+      : selectedSegmentationModelId === 'fixture-segmenter'
+        ? 'Fixture segmenter is explicit test/demo-only'
+        : !selectedSegmentationModelConfigured
+          ? selectedSegmentationModelLimitation ?? 'Selected segmentation model is not configured'
+          : null
 
   return (
     <section className="workspace-panel ai-lab-shell segmentation-lab-shell">
@@ -184,6 +195,10 @@ export function SegmentationLab({
               </div>
             </div>
           </div>
+          <div className={segmentationRunReady ? 'lab-action-guardrail lab-action-guardrail-ready' : 'lab-action-guardrail'}>
+            <span>Run action</span>
+            <strong>{segmentationRunReady ? 'Ready to submit a segmentation job' : segmentationRunBlockedReason}</strong>
+          </div>
           {rasterDatasets.length === 0 ? (
             <div className="result-state result-state-empty">
               <strong>No raster datasets available for segmentation.</strong>
@@ -237,7 +252,7 @@ export function SegmentationLab({
             className="primary-action"
             type="button"
             onClick={onRunSegmentation}
-            disabled={runningSegmentation || !selectedProjectId || rasterDatasets.length === 0 || !selectedSegmentationModelConfigured}
+            disabled={runningSegmentation || !segmentationRunReady}
           >
             Run segmentation
           </button>

frontend/src/styles/app.css

@@ -2719,6 +2719,40 @@ button.entity-card {
   overflow-wrap: anywhere;
 }
 
+.lab-action-guardrail {
+  display: flex;
+  min-width: 0;
+  align-items: center;
+  justify-content: space-between;
+  gap: 0.6rem;
+  border: 1px solid #d8e3de;
+  border-radius: 8px;
+  padding: 0.52rem 0.62rem;
+  background: #ffffff;
+}
+
+.lab-action-guardrail-ready {
+  border-color: #b8dcc9;
+  background: #f8fff9;
+}
+
+.lab-action-guardrail span {
+  flex: 0 0 auto;
+  color: var(--muted);
+  font-size: 0.72rem;
+  font-weight: 700;
+  text-transform: uppercase;
+}
+
+.lab-action-guardrail strong {
+  min-width: 0;
+  color: var(--text);
+  font-size: 0.88rem;
+  line-height: 1.28;
+  text-align: right;
+  overflow-wrap: anywhere;
+}
+
 .ai-lab-shell .model-list {
   grid-template-columns: repeat(auto-fit, minmax(12.5rem, 1fr));
   gap: 0.55rem;