Merge pull request 'ACT-001: actuator-first sensor assignment and lifecycle' (#31) from feature/actuator-sensor-lifecycle into main

2026-06-13 22:47:14 +02:00
parent 7ed667f954 6305f52cd2
commit 685feb57b3
30 changed files with 2010 additions and 103 deletions
--- a/.env.example
+++ b/.env.example
@@ -2,3 +2,8 @@ SILLYHOME_HA_URL=http://homeassistant.local:8123
 SILLYHOME_HA_TOKEN=REPLACE_ME_WITH_LONG_LIVED_TOKEN
 SILLYHOME_MODEL_STORE=.model_store
 SILLYHOME_AUTOMATION_STORE=.automation_store
+SILLYHOME_ACTUATOR_STORE=.actuator_store
+SILLYHOME_HISTORY_DAYS=14
+SILLYHOME_MIN_TRAINING_POINTS=24
+SILLYHOME_RETRAIN_STALE_HOURS=24
+SILLYHOME_RECONCILE_INTERVAL_SECONDS=900
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,8 +1,11 @@
 # Changelog

-## Unreleased
- Deterministische, nutzerverständliche Erklärungen für jede Modellvorhersage
- Persistenter Automation-Freigabeprozess mit sicherem YAML-Export
+## 0.4.0 - 2026-06-13
+- Aktuator-zentrierte Einrichtung: Nutzer wählen nur noch Aktuatoren, Sensoren werden deterministisch gefunden und bewertet
+- Persistente automatische und manuelle Sensorzuordnungen mit Evidenz, Confidence, Review-Gating und Neustart-Sicherheit
+- Autonomer Modell-Lebenszyklus auf echter HA-Historie: Training, Retraining bei Staleness oder Datenänderung, Archivierung von Waisen
+- Neues Dashboard und API für Aktuatorauswahl, Reconciliation, Overrides, Modellstatus und Audit-Trail
+- Neue Container-/Add-on-Defaults für Aktuator-Store und periodische Reconciliation ohne zusätzliche Gerätesteuerung

 ## 0.2.0 - 2026-06-13
 - Klassifizierte Home-Assistant-Entity-Discovery mit Lernrelevanz und Filtern
--- a/9
+++ b/9
@@ -4,7 +4,12 @@ ENV PYTHONDONTWRITEBYTECODE=1 \
    PYTHONUNBUFFERED=1 \
    PIP_NO_CACHE_DIR=1 \
    SILLYHOME_MODEL_STORE=/app/data/models
-ENV SILLYHOME_AUTOMATION_STORE=/app/data/automations
+ENV SILLYHOME_AUTOMATION_STORE=/app/data/automations \
+    SILLYHOME_ACTUATOR_STORE=/app/data/actuators \
+    SILLYHOME_HISTORY_DAYS=14 \
+    SILLYHOME_MIN_TRAINING_POINTS=24 \
+    SILLYHOME_RETRAIN_STALE_HOURS=24 \
+    SILLYHOME_RECONCILE_INTERVAL_SECONDS=900

 WORKDIR /app

@@ -15,7 +20,7 @@ COPY app ./app
 COPY backend ./backend
 RUN python -m pip install --upgrade pip && \
    python -m pip install . && \
-    mkdir -p /app/data/models /app/data/automations && \
+    mkdir -p /app/data/models /app/data/automations /app/data/actuators && \
    chown -R sillyhome:sillyhome /app/data

 EXPOSE 8000
--- a/README.md
+++ b/README.md
@@ -4,11 +4,10 @@ Lokaler, datenschutzfreundlicher API-Prototyp für Home Assistant.

 ## Reifegrad

-Die aktuelle Entwicklungslinie stellt eine gehärtete technische Basis bereit:
-Home-Assistant-Entities und Historie lesen, Sensoren klassifizieren,
-regelbasierte Bausteine sowie ein lokal trainierbares statistisches
-Baseline-Modell mit persistenter Registry, Confidence und echten
-Evaluationsmetriken.
+Die aktuelle Entwicklungslinie ist aktor-zentriert: Nutzer konfigurieren nur
+noch Home-Assistant-Aktuatoren. SillyHome Next findet dazu passende numerische
+Sensoren und Kontext-Entities, zeigt Evidenz und Review-Bedarf an und hält
+passende Modelle lokal und autonom aktuell.

 ## Motivation
 TheSillyHome zeigte die Idee: statt statischer Regeln das Zuhause aus Verhaltensmustern verstehen. Diese Architektur modernisiert den Ansatz in Richtung Explainable AI, hybride Intelligenzebenen und langlebige Wartbarkeit.
@@ -46,6 +45,9 @@ uvicorn app.main:app --reload
 - `http://127.0.0.1:8000/v1/entities` - Home-Assistant-Entities
 - `http://127.0.0.1:8000/v1/discovery` - klassifizierte, filterbare Entities
 - `http://127.0.0.1:8000/v1/history` - normalisierte numerische Zeitreihen
+- `http://127.0.0.1:8000/v1/actuators/discovery` - unterstützte Aktuatoren für den aktor-zentrierten Workflow
+- `POST http://127.0.0.1:8000/v1/actuators` - Aktuator registrieren, Sensorzuordnung prüfen und Modell-Lebenszyklus starten
+- `POST http://127.0.0.1:8000/v1/actuators/reconciliation/run` - globale Reconciliation manuell anstoßen
 - `http://127.0.0.1:8000/ml/health` - Registry-/Serving-Health
 - `POST http://127.0.0.1:8000/ml/retrain` - Modell-Metadaten aktualisieren
 - `POST http://127.0.0.1:8000/ml/evaluate` - MAE/RMSE/Coverage berechnen
@@ -69,6 +71,11 @@ dem Netz muss ein authentifizierender Reverse Proxy vorgeschaltet werden.
 - `SILLYHOME_HA_TOKEN` – Long-Lived Access Token eines dedizierten HA-Benutzers mit minimalen Rechten
 - `SILLYHOME_MODEL_STORE` – Verzeichnis für persistierte Modell-Metadaten
 - `SILLYHOME_AUTOMATION_STORE` – Verzeichnis für Automation-Entwürfe
+- `SILLYHOME_ACTUATOR_STORE` – Verzeichnis für persistente Aktuator-Zuordnungen, Overrides und Reconciliation-Status
+- `SILLYHOME_HISTORY_DAYS` – Trainingsfenster für HA-History (1 bis 31 Tage)
+- `SILLYHOME_MIN_TRAINING_POINTS` – Mindestanzahl nutzbarer numerischer Messpunkte vor einem Modelltraining
+- `SILLYHOME_RETRAIN_STALE_HOURS` – Staleness-Grenze für automatisches Retraining
+- `SILLYHOME_RECONCILE_INTERVAL_SECONDS` – Intervall für sichere periodische Reconciliation

 Niemals Administrator-Tokens oder Passwörter eintragen. `.env` gehört nicht ins
 Versionskontrollsystem.
@@ -84,6 +91,13 @@ Danach **SillyHome Next** installieren und starten. Das Dashboard wird per Ingre
 geöffnet. Das Add-on nutzt die Supervisor-API nur lesend; Automation-Entwürfe werden
 lokal gespeichert und niemals automatisch ausgeführt.

+### Normaler Workflow
+1. Im Dashboard oder per API einen Aktuator auswählen, zum Beispiel `light.abstellkammer`.
+2. SillyHome Next bewertet passende numerische Sensoren und binäre Kontext-Entities anhand von Bereich, Gerät, Namen, Domain und `device_class`.
+3. Starke und eindeutige Zuordnungen werden automatisch genutzt; schwache oder knappe Kandidaten bleiben mit Review-Hinweis sichtbar.
+4. Manuelle Overrides haben Vorrang, bleiben persistent und überstehen Neustarts.
+5. Sobald genügend numerische HA-Historie vorhanden ist, trainiert das System automatisch ein lokales Modell pro Aktuator-Zuordnung und retrainiert es bei relevanten Datenänderungen oder Staleness.
+
 Vor einem Update sollte in Home Assistant unter **Einstellungen → System → Backups**
 eine Teil-Sicherung des Add-ons erstellt werden. Zur Wiederherstellung das gewünschte
 Backup öffnen, **SillyHome Next** auswählen und wiederherstellen. Der erste produktive
--- a/addon/config.yaml
+++ b/addon/config.yaml
@@ -1,5 +1,5 @@
 name: SillyHome Next
-version: "0.3.0"
+version: "0.4.0"
 slug: sillyhome_next
 description: Lokale HA-Analyse, Vorhersagen und sichere Automation-Entwürfe
 url: http://192.168.6.31:3000/pino/sillyhome-next
@@ -16,8 +16,16 @@ panel_admin: true
 homeassistant_api: true
 hassio_api: false
 auth_api: false
-options: {}
-schema: {}
+options:
+  history_days: 14
+  min_training_points: 24
+  retrain_stale_hours: 24
+  reconcile_interval_seconds: 900
+schema:
+  history_days: "int(1,31)"
+  min_training_points: "int(2,10000)"
+  retrain_stale_hours: "int(1,720)"
+  reconcile_interval_seconds: "int(60,86400)"
 map:
  - type: addon_config
    read_only: false
--- a/addon/run.sh
+++ b/addon/run.sh
@@ -5,7 +5,15 @@ export SILLYHOME_HA_URL="${SILLYHOME_HA_URL:-http://supervisor/core}"
 export SILLYHOME_HA_TOKEN="${SILLYHOME_HA_TOKEN:-${SUPERVISOR_TOKEN:-}}"
 export SILLYHOME_MODEL_STORE=/data/models
 export SILLYHOME_AUTOMATION_STORE=/data/automations
+export SILLYHOME_ACTUATOR_STORE=/data/actuators

-mkdir -p "$SILLYHOME_MODEL_STORE" "$SILLYHOME_AUTOMATION_STORE"
+if [ -f /data/options.json ]; then
+  export SILLYHOME_HISTORY_DAYS="$(python -c 'import json; print(json.load(open("/data/options.json")).get("history_days", 14))')"
+  export SILLYHOME_MIN_TRAINING_POINTS="$(python -c 'import json; print(json.load(open("/data/options.json")).get("min_training_points", 24))')"
+  export SILLYHOME_RETRAIN_STALE_HOURS="$(python -c 'import json; print(json.load(open("/data/options.json")).get("retrain_stale_hours", 24))')"
+  export SILLYHOME_RECONCILE_INTERVAL_SECONDS="$(python -c 'import json; print(json.load(open("/data/options.json")).get("reconcile_interval_seconds", 900))')"
+fi
+
+mkdir -p "$SILLYHOME_MODEL_STORE" "$SILLYHOME_AUTOMATION_STORE" "$SILLYHOME_ACTUATOR_STORE"
 exec uvicorn app.main:app --app-dir /app --host 0.0.0.0 --port 8000 \
  --proxy-headers --forwarded-allow-ips='*'
--- a/app/actuators/init.py
+++ b/app/actuators/init.py
@@ -0,0 +1,27 @@
+from app.actuators.lifecycle import (
+    ActuatorReconciliationService,
+)
+from app.actuators.models import (
+    ActuatorRecord,
+    AssignmentCandidate,
+    AssignmentSelection,
+    LifecycleAuditEntry,
+    LifecycleStatus,
+    ManualOverride,
+    ReconciliationState,
+    model_id_for_actuator,
+)
+from app.actuators.store import ActuatorStore
+
+__all__ = [
+    "ActuatorReconciliationService",
+    "ActuatorRecord",
+    "ActuatorStore",
+    "AssignmentCandidate",
+    "AssignmentSelection",
+    "LifecycleAuditEntry",
+    "LifecycleStatus",
+    "ManualOverride",
+    "ReconciliationState",
+    "model_id_for_actuator",
+]
--- a/app/actuators/lifecycle.py
+++ b/app/actuators/lifecycle.py
@@ -0,0 +1,607 @@
+from __future__ import annotations
+
+import hashlib
+import logging
+import re
+from collections.abc import Iterable
+from datetime import datetime, timedelta, timezone
+
+from app.actuators.models import (
+    ActuatorRecord,
+    AssignmentCandidate,
+    AssignmentSelection,
+    AssignmentSource,
+    LifecycleAuditEntry,
+    LifecycleStatus,
+    ManualOverride,
+    ModelLifecycleState,
+    ReconciliationState,
+    model_id_for_actuator,
+)
+from app.actuators.store import ActuatorStore
+from app.config import Settings
+from app.ha.discovery import DiscoveredEntity, EntityRole
+from app.ha.history import EntityHistorySeries, NumericHistoryPoint
+from app.ha.models import HaEntitySummary
+from app.ha.reader import HaReader
+from app.ml.feature_store import FeatureVector
+from app.ml.registry.model_registry import ModelRegistry
+from app.ml.retraining import retrain_model
+from app.ml.training import TrainedArtifact
+
+logger = logging.getLogger(__name__)
+
+_TOKEN_PATTERN = re.compile(r"[a-z0-9]+", re.IGNORECASE)
+_STOPWORDS = frozenset(
+    {
+        "actuator",
+        "battery",
+        "bin",
+        "binary",
+        "brightness",
+        "current",
+        "door",
+        "energy",
+        "entity",
+        "humidity",
+        "illuminance",
+        "light",
+        "power",
+        "sensor",
+        "state",
+        "switch",
+        "temperature",
+        "value",
+    }
+)
+_NUMERIC_AUTO_ACCEPT_SCORE = 0.82
+_NUMERIC_MIN_MARGIN = 0.18
+_CONTEXT_AUTO_ACCEPT_SCORE = 0.78
+_MAX_CONTEXT_SELECTIONS = 3
+_AUDIT_LIMIT = 20
+
+
+class ActuatorReconciliationService:
+    def __init__(
+        self,
+        *,
+        ha_reader: HaReader,
+        store: ActuatorStore,
+        registry: ModelRegistry,
+        settings: Settings,
+    ) -> None:
+        self._ha_reader = ha_reader
+        self._store = store
+        self._registry = registry
+        self._settings = settings
+
+    def list_configured(self) -> list[ActuatorRecord]:
+        return self._store.list()
+
+    def configure_actuator(self, actuator_entity_id: str, *, enabled: bool = True) -> ActuatorRecord:
+        self._store.configure(actuator_entity_id, enabled=enabled)
+        return self.reconcile_actuator(actuator_entity_id, trigger="configuration")
+
+    def get_actuator(self, actuator_entity_id: str) -> ActuatorRecord:
+        return self._store.get(actuator_entity_id)
+
+    def set_override(
+        self,
+        actuator_entity_id: str,
+        override: ManualOverride | None,
+    ) -> ActuatorRecord:
+        record = self._store.get(actuator_entity_id)
+        updated = record.model_copy(
+            update={
+                "manual_override": override,
+                "updated_at": datetime.now(timezone.utc),
+            }
+        )
+        self._store.upsert(updated)
+        return self.reconcile_actuator(actuator_entity_id, trigger="override")
+
+    def delete_actuator(self, actuator_entity_id: str) -> None:
+        model_id = model_id_for_actuator(actuator_entity_id)
+        self._registry.archive(model_id)
+        self._store.delete(actuator_entity_id)
+
+    def reconcile_all(self, trigger: str = "manual") -> ReconciliationState:
+        state = self._store.load_reconciliation_state().model_copy(
+            update={
+                "running": True,
+                "last_started_at": datetime.now(timezone.utc),
+                "last_trigger": trigger,
+            }
+        )
+        self._store.save_reconciliation_state(state)
+        records = self._store.list()
+        for record in records:
+            self.reconcile_actuator(record.actuator_entity_id, trigger=trigger)
+        self._archive_orphan_models({model_id_for_actuator(record.actuator_entity_id) for record in records})
+        refreshed = self._store.list()
+        summary = ReconciliationState(
+            last_started_at=state.last_started_at,
+            last_completed_at=datetime.now(timezone.utc),
+            last_trigger=trigger,
+            running=False,
+            configured_actuators=len(refreshed),
+            review_required=sum(1 for record in refreshed if record.assignment.review_required),
+            trained_models=sum(
+                1 for record in refreshed if record.lifecycle.status is LifecycleStatus.TRAINED
+            ),
+            last_summary=(
+                f"{len(refreshed)} Aktuatoren geprüft, "
+                f"{sum(1 for record in refreshed if record.assignment.review_required)} "
+                "mit Prüfbedarf."
+            ),
+        )
+        self._store.save_reconciliation_state(summary)
+        return summary
+
+    def reconcile_actuator(self, actuator_entity_id: str, trigger: str = "manual") -> ActuatorRecord:
+        now = datetime.now(timezone.utc)
+        record = self._store.get(actuator_entity_id)
+        entities = {entity.entity_id: entity for entity in self._ha_reader.read_entities()}
+        discovered = {entity.entity_id: entity for entity in self._ha_reader.discover()}
+        actuator = entities.get(actuator_entity_id)
+        descriptor = discovered.get(actuator_entity_id)
+        lifecycle = record.lifecycle.model_copy(update={"last_reconciled_at": now})
+
+        if not record.enabled:
+            lifecycle = self._archive_state(
+                lifecycle,
+                "Aktuator ist deaktiviert; Modell bleibt archiviert.",
+                now=now,
+            )
+            updated = record.model_copy(
+                update={
+                    "assignment": AssignmentSelection(
+                        selected_numeric_entity_id=None,
+                        selected_context_entity_ids=[],
+                        source=AssignmentSource.NONE,
+                        confidence=0.0,
+                        review_required=False,
+                        reason="Aktuator ist deaktiviert.",
+                    ),
+                    "numeric_candidates": [],
+                    "context_candidates": [],
+                    "lifecycle": lifecycle,
+                    "updated_at": now,
+                }
+            )
+            return self._store.upsert(updated)
+
+        if actuator is None or descriptor is None or descriptor.role is not EntityRole.ACTUATOR:
+            lifecycle = self._archive_state(
+                lifecycle,
+                "Aktuator ist in Home Assistant nicht mehr als Aktor vorhanden.",
+                now=now,
+                status=LifecycleStatus.ORPHANED,
+            )
+            updated = record.model_copy(
+                update={
+                    "assignment": AssignmentSelection(
+                        selected_numeric_entity_id=None,
+                        selected_context_entity_ids=[],
+                        source=AssignmentSource.NONE,
+                        confidence=0.0,
+                        review_required=True,
+                        reason="Aktuator fehlt oder ist kein unterstützter Aktor mehr.",
+                    ),
+                    "numeric_candidates": [],
+                    "context_candidates": [],
+                    "lifecycle": lifecycle,
+                    "updated_at": now,
+                }
+            )
+            return self._store.upsert(updated)
+
+        numeric_candidates = self._rank_candidates(
+            actuator=actuator,
+            candidates=_filter_candidates(entities, discovered, {EntityRole.MEASUREMENT}),
+            context=False,
+        )
+        context_candidates = self._rank_candidates(
+            actuator=actuator,
+            candidates=_filter_candidates(
+                entities,
+                discovered,
+                {EntityRole.BINARY_CONTEXT, EntityRole.CONTEXT},
+            ),
+            context=True,
+        )
+        assignment = self._select_assignment(
+            actuator=actuator,
+            numeric_candidates=numeric_candidates,
+            context_candidates=context_candidates,
+            override=record.manual_override,
+        )
+        lifecycle = self._reconcile_lifecycle(
+            actuator=actuator,
+            assignment=assignment,
+            lifecycle=lifecycle,
+            now=now,
+        )
+        updated = record.model_copy(
+            update={
+                "assignment": assignment,
+                "numeric_candidates": numeric_candidates,
+                "context_candidates": context_candidates,
+                "lifecycle": lifecycle,
+                "updated_at": now,
+            }
+        )
+        self._store.upsert(updated)
+        logger.info(
+            "Actuator %s reconciled via %s -> %s",
+            actuator_entity_id,
+            trigger,
+            lifecycle.status,
+        )
+        return updated
+
+    def _select_assignment(
+        self,
+        *,
+        actuator: HaEntitySummary,
+        numeric_candidates: list[AssignmentCandidate],
+        context_candidates: list[AssignmentCandidate],
+        override: ManualOverride | None,
+    ) -> AssignmentSelection:
+        if override is not None:
+            selected_numeric = override.numeric_entity_id
+            selected_contexts = list(dict.fromkeys(override.context_entity_ids))
+            return AssignmentSelection(
+                selected_numeric_entity_id=selected_numeric,
+                selected_context_entity_ids=selected_contexts,
+                source=AssignmentSource.MANUAL,
+                confidence=1.0 if selected_numeric else 0.6,
+                review_required=False,
+                reason=(
+                    "Manuelle Zuordnung überschreibt die automatische Heuristik dauerhaft."
+                ),
+            )
+
+        top_numeric = numeric_candidates[0] if numeric_candidates else None
+        top_contexts = [
+            candidate.entity_id
+            for candidate in context_candidates
+            if candidate.auto_accepted
+        ][: _MAX_CONTEXT_SELECTIONS]
+        if top_numeric is None:
+            return AssignmentSelection(
+                selected_numeric_entity_id=None,
+                selected_context_entity_ids=top_contexts,
+                source=AssignmentSource.NONE,
+                confidence=0.0,
+                review_required=True,
+                reason=f"Kein numerischer Sensor konnte für {display_name(actuator)} bestimmt werden.",
+            )
+
+        return AssignmentSelection(
+            selected_numeric_entity_id=top_numeric.entity_id,
+            selected_context_entity_ids=top_contexts,
+            source=AssignmentSource.AUTOMATIC,
+            confidence=top_numeric.confidence,
+            review_required=not top_numeric.auto_accepted,
+            reason=(
+                "Automatisch akzeptiert."
+                if top_numeric.auto_accepted
+                else "Top-Kandidat gefunden, aber Zuordnung ist noch nicht eindeutig genug."
+            ),
+        )
+
+    def _reconcile_lifecycle(
+        self,
+        *,
+        actuator: HaEntitySummary,
+        assignment: AssignmentSelection,
+        lifecycle: ModelLifecycleState,
+        now: datetime,
+    ) -> ModelLifecycleState:
+        model_id = lifecycle.model_id
+        if assignment.selected_numeric_entity_id is None:
+            return self._archive_state(
+                lifecycle,
+                "Ohne numerische Sensorzuordnung wird kein Modell aktiv gehalten.",
+                now=now,
+            )
+        if assignment.review_required and assignment.source is not AssignmentSource.MANUAL:
+            return self._archive_state(
+                lifecycle,
+                "Zuordnung ist nicht eindeutig; Modell wartet auf Review.",
+                now=now,
+                status=LifecycleStatus.REVIEW_REQUIRED,
+            )
+
+        sensor_id = assignment.selected_numeric_entity_id
+        series = self._read_history(sensor_id, now)
+        points = series.points if series is not None else []
+        if len(points) < self._settings.min_training_points:
+            return self._with_audit(
+                lifecycle.model_copy(
+                    update={
+                        "status": LifecycleStatus.PENDING_HISTORY,
+                        "last_reconciled_at": now,
+                        "reason": (
+                            f"{len(points)} von mindestens {self._settings.min_training_points} "
+                            f"Messpunkten für {sensor_id} vorhanden."
+                        ),
+                        "next_action": "Mehr Historie sammeln und Reconciliation erneut ausführen.",
+                        "last_history_point_count": len(points),
+                    }
+                ),
+                action="history_wait",
+                reason=(
+                    f"Training für {display_name(actuator)} verschoben: zu wenig numerische Historie."
+                ),
+                now=now,
+            )
+
+        signature = _history_signature(sensor_id, points)
+        artifact = self._registry.get_optional(model_id)
+        needs_retrain = artifact is None
+        retrain_reason = "Noch kein Modell vorhanden."
+        if artifact is not None:
+            valid, reason = _artifact_valid_for_sensor(artifact, sensor_id)
+            if not valid:
+                self._registry.archive(model_id)
+                needs_retrain = True
+                retrain_reason = reason
+            elif lifecycle.last_history_signature != signature:
+                needs_retrain = True
+                retrain_reason = "Historie hat sich seit dem letzten Training materiell geändert."
+            elif lifecycle.last_trained_at is None or (
+                now - lifecycle.last_trained_at
+            ) >= timedelta(hours=self._settings.retrain_stale_hours):
+                needs_retrain = True
+                retrain_reason = "Modell gilt als veraltet und wird präventiv neu trainiert."
+
+        if needs_retrain:
+            vectors = [FeatureVector(sensor_id=sensor_id, values={"value": point.value}) for point in points]
+            result = retrain_model(self._registry, model_id, vectors)
+            return self._with_audit(
+                lifecycle.model_copy(
+                    update={
+                        "status": LifecycleStatus.TRAINED,
+                        "last_reconciled_at": now,
+                        "last_trained_at": now,
+                        "last_history_signature": signature,
+                        "last_history_point_count": len(points),
+                        "reason": retrain_reason,
+                        "next_action": "Automatisch überwachen und bei neuen Daten neu trainieren.",
+                    }
+                ),
+                action="retrained" if result.replaced else "trained",
+                reason=f"{retrain_reason} Modell {model_id} aktualisiert.",
+                now=now,
+            )
+
+        return self._with_audit(
+            lifecycle.model_copy(
+                update={
+                    "status": LifecycleStatus.TRAINED,
+                    "last_reconciled_at": now,
+                    "last_history_signature": signature,
+                    "last_history_point_count": len(points),
+                    "reason": "Modell ist aktuell und passt zur bestätigten Sensorzuordnung.",
+                    "next_action": "Auf neue Historie oder Staleness warten.",
+                }
+            ),
+            action="kept",
+            reason=f"Modell {model_id} blieb unverändert.",
+            now=now,
+        )
+
+    def _read_history(self, sensor_id: str, now: datetime) -> EntityHistorySeries | None:
+        start = now - timedelta(days=self._settings.history_days)
+        history = list(self._ha_reader.read_history([sensor_id], start, now))
+        for series in history:
+            if series.entity_id == sensor_id:
+                return series
+        return None
+
+    def _archive_orphan_models(self, configured_model_ids: set[str]) -> None:
+        for artifact in self._registry.list_models():
+            if not artifact.artifact_id.startswith("actuator."):
+                continue
+            if artifact.artifact_id not in configured_model_ids:
+                self._registry.archive(artifact.artifact_id)
+
+    def _archive_state(
+        self,
+        lifecycle: ModelLifecycleState,
+        reason: str,
+        *,
+        now: datetime,
+        status: LifecycleStatus = LifecycleStatus.ARCHIVED,
+    ) -> ModelLifecycleState:
+        self._registry.archive(lifecycle.model_id)
+        return self._with_audit(
+            lifecycle.model_copy(
+                update={
+                    "status": status,
+                    "last_reconciled_at": now,
+                    "reason": reason,
+                    "next_action": "Review oder neue Zuordnung erforderlich.",
+                }
+            ),
+            action="archived",
+            reason=reason,
+            now=now,
+        )
+
+    def _rank_candidates(
+        self,
+        *,
+        actuator: HaEntitySummary,
+        candidates: Iterable[tuple[HaEntitySummary, DiscoveredEntity]],
+        context: bool,
+    ) -> list[AssignmentCandidate]:
+        scored: list[AssignmentCandidate] = []
+        all_scores: list[float] = []
+        for entity, discovered in candidates:
+            score, evidence = _score_candidate(actuator, entity, discovered.role, context=context)
+            if score <= 0:
+                continue
+            all_scores.append(score)
+            scored.append(
+                AssignmentCandidate(
+                    entity_id=entity.entity_id,
+                    domain=entity.domain,
+                    role=discovered.role,
+                    device_class=entity.device_class,
+                    state_class=entity.state_class,
+                    unit_of_measurement=entity.unit_of_measurement,
+                    friendly_name=entity.friendly_name,
+                    area_name=entity.area_name,
+                    device_name=entity.device_name,
+                    score=score,
+                    confidence=0.0,
+                    evidence=evidence,
+                )
+            )
+        if not scored:
+            return []
+        highest = max(all_scores)
+        sorted_candidates = sorted(scored, key=lambda item: (-item.score, item.entity_id))
+        second_score = sorted_candidates[1].score if len(sorted_candidates) > 1 else 0.0
+        for index, candidate in enumerate(sorted_candidates):
+            confidence = candidate.score / highest if highest else 0.0
+            margin = candidate.score - second_score if index == 0 else 0.0
+            auto_score = _CONTEXT_AUTO_ACCEPT_SCORE if context else _NUMERIC_AUTO_ACCEPT_SCORE
+            auto_accepted = confidence >= auto_score and (
+                context or margin >= _NUMERIC_MIN_MARGIN
+            )
+            sorted_candidates[index] = candidate.model_copy(
+                update={
+                    "confidence": round(confidence, 4),
+                    "auto_accepted": auto_accepted,
+                }
+            )
+        return sorted_candidates
+
+    @staticmethod
+    def _with_audit(
+        lifecycle: ModelLifecycleState,
+        *,
+        action: str,
+        reason: str,
+        now: datetime,
+    ) -> ModelLifecycleState:
+        audit = list(lifecycle.audit)
+        entry = LifecycleAuditEntry(at=now, action=action, reason=reason)
+        if not audit or audit[-1].action != action or audit[-1].reason != reason:
+            audit.append(entry)
+        if len(audit) > _AUDIT_LIMIT:
+            audit = audit[-_AUDIT_LIMIT:]
+        return lifecycle.model_copy(update={"audit": audit})
+
+
+def display_name(entity: HaEntitySummary) -> str:
+    return entity.friendly_name or entity.device_name or entity.entity_id
+
+
+def _filter_candidates(
+    entities: dict[str, HaEntitySummary],
+    discovered: dict[str, DiscoveredEntity],
+    roles: set[EntityRole],
+) -> list[tuple[HaEntitySummary, DiscoveredEntity]]:
+    result: list[tuple[HaEntitySummary, DiscoveredEntity]] = []
+    for entity_id, summary in entities.items():
+        candidate = discovered.get(entity_id)
+        if candidate is None or candidate.role not in roles:
+            continue
+        result.append((summary, candidate))
+    return result
+
+
+def _score_candidate(
+    actuator: HaEntitySummary,
+    entity: HaEntitySummary,
+    role: EntityRole,
+    *,
+    context: bool,
+) -> tuple[float, list[str]]:
+    evidence: list[str] = []
+    score = 0.0
+    actuator_tokens = _metadata_tokens(actuator)
+    entity_tokens = _metadata_tokens(entity)
+    overlap = sorted(actuator_tokens.intersection(entity_tokens))
+    if overlap:
+        score += min(0.4, 0.1 * len(overlap))
+        evidence.append(f"Gemeinsame Tokens: {', '.join(overlap[:4])}")
+    if actuator.area_name and entity.area_name and actuator.area_name == entity.area_name:
+        score += 0.35
+        evidence.append(f"Gleicher Bereich: {actuator.area_name}")
+    if actuator.device_id and entity.device_id and actuator.device_id == entity.device_id:
+        score += 0.2
+        evidence.append("Gleiche Home-Assistant-Geräte-ID")
+    if actuator.device_name and entity.device_name and actuator.device_name == entity.device_name:
+        score += 0.15
+        evidence.append(f"Gleicher Gerätename: {actuator.device_name}")
+    if actuator.friendly_name and entity.friendly_name and actuator.friendly_name == entity.friendly_name:
+        score += 0.1
+        evidence.append("Gleicher Friendly Name")
+    preferred_device_classes = _preferred_device_classes(actuator.domain, context=context)
+    if entity.device_class in preferred_device_classes:
+        score += 0.2
+        evidence.append(f"Passende device_class: {entity.device_class}")
+    if not context and entity.unit_of_measurement is not None:
+        score += 0.05
+        evidence.append(f"Numerische Einheit vorhanden: {entity.unit_of_measurement}")
+    if context and role is EntityRole.BINARY_CONTEXT:
+        score += 0.05
+        evidence.append("Binärer Kontextsensor bevorzugt für Zusatzkontext.")
+    return round(min(score, 1.0), 4), evidence
+
+
+def _preferred_device_classes(domain: str, *, context: bool) -> frozenset[str]:
+    if context:
+        return frozenset({"door", "garage_door", "motion", "occupancy", "opening", "presence"})
+    mapping = {
+        "climate": {"temperature", "humidity", "power"},
+        "cover": {"illuminance", "temperature", "wind_speed"},
+        "fan": {"temperature", "humidity", "power"},
+        "humidifier": {"humidity", "temperature", "power"},
+        "light": {"illuminance", "power", "energy"},
+        "switch": {"power", "energy", "current"},
+        "valve": {"temperature", "pressure", "humidity"},
+    }
+    return frozenset(mapping.get(domain, {"power", "energy", "temperature"}))
+
+
+def _metadata_tokens(entity: HaEntitySummary) -> set[str]:
+    raw_values = [
+        entity.entity_id,
+        entity.friendly_name,
+        entity.area_name,
+        entity.device_name,
+    ]
+    tokens: set[str] = set()
+    for value in raw_values:
+        if value is None:
+            continue
+        for token in _TOKEN_PATTERN.findall(value.lower().replace("_", " ")):
+            if len(token) < 3 or token in _STOPWORDS:
+                continue
+            tokens.add(token)
+    return tokens
+
+
+def _history_signature(sensor_id: str, points: list[NumericHistoryPoint]) -> str:
+    digest = hashlib.sha256()
+    digest.update(sensor_id.encode("utf-8"))
+    for point in points:
+        digest.update(point.timestamp.isoformat().encode("utf-8"))
+        digest.update(f"{point.value:.6f}".encode("utf-8"))
+    return digest.hexdigest()
+
+
+def _artifact_valid_for_sensor(artifact: TrainedArtifact, sensor_id: str) -> tuple[bool, str]:
+    if sensor_id not in artifact.supported_sensors:
+        return False, "Vorhandenes Modell passt nicht mehr zur aktuellen Sensorzuordnung."
+    feature_models = artifact.feature_models.get(sensor_id, {})
+    if "value" not in feature_models:
+        return False, "Vorhandenes Modell enthält kein numerisches Trainingsmerkmal 'value'."
+    return True, "Modell ist kompatibel."
--- a/app/actuators/models.py
+++ b/app/actuators/models.py
@@ -0,0 +1,102 @@
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from enum import StrEnum
+
+from pydantic import BaseModel, Field
+
+from app.ha.discovery import EntityRole
+
+
+class AssignmentSource(StrEnum):
+    NONE = "none"
+    AUTOMATIC = "automatic"
+    MANUAL = "manual"
+
+
+class LifecycleStatus(StrEnum):
+    PENDING_ASSIGNMENT = "pending_assignment"
+    REVIEW_REQUIRED = "review_required"
+    PENDING_HISTORY = "pending_history"
+    TRAINED = "trained"
+    STALE = "stale"
+    INVALID = "invalid"
+    ORPHANED = "orphaned"
+    ARCHIVED = "archived"
+
+
+class AssignmentCandidate(BaseModel):
+    entity_id: str
+    domain: str
+    role: EntityRole
+    device_class: str | None = None
+    state_class: str | None = None
+    unit_of_measurement: str | None = None
+    friendly_name: str | None = None
+    area_name: str | None = None
+    device_name: str | None = None
+    score: float = Field(ge=0.0)
+    confidence: float = Field(ge=0.0, le=1.0)
+    auto_accepted: bool = False
+    evidence: list[str] = Field(default_factory=list)
+
+
+class AssignmentSelection(BaseModel):
+    selected_numeric_entity_id: str | None = None
+    selected_context_entity_ids: list[str] = Field(default_factory=list)
+    source: AssignmentSource = AssignmentSource.NONE
+    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
+    review_required: bool = True
+    reason: str = "Noch keine Zuordnung vorhanden."
+
+
+class ManualOverride(BaseModel):
+    numeric_entity_id: str | None = None
+    context_entity_ids: list[str] = Field(default_factory=list)
+    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    note: str | None = None
+
+
+class LifecycleAuditEntry(BaseModel):
+    at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    action: str = Field(min_length=1, max_length=120)
+    reason: str = Field(min_length=1, max_length=500)
+
+
+class ModelLifecycleState(BaseModel):
+    model_id: str
+    status: LifecycleStatus = LifecycleStatus.PENDING_ASSIGNMENT
+    last_reconciled_at: datetime | None = None
+    last_trained_at: datetime | None = None
+    last_history_signature: str | None = None
+    last_history_point_count: int = Field(default=0, ge=0)
+    reason: str = "Noch keine Trainingsdaten ausgewertet."
+    next_action: str = "Aktuator auswählen und Zuordnung prüfen."
+    audit: list[LifecycleAuditEntry] = Field(default_factory=list)
+
+
+class ActuatorRecord(BaseModel):
+    actuator_entity_id: str = Field(pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
+    enabled: bool = True
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    assignment: AssignmentSelection = Field(default_factory=AssignmentSelection)
+    manual_override: ManualOverride | None = None
+    numeric_candidates: list[AssignmentCandidate] = Field(default_factory=list)
+    context_candidates: list[AssignmentCandidate] = Field(default_factory=list)
+    lifecycle: ModelLifecycleState
+
+
+class ReconciliationState(BaseModel):
+    last_started_at: datetime | None = None
+    last_completed_at: datetime | None = None
+    last_trigger: str | None = None
+    running: bool = False
+    configured_actuators: int = Field(default=0, ge=0)
+    review_required: int = Field(default=0, ge=0)
+    trained_models: int = Field(default=0, ge=0)
+    last_summary: str = "Noch keine Reconciliation ausgeführt."
+
+
+def model_id_for_actuator(actuator_entity_id: str) -> str:
+    return f"actuator.{actuator_entity_id}"
--- a/app/actuators/store.py
+++ b/app/actuators/store.py
@@ -0,0 +1,116 @@
+from __future__ import annotations
+
+import json
+import os
+from datetime import datetime, timezone
+from pathlib import Path
+from threading import RLock
+
+from app.actuators.models import (
+    ActuatorRecord,
+    LifecycleStatus,
+    ModelLifecycleState,
+    ReconciliationState,
+    model_id_for_actuator,
+)
+
+
+class ActuatorStore:
+    def __init__(self, root: str | Path) -> None:
+        self._root = Path(root).resolve()
+        self._actuators_root = self._root / "actuators"
+        self._actuators_root.mkdir(parents=True, exist_ok=True)
+        self._lock = RLock()
+        self._reconciliation_state_path = self._root / "reconciliation_state.json"
+
+    def list(self) -> list[ActuatorRecord]:
+        with self._lock:
+            return [self._load(path) for path in sorted(self._actuators_root.glob("*.json"))]
+
+    def get(self, actuator_entity_id: str) -> ActuatorRecord:
+        with self._lock:
+            target = self._target(actuator_entity_id)
+            if not target.exists():
+                raise KeyError("Aktuator-Konfiguration nicht gefunden.")
+            return self._load(target)
+
+    def upsert(self, record: ActuatorRecord) -> ActuatorRecord:
+        with self._lock:
+            self._persist(record)
+            return record
+
+    def configure(self, actuator_entity_id: str, *, enabled: bool = True) -> ActuatorRecord:
+        with self._lock:
+            target = self._target(actuator_entity_id)
+            if target.exists():
+                record = self._load(target)
+                updated = record.model_copy(
+                    update={
+                        "enabled": enabled,
+                        "updated_at": datetime.now(timezone.utc),
+                    }
+                )
+                self._persist(updated)
+                return updated
+            record = ActuatorRecord(
+                actuator_entity_id=actuator_entity_id,
+                enabled=enabled,
+                lifecycle=ModelLifecycleState(
+                    model_id=model_id_for_actuator(actuator_entity_id),
+                    status=LifecycleStatus.PENDING_ASSIGNMENT,
+                ),
+            )
+            self._persist(record)
+            return record
+
+    def delete(self, actuator_entity_id: str) -> None:
+        with self._lock:
+            target = self._target(actuator_entity_id)
+            if target.exists():
+                target.unlink()
+
+    def load_reconciliation_state(self) -> ReconciliationState:
+        with self._lock:
+            if not self._reconciliation_state_path.exists():
+                return ReconciliationState()
+            try:
+                return ReconciliationState.model_validate_json(
+                    self._reconciliation_state_path.read_text(encoding="utf-8")
+                )
+            except ValueError as exc:
+                raise ValueError("Ungültiger Reconciliation-Status.") from exc
+
+    def save_reconciliation_state(self, state: ReconciliationState) -> ReconciliationState:
+        with self._lock:
+            self._persist_reconciliation_state(state)
+            return state
+
+    def _target(self, actuator_entity_id: str) -> Path:
+        if "." not in actuator_entity_id:
+            raise ValueError("Ungültige actuator_entity_id.")
+        safe_name = actuator_entity_id.replace(".", "__")
+        return self._actuators_root / f"{safe_name}.json"
+
+    def _persist(self, record: ActuatorRecord) -> None:
+        target = self._target(record.actuator_entity_id)
+        temporary = target.with_suffix(".json.tmp")
+        temporary.write_text(
+            json.dumps(record.model_dump(mode="json"), ensure_ascii=True, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+        os.replace(temporary, target)
+
+    def _persist_reconciliation_state(self, state: ReconciliationState) -> None:
+        temporary = self._reconciliation_state_path.with_suffix(".json.tmp")
+        temporary.write_text(
+            json.dumps(state.model_dump(mode="json"), ensure_ascii=True, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+        os.replace(temporary, self._reconciliation_state_path)
+
+    @staticmethod
+    def _load(path: Path) -> ActuatorRecord:
+        try:
+            return ActuatorRecord.model_validate_json(path.read_text(encoding="utf-8"))
+        except ValueError as exc:
+            raise ValueError(f"Ungültige Aktuator-Konfiguration: {path.name}") from exc
--- a/app/api/v1/actuators.py
+++ b/app/api/v1/actuators.py
@@ -0,0 +1,122 @@
+from __future__ import annotations
+
+from fastapi import APIRouter, Depends, HTTPException, Query, Request, status
+from pydantic import BaseModel, Field
+
+from app.actuators.lifecycle import ActuatorReconciliationService
+from app.actuators.models import ActuatorRecord, ManualOverride, ReconciliationState
+from app.actuators.store import ActuatorStore
+from app.dependencies import get_ha_reader
+from app.ha.discovery import EntityRole
+from app.ha.models import HaEntitySummary
+from app.ha.reader import HaReader
+
+router = APIRouter(prefix="/v1/actuators", tags=["actuators"])
+
+
+class ConfigureActuatorRequest(BaseModel):
+    actuator_entity_id: str = Field(pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
+    enabled: bool = True
+
+
+class OverrideRequest(BaseModel):
+    numeric_entity_id: str | None = Field(default=None, pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
+    context_entity_ids: list[str] = Field(default_factory=list)
+    note: str | None = Field(default=None, max_length=300)
+    clear: bool = False
+
+
+@router.get("/discovery", response_model=list[HaEntitySummary])
+def discover_actuators(ha_reader: HaReader = Depends(get_ha_reader)) -> list[HaEntitySummary]:
+    entities = {entity.entity_id: entity for entity in ha_reader.read_entities()}
+    discovered = ha_reader.discover()
+    actuator_ids = sorted(
+        entity.entity_id for entity in discovered if entity.role is EntityRole.ACTUATOR
+    )
+    return [entities[entity_id] for entity_id in actuator_ids if entity_id in entities]
+
+
+@router.get("", response_model=list[ActuatorRecord])
+def list_configured(request: Request) -> list[ActuatorRecord]:
+    return _service(request).list_configured()
+
+
+@router.post("", response_model=ActuatorRecord, status_code=201)
+def configure(payload: ConfigureActuatorRequest, request: Request) -> ActuatorRecord:
+    try:
+        return _service(request).configure_actuator(
+            payload.actuator_entity_id,
+            enabled=payload.enabled,
+        )
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+
+
+@router.get("/{actuator_entity_id}", response_model=ActuatorRecord)
+def get_actuator(actuator_entity_id: str, request: Request) -> ActuatorRecord:
+    try:
+        return _service(request).get_actuator(actuator_entity_id)
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+
+
+@router.delete("/{actuator_entity_id}", status_code=204)
+def delete_actuator(actuator_entity_id: str, request: Request) -> None:
+    _service(request).delete_actuator(actuator_entity_id)
+
+
+@router.post("/{actuator_entity_id}/override", response_model=ActuatorRecord)
+def set_override(
+    actuator_entity_id: str,
+    payload: OverrideRequest,
+    request: Request,
+) -> ActuatorRecord:
+    override = None if payload.clear else ManualOverride(
+        numeric_entity_id=payload.numeric_entity_id,
+        context_entity_ids=payload.context_entity_ids,
+        note=payload.note,
+    )
+    try:
+        return _service(request).set_override(actuator_entity_id, override)
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+
+
+@router.post("/{actuator_entity_id}/reconcile", response_model=ActuatorRecord)
+def reconcile_actuator(
+    actuator_entity_id: str,
+    request: Request,
+) -> ActuatorRecord:
+    try:
+        return _service(request).reconcile_actuator(actuator_entity_id, trigger="manual")
+    except KeyError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+
+
+@router.get("/reconciliation/state", response_model=ReconciliationState)
+def get_reconciliation_state(request: Request) -> ReconciliationState:
+    store = getattr(request.app.state, "actuator_store", None)
+    if not isinstance(store, ActuatorStore):
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Actuator Store nicht initialisiert.",
+        )
+    return store.load_reconciliation_state()
+
+
+@router.post("/reconciliation/run", response_model=ReconciliationState)
+def run_reconciliation(
+    request: Request,
+    trigger: str = Query(default="manual", pattern=r"^[a-z0-9_-]{1,32}$"),
+) -> ReconciliationState:
+    return _service(request).reconcile_all(trigger=trigger)
+
+
+def _service(request: Request) -> ActuatorReconciliationService:
+    service = getattr(request.app.state, "actuator_service", None)
+    if not isinstance(service, ActuatorReconciliationService):
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Actuator-Reconciliation nicht initialisiert.",
+        )
+    return service
--- a/app/config.py
+++ b/app/config.py
@@ -10,6 +10,11 @@ class Settings:
    ha_token: str | None = None
    model_store: str = ".model_store"
    automation_store: str = ".automation_store"
+    actuator_store: str = ".actuator_store"
+    history_days: int = 14
+    min_training_points: int = 24
+    retrain_stale_hours: int = 24
+    reconcile_interval_seconds: int = 900

    @property
    def ha_configured(self) -> bool:
@@ -22,4 +27,11 @@ def load_settings() -> Settings:
        ha_token=os.getenv("SILLYHOME_HA_TOKEN") or os.getenv("HA_TOKEN"),
        model_store=os.getenv("SILLYHOME_MODEL_STORE", ".model_store"),
        automation_store=os.getenv("SILLYHOME_AUTOMATION_STORE", ".automation_store"),
+        actuator_store=os.getenv("SILLYHOME_ACTUATOR_STORE", ".actuator_store"),
+        history_days=max(1, min(31, int(os.getenv("SILLYHOME_HISTORY_DAYS", "14")))),
+        min_training_points=max(2, int(os.getenv("SILLYHOME_MIN_TRAINING_POINTS", "24"))),
+        retrain_stale_hours=max(1, int(os.getenv("SILLYHOME_RETRAIN_STALE_HOURS", "24"))),
+        reconcile_interval_seconds=max(
+            60, int(os.getenv("SILLYHOME_RECONCILE_INTERVAL_SECONDS", "900"))
+        ),
    )
--- a/app/ha/client.py
+++ b/app/ha/client.py
@@ -3,6 +3,7 @@ from __future__ import annotations
 import logging
 from dataclasses import dataclass
 from datetime import datetime
+import json
 import re
 from urllib.parse import quote

@@ -83,6 +84,32 @@ class HaClient:
            )
        return payload

+    def list_entity_metadata(self, entity_ids: list[str]) -> dict[str, dict[str, str | None]]:
+        if not entity_ids:
+            return {}
+        if any(not _ENTITY_ID_PATTERN.fullmatch(entity_id) for entity_id in entity_ids):
+            raise ValueError("entity_id enthält ein ungültiges Format.")
+        template = _metadata_template(entity_ids)
+        rendered = self._post_text("/api/template", {"template": template})
+        try:
+            payload = json.loads(rendered)
+        except json.JSONDecodeError as exc:
+            raise HaUnexpectedPayloadError("Entity-Metadaten konnten nicht gelesen werden.") from exc
+        if not isinstance(payload, list):
+            raise HaUnexpectedPayloadError("Entity-Metadaten haben ein unerwartetes Format.")
+        result: dict[str, dict[str, str | None]] = {}
+        for item in payload:
+            if not isinstance(item, dict):
+                raise HaUnexpectedPayloadError("Entity-Metadaten haben ein unerwartetes Format.")
+            entity_id = item.get("entity_id")
+            if not isinstance(entity_id, str) or "." not in entity_id:
+                raise HaUnexpectedPayloadError("Entity-Metadaten enthalten ungültige entity_id.")
+            result[entity_id] = {
+                key: _optional_string(item.get(key))
+                for key in ("area_id", "area_name", "device_id", "device_name")
+            }
+        return result
+
    def _get_json(
        self,
        path: str,
@@ -125,3 +152,57 @@ class HaClient:
            ) from exc

        return payload
+
+    def _post_text(self, path: str, payload: dict[str, str]) -> str:
+        try:
+            response = self._session.post(
+                f"{self._settings.url.rstrip('/')}{path}",
+                json=payload,
+                timeout=self._settings.timeout_seconds,
+            )
+        except requests.Timeout as exc:
+            raise HaTimeoutError("Zeitüberschreitung beim Zugriff auf Home Assistant.") from exc
+        except requests.RequestException as exc:
+            raise HaHttpError(
+                getattr(getattr(exc, "response", None), "status_code", 502),
+                "Netzwerkfehler beim Zugriff auf Home Assistant.",
+            ) from exc
+
+        if response.status_code in (401, 403):
+            raise HaAuthError(
+                response.status_code,
+                "Authentifizierung bei Home Assistant fehlgeschlagen.",
+            )
+        try:
+            response.raise_for_status()
+        except requests.HTTPError as exc:
+            raise HaHttpError(response.status_code, "Home Assistant meldet einen Fehler.") from exc
+        return response.text
+
+
+def _metadata_template(entity_ids: list[str]) -> str:
+    ids = json.dumps(entity_ids, ensure_ascii=True)
+    return (
+        "{% set ids = "
+        f"{ids}"
+        " %}["
+        "{% for entity_id in ids %}"
+        "{% set device = device_id(entity_id) %}"
+        "{{ "
+        "{"
+        "\"entity_id\": entity_id,"
+        "\"area_id\": area_id(entity_id),"
+        "\"area_name\": area_name(entity_id),"
+        "\"device_id\": device,"
+        "\"device_name\": device_attr(device, 'name') if device else none"
+        "}"
+        " | tojson }}"
+        "{% if not loop.last %},{% endif %}"
+        "{% endfor %}]"
+    )
+
+
+def _optional_string(value: object) -> str | None:
+    if value is None or value == "":
+        return None
+    return str(value)
--- a/app/ha/models.py
+++ b/app/ha/models.py
@@ -17,3 +17,8 @@ class HaEntitySummary(BaseModel):
    state_class: str | None = None
    device_class: str | None = None
    unit_of_measurement: str | None = None
+    friendly_name: str | None = None
+    area_id: str | None = None
+    area_name: str | None = None
+    device_id: str | None = None
+    device_name: str | None = None
--- a/app/ha/reader.py
+++ b/app/ha/reader.py
@@ -3,12 +3,17 @@ from __future__ import annotations
 from collections.abc import Sequence
 from datetime import datetime
 from typing import Any
+import logging
+
+from app.ha.exceptions import HaClientError

 from app.ha.client import HaClient
 from app.ha.discovery import DiscoveredEntity, discover_entities
 from app.ha.history import EntityHistorySeries, normalize_history_payload
 from app.ha.models import HaEntitySummary

+logger = logging.getLogger(__name__)
+

 class HaReader:
    def __init__(self, client: HaClient) -> None:
@@ -16,6 +21,16 @@ class HaReader:

    def read_entities(self) -> Sequence[HaEntitySummary]:
        entities = self._client.list_entities()
+        entity_ids = [
+            raw_entity_id
+            for item in entities
+            if isinstance((raw_entity_id := item.get("entity_id")), str) and "." in raw_entity_id
+        ]
+        try:
+            metadata_by_entity = self._client.list_entity_metadata(entity_ids)
+        except (HaClientError, ValueError) as exc:
+            logger.warning("HA metadata enrichment skipped: %s", exc)
+            metadata_by_entity = {}
        summaries: list[HaEntitySummary] = []
        for item in entities:
            raw_entity_id = item.get("entity_id")
@@ -25,6 +40,7 @@ class HaReader:
            domain = entity_id.split(".", 1)[0]
            raw_attributes = item.get("attributes") or {}
            attributes: dict[str, Any] = raw_attributes if isinstance(raw_attributes, dict) else {}
+            metadata = metadata_by_entity.get(entity_id, {})
            summaries.append(
                HaEntitySummary(
                    entity_id=entity_id,
@@ -32,6 +48,15 @@ class HaReader:
                    state_class=_optional_str(attributes.get("state_class")),
                    device_class=_optional_str(attributes.get("device_class")),
                    unit_of_measurement=_optional_str(attributes.get("unit_of_measurement")),
+                    friendly_name=_optional_str(attributes.get("friendly_name")),
+                    area_id=_optional_str(metadata.get("area_id") or attributes.get("area_id")),
+                    area_name=_optional_str(metadata.get("area_name") or attributes.get("area_name")),
+                    device_id=_optional_str(metadata.get("device_id") or attributes.get("device_id")),
+                    device_name=_optional_str(
+                        metadata.get("device_name")
+                        or attributes.get("device_name")
+                        or attributes.get("device")
+                    ),
                )
            )
        return summaries
--- a/app/main.py
+++ b/app/main.py
@@ -1,4 +1,5 @@
-from contextlib import asynccontextmanager
+import asyncio
+from contextlib import asynccontextmanager, suppress
 from collections.abc import AsyncIterator
 from pathlib import Path
 from typing import cast
@@ -7,6 +8,9 @@ from fastapi import FastAPI
 from fastapi.responses import FileResponse
 from fastapi.staticfiles import StaticFiles

+from app.actuators.lifecycle import ActuatorReconciliationService
+from app.actuators.store import ActuatorStore
+from app.api.v1.actuators import router as actuators_router
 from app.api.v1.entities import router as entities_router
 from app.api.v1.automations import router as automations_router
 from app.automations.store import AutomationStore
@@ -22,10 +26,14 @@ from backend.routes.ml import init_ml_routes
 async def lifespan(app: FastAPI) -> AsyncIterator[None]:
    settings = app.state.settings
    client: HaClient | None = None
+    reconcile_task: asyncio.Task[None] | None = None
    app.state.registry = ModelRegistry(settings.model_store)
    app.state.automation_store = AutomationStore(settings.automation_store)
+    app.state.actuator_store = ActuatorStore(settings.actuator_store)
    if hasattr(app.state, "ha_reader"):
        del app.state.ha_reader
+    if hasattr(app.state, "actuator_service"):
+        del app.state.actuator_service
    if settings.ha_configured:
        client = HaClient(
            settings=HaClientSettings(
@@ -34,9 +42,21 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
            )
        )
        app.state.ha_reader = HaReader(client=client)
+        app.state.actuator_service = ActuatorReconciliationService(
+            ha_reader=app.state.ha_reader,
+            store=app.state.actuator_store,
+            registry=app.state.registry,
+            settings=settings,
+        )
+        await asyncio.to_thread(app.state.actuator_service.reconcile_all, "startup")
+        reconcile_task = asyncio.create_task(_periodic_reconciliation(app))
    try:
        yield
    finally:
+        if reconcile_task is not None:
+            reconcile_task.cancel()
+            with suppress(asyncio.CancelledError):
+                await reconcile_task
        if client is not None:
            client.close()

@@ -44,13 +64,14 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 app = FastAPI(
    title="SillyHome Next API",
    description="Lokales Smart-Home-Intelligenzsystem für Home Assistant.",
-    version="0.3.0",
+    version="0.4.0",
    lifespan=lifespan,
 )
 app.state.settings = load_settings()
 register_exception_handlers(app)
 app.include_router(entities_router)
 app.include_router(automations_router)
+app.include_router(actuators_router)
 init_ml_routes(app, model_store=app.state.settings.model_store)

 STATIC_DIR = Path(__file__).with_name("static")
@@ -65,3 +86,12 @@ def health() -> dict[str, str]:
@app.get("/")
 def root() -> FileResponse:
    return FileResponse(STATIC_DIR / "index.html")
+
+
+async def _periodic_reconciliation(app: FastAPI) -> None:
+    while True:
+        await asyncio.sleep(app.state.settings.reconcile_interval_seconds)
+        service = getattr(app.state, "actuator_service", None)
+        if not isinstance(service, ActuatorReconciliationService):
+            continue
+        await asyncio.to_thread(service.reconcile_all, "scheduled")
--- a/app/ml/registry/model_registry.py
+++ b/app/ml/registry/model_registry.py
@@ -20,6 +20,8 @@ class ModelRegistry:
    def __init__(self, root: str | Path) -> None:
        self._root = Path(root).resolve()
        self._root.mkdir(parents=True, exist_ok=True)
+        self._archive_root = self._root / "archive"
+        self._archive_root.mkdir(parents=True, exist_ok=True)
        self._artifacts: dict[str, TrainedArtifact] = {}
        self._lock = RLock()
        self._load_existing()
@@ -43,10 +45,27 @@ class ModelRegistry:
                raise KeyError(f"Artifact '{artifact_id}' nicht registriert.")
            return self._artifacts[artifact_id]

+    def get_optional(self, artifact_id: str) -> TrainedArtifact | None:
+        self._validate_artifact_id(artifact_id)
+        with self._lock:
+            return self._artifacts.get(artifact_id)
+
    def list_models(self) -> Iterable[TrainedArtifact]:
        with self._lock:
            return [self._artifacts[key] for key in sorted(self._artifacts)]

+    def archive(self, artifact_id: str) -> bool:
+        self._validate_artifact_id(artifact_id)
+        with self._lock:
+            artifact = self._artifacts.pop(artifact_id, None)
+            source = self._root / f"{artifact_id}.json"
+            if not source.exists():
+                return artifact is not None
+            target = self._archive_root / f"{artifact_id}.json"
+            os.replace(source, target)
+            logger.info("Modell archiviert: %s", target)
+            return True
+
    def _load_existing(self) -> None:
        for source in sorted(self._root.glob("*.json")):
            try:
--- a/app/static/index.html
+++ b/app/static/index.html
@@ -8,63 +8,65 @@
    :root { color-scheme: dark; font-family: system-ui, sans-serif; background: #10151c; color: #eaf1f8; }
    body { margin: 0; }
    header { padding: 20px; background: linear-gradient(135deg,#142b3a,#193f36); }
-    h1,h2 { margin: 0 0 12px; }
+    h1,h2,h3 { margin: 0 0 12px; }
    header p { margin: 4px 0; color: #b9c9d6; }
-    main { display: grid; grid-template-columns: repeat(auto-fit,minmax(310px,1fr)); gap: 14px; padding: 14px; }
+    main { display: grid; grid-template-columns: repeat(auto-fit,minmax(320px,1fr)); gap: 14px; padding: 14px; }
    section { background: #18212b; border: 1px solid #2d3a47; border-radius: 12px; padding: 16px; }
    .wide { grid-column: 1 / -1; }
-    .ok { color: #66dfa9; } .bad { color: #ff8f8f; }
+    .ok { color: #66dfa9; }
+    .warn { color: #f3c969; }
+    .bad { color: #ff8f8f; }
    label { display: block; margin: 9px 0 4px; color: #b9c9d6; }
    input,select,textarea,button { box-sizing: border-box; width: 100%; border-radius: 7px; border: 1px solid #3b4b5b; padding: 9px; background: #101820; color: #fff; }
    button { margin-top: 10px; background: #23715b; border: 0; font-weight: 700; cursor: pointer; }
    button.secondary { background: #37495c; }
-    pre { white-space: pre-wrap; max-height: 310px; overflow: auto; background: #0d141b; padding: 10px; border-radius: 7px; }
+    pre { white-space: pre-wrap; overflow: auto; background: #0d141b; padding: 10px; border-radius: 7px; }
    table { width: 100%; border-collapse: collapse; font-size: .9rem; }
-    td,th { padding: 7px; border-bottom: 1px solid #2d3a47; text-align: left; }
+    td,th { padding: 7px; border-bottom: 1px solid #2d3a47; text-align: left; vertical-align: top; }
+    ul { margin: 8px 0; padding-left: 18px; }
    .notice { border-left: 4px solid #e8b34b; padding-left: 10px; }
+    .grid-two { display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr)); gap:8px; }
+    .chips { display:flex; flex-wrap:wrap; gap:6px; margin-top:8px; }
+    .chip { padding:4px 8px; border-radius:999px; background:#22303c; border:1px solid #31404d; font-size:.85rem; }
  </style>
 </head>
 <body>
 <header>
  <h1>SillyHome Next</h1>
-  <p>Lokale Home-Assistant-Analyse, Vorhersagen und kontrollierte Automation-Entwürfe.</p>
-  <p class="notice">Sicherheitsmodus: Entwürfe werden niemals automatisch in Home Assistant ausgeführt.</p>
+  <p>Aktuator-zentrierte Home-Assistant-Analyse mit nachvollziehbarer Sensorzuordnung und kontrolliertem Modell-Lebenszyklus.</p>
+  <p class="notice">Sicherheitsmodus: SillyHome führt niemals selbst Aktor-Services aus. Automationen bleiben manuell freizugebende YAML-Entwürfe.</p>
 </header>
 <main>
  <section>
    <h2>Systemstatus</h2>
    <div id="status">Prüfung läuft ...</div>
-    <button class="secondary" onclick="loadStatus()">Neu laden</button>
+    <div class="chips" id="status-chips"></div>
+    <button class="secondary" onclick="loadOverview()">Neu laden</button>
+    <button onclick="runReconciliation()">Reconciliation ausführen</button>
  </section>
+
  <section>
-    <h2>Entity Discovery</h2>
-    <label for="domain">Domain (optional)</label>
-    <input id="domain" placeholder="sensor">
-    <button onclick="discover()">HA-Entities analysieren</button>
-    <pre id="discovery">Noch nicht geladen.</pre>
+    <h2>Aktuator wählen</h2>
+    <label for="actuator-select">Home-Assistant-Aktor</label>
+    <select id="actuator-select"></select>
+    <button onclick="configureActuator()">Aktuator übernehmen</button>
+    <pre id="actuator-config-result">Noch kein Aktuator konfiguriert.</pre>
  </section>
-  <section>
-    <h2>Modell trainieren</h2>
-    <label for="train-model">Modell-ID</label><input id="train-model" value="home-model">
-    <label for="train-sensor">Sensor</label><input id="train-sensor" placeholder="sensor.temperatur">
-    <label for="train-feature">Merkmal</label><input id="train-feature" value="value">
-    <label for="train-values">Messwerte, komma-getrennt</label><input id="train-values" placeholder="19,20,21">
-    <button onclick="train()">Trainieren</button>
-    <pre id="training">Bereit.</pre>
+
+  <section class="wide">
+    <h2>Konfigurierte Aktuatoren</h2>
+    <div id="configured-actuators">Noch nicht geladen.</div>
  </section>
-  <section>
-    <h2>Vorhersage</h2>
-    <label for="predict-model">Modell-ID</label><input id="predict-model" value="home-model">
-    <label for="predict-sensor">Sensor</label><input id="predict-sensor" placeholder="sensor.temperatur">
-    <label for="predict-feature">Merkmal</label><input id="predict-feature" value="value">
-    <label for="predict-value">Aktueller Wert</label><input id="predict-value" type="number" step="any">
-    <button onclick="predict()">Vorhersagen und erklären</button>
-    <pre id="prediction">Bereit.</pre>
+
+  <section class="wide">
+    <h2>Zuordnung und Modellstatus</h2>
+    <div id="actuator-detail">Einen konfigurierten Aktuator auswählen.</div>
  </section>
+
  <section class="wide">
    <h2>Automation-Entwurf</h2>
    <p>Der Entwurf muss explizit freigegeben werden. Auch danach wird nur YAML exportiert, nichts geschaltet.</p>
-    <div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(220px,1fr));gap:8px">
+    <div class="grid-two">
      <div><label for="alias">Name</label><input id="alias" value="Licht bei Dunkelheit"></div>
      <div><label for="trigger">Trigger-Entity</label><input id="trigger" placeholder="sensor.flur_illuminance"></div>
      <div><label for="below">Unter Grenzwert</label><input id="below" type="number" value="10"></div>
@@ -78,49 +80,228 @@
 </main>
 <script>
 const pretty = value => JSON.stringify(value, null, 2);
+let currentActuatorId = null;
+
 async function api(path, options = {}) {
  const response = await fetch(path, {headers: {"Content-Type": "application/json"}, ...options});
  const body = await response.json().catch(() => ({}));
  if (!response.ok) throw new Error(body.detail || `${response.status} ${response.statusText}`);
  return body;
 }
-async function loadStatus() {
-  const box=document.getElementById("status");
-  try {
-    const [health, ml, models]=await Promise.all([api("health"),api("ml/health"),api("ml/models")]);
-    box.innerHTML=`<p class="ok">API und ML bereit</p><p>Modelle: ${models.models.length}</p>`;
-  } catch(e) { box.innerHTML=`<p class="bad">${e.message}</p>`; }
+
+function statusClass(record) {
+  if (record.assignment.review_required) return "warn";
+  if (record.lifecycle.status === "trained") return "ok";
+  if (record.lifecycle.status === "review_required" || record.lifecycle.status === "invalid") return "warn";
+  return "bad";
 }
-async function discover() {
-  const out=document.getElementById("discovery"), domain=document.getElementById("domain").value.trim();
-  out.textContent="Lade ...";
-  try {
-    const rows=await api(`v1/discovery?learnable=true${domain?`&domain=${encodeURIComponent(domain)}`:""}`);
-    out.textContent=pretty({learnable_entities:rows.length, entities:rows.slice(0,100)});
-  } catch(e) { out.textContent=e.message; }
+
+function renderEvidence(evidence) {
+  return evidence.length ? `<ul>${evidence.map(item => `<li>${item}</li>`).join("")}</ul>` : "<span class='bad'>Keine Evidenz</span>";
 }
-async function train() {
-  const out=document.getElementById("training");
+
+async function loadOverview() {
+  const status = document.getElementById("status");
+  const chips = document.getElementById("status-chips");
  try {
-    const values=document.getElementById("train-values").value.split(",").map(Number).filter(Number.isFinite);
-    if (!values.length) throw new Error("Mindestens einen Messwert eingeben.");
-    const sensor=document.getElementById("train-sensor").value.trim(), feature=document.getElementById("train-feature").value.trim();
-    const samples=values.map(value=>({sensor_id:sensor,values:{[feature]:value}}));
-    out.textContent=pretty(await api("ml/retrain",{method:"POST",body:JSON.stringify({modelId:document.getElementById("train-model").value,samples})}));
-    await loadStatus();
-  } catch(e) { out.textContent=e.message; }
+    const [health, ml, reconciliation, actuators] = await Promise.all([
+      api("health"),
+      api("ml/health"),
+      api("v1/actuators/reconciliation/state"),
+      api("v1/actuators"),
+    ]);
+    status.innerHTML = `<p class="ok">API und ML bereit</p><p>Letzte Reconciliation: ${reconciliation.last_completed_at || "noch nie"}</p><p>${reconciliation.last_summary}</p>`;
+    chips.innerHTML = [
+      `<span class="chip">Health: ${health.status}</span>`,
+      `<span class="chip">ML: ${ml.status}</span>`,
+      `<span class="chip">Aktuatoren: ${actuators.length}</span>`,
+      `<span class="chip">Trainierte Modelle: ${reconciliation.trained_models}</span>`,
+    ].join("");
+  } catch (error) {
+    status.innerHTML = `<p class="bad">${error.message}</p>`;
+    chips.innerHTML = "";
  }
-async function predict() {
-  const out=document.getElementById("prediction");
+  await Promise.all([loadActuatorDiscovery(), loadConfiguredActuators(), loadProposals()]);
+}
+
+async function loadActuatorDiscovery() {
+  const select = document.getElementById("actuator-select");
  try {
-    const feature=document.getElementById("predict-feature").value.trim();
-    out.textContent=pretty(await api("ml/predict",{method:"POST",body:JSON.stringify({
-      modelId:document.getElementById("predict-model").value,
-      sensor_id:document.getElementById("predict-sensor").value.trim(),
-      values:{[feature]:Number(document.getElementById("predict-value").value)}
-    })}));
-  } catch(e) { out.textContent=e.message; }
+    const actuators = await api("v1/actuators/discovery");
+    select.innerHTML = actuators.length
+      ? actuators.map(entity => `<option value="${entity.entity_id}">${entity.friendly_name || entity.entity_id}${entity.area_name ? ` (${entity.area_name})` : ""}</option>`).join("")
+      : "<option value=''>Keine Aktuatoren gefunden</option>";
+  } catch (error) {
+    select.innerHTML = `<option value="">${error.message}</option>`;
  }
+}
+
+async function configureActuator() {
+  const actuatorId = document.getElementById("actuator-select").value;
+  const box = document.getElementById("actuator-config-result");
+  if (!actuatorId) return;
+  try {
+    const record = await api("v1/actuators", {
+      method: "POST",
+      body: JSON.stringify({actuator_entity_id: actuatorId}),
+    });
+    currentActuatorId = record.actuator_entity_id;
+    box.textContent = pretty(record);
+    await loadOverview();
+    await showActuator(record.actuator_entity_id);
+  } catch (error) {
+    box.textContent = error.message;
+  }
+}
+
+async function runReconciliation() {
+  try {
+    await api("v1/actuators/reconciliation/run", {method: "POST"});
+    await loadOverview();
+    if (currentActuatorId) await showActuator(currentActuatorId);
+  } catch (error) {
+    alert(error.message);
+  }
+}
+
+async function loadConfiguredActuators() {
+  const box = document.getElementById("configured-actuators");
+  try {
+    const rows = await api("v1/actuators");
+    box.innerHTML = rows.length ? `
+      <table>
+        <tr><th>Aktuator</th><th>Numerischer Sensor</th><th>Review</th><th>Modellstatus</th><th>Letztes Training</th><th>Aktion</th></tr>
+        ${rows.map(record => `
+          <tr>
+            <td>${record.actuator_entity_id}</td>
+            <td>${record.assignment.selected_numeric_entity_id || "-"}</td>
+            <td class="${record.assignment.review_required ? "warn" : "ok"}">${record.assignment.review_required ? "erforderlich" : "nein"}</td>
+            <td class="${statusClass(record)}">${record.lifecycle.status}</td>
+            <td>${record.lifecycle.last_trained_at || "-"}</td>
+            <td><button onclick="showActuator('${record.actuator_entity_id}')">Details</button></td>
+          </tr>
+        `).join("")}
+      </table>` : "<p>Keine konfigurierten Aktuatoren.</p>";
+  } catch (error) {
+    box.textContent = error.message;
+  }
+}
+
+async function showActuator(actuatorId) {
+  currentActuatorId = actuatorId;
+  const box = document.getElementById("actuator-detail");
+  try {
+    const record = await api(`v1/actuators/${encodeURIComponent(actuatorId)}`);
+    const numericRows = record.numeric_candidates.map(candidate => `
+      <tr>
+        <td>${candidate.entity_id}</td>
+        <td>${candidate.score.toFixed(3)} / ${candidate.confidence.toFixed(2)}</td>
+        <td>${candidate.auto_accepted ? "<span class='ok'>auto</span>" : "<span class='warn'>review</span>"}</td>
+        <td>${renderEvidence(candidate.evidence)}</td>
+      </tr>
+    `).join("");
+    const contextRows = record.context_candidates.map(candidate => `
+      <tr>
+        <td>${candidate.entity_id}</td>
+        <td>${candidate.score.toFixed(3)} / ${candidate.confidence.toFixed(2)}</td>
+        <td>${candidate.auto_accepted ? "<span class='ok'>auto</span>" : "<span class='warn'>optional</span>"}</td>
+        <td>${renderEvidence(candidate.evidence)}</td>
+      </tr>
+    `).join("");
+    box.innerHTML = `
+      <div class="grid-two">
+        <div>
+          <h3>Auswahl</h3>
+          <p><strong>Aktuator:</strong> ${record.actuator_entity_id}</p>
+          <p><strong>Numerischer Sensor:</strong> ${record.assignment.selected_numeric_entity_id || "-"}</p>
+          <p><strong>Kontext:</strong> ${record.assignment.selected_context_entity_ids.join(", ") || "-"}</p>
+          <p><strong>Quelle:</strong> ${record.assignment.source}</p>
+          <p><strong>Review:</strong> <span class="${record.assignment.review_required ? "warn" : "ok"}">${record.assignment.review_required ? "erforderlich" : "nicht erforderlich"}</span></p>
+          <p><strong>Begruendung:</strong> ${record.assignment.reason}</p>
+        </div>
+        <div>
+          <h3>Modell-Lebenszyklus</h3>
+          <p><strong>Status:</strong> <span class="${statusClass(record)}">${record.lifecycle.status}</span></p>
+          <p><strong>Letztes Training:</strong> ${record.lifecycle.last_trained_at || "-"}</p>
+          <p><strong>Messpunkte:</strong> ${record.lifecycle.last_history_point_count}</p>
+          <p><strong>Grund:</strong> ${record.lifecycle.reason}</p>
+          <p><strong>Nächste Aktion:</strong> ${record.lifecycle.next_action}</p>
+          <button onclick="reconcileActuator('${record.actuator_entity_id}')">Diesen Aktuator erneut prüfen</button>
+        </div>
+      </div>
+      <div class="grid-two">
+        <div>
+          <h3>Manuelle Overrides</h3>
+          <label for="override-numeric">Numerischer Sensor</label>
+          <input id="override-numeric" value="${record.manual_override?.numeric_entity_id || record.assignment.selected_numeric_entity_id || ""}">
+          <label for="override-context">Kontext-Entities (kommagetrennt)</label>
+          <textarea id="override-context">${(record.manual_override?.context_entity_ids || record.assignment.selected_context_entity_ids || []).join(", ")}</textarea>
+          <label for="override-note">Notiz</label>
+          <input id="override-note" value="${record.manual_override?.note || ""}">
+          <button onclick="saveOverride('${record.actuator_entity_id}')">Override speichern</button>
+          <button class="secondary" onclick="clearOverride('${record.actuator_entity_id}')">Override löschen</button>
+        </div>
+        <div>
+          <h3>Audit</h3>
+          <pre>${pretty(record.lifecycle.audit)}</pre>
+        </div>
+      </div>
+      <h3>Numerische Kandidaten</h3>
+      ${numericRows ? `<table><tr><th>Entity</th><th>Score / Confidence</th><th>Auto</th><th>Evidenz</th></tr>${numericRows}</table>` : "<p>Keine Kandidaten.</p>"}
+      <h3>Kontext-Kandidaten</h3>
+      ${contextRows ? `<table><tr><th>Entity</th><th>Score / Confidence</th><th>Auto</th><th>Evidenz</th></tr>${contextRows}</table>` : "<p>Keine Kandidaten.</p>"}
+    `;
+  } catch (error) {
+    box.textContent = error.message;
+  }
+}
+
+async function reconcileActuator(actuatorId) {
+  try {
+    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/reconcile`, {method: "POST"});
+    await loadOverview();
+    await showActuator(actuatorId);
+  } catch (error) {
+    alert(error.message);
+  }
+}
+
+async function saveOverride(actuatorId) {
+  const numeric = document.getElementById("override-numeric").value.trim() || null;
+  const contexts = document.getElementById("override-context").value
+    .split(",")
+    .map(item => item.trim())
+    .filter(Boolean);
+  const note = document.getElementById("override-note").value.trim() || null;
+  try {
+    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/override`, {
+      method: "POST",
+      body: JSON.stringify({
+        numeric_entity_id: numeric,
+        context_entity_ids: contexts,
+        note,
+      }),
+    });
+    await loadOverview();
+    await showActuator(actuatorId);
+  } catch (error) {
+    alert(error.message);
+  }
+}
+
+async function clearOverride(actuatorId) {
+  try {
+    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/override`, {
+      method: "POST",
+      body: JSON.stringify({clear: true}),
+    });
+    await loadOverview();
+    await showActuator(actuatorId);
+  } catch (error) {
+    alert(error.message);
+  }
+}
+
 async function createProposal() {
  try {
    await api("v1/automations/proposals", {method: "POST", body: JSON.stringify({
@@ -130,20 +311,45 @@ async function createProposal() {
      action: {service: document.getElementById("service").value, entity_id: document.getElementById("target").value, data: {}}
    })});
    await loadProposals();
-  } catch(e) { alert(e.message); }
+  } catch (error) {
+    alert(error.message);
  }
+}
+
 async function decide(id, revision, action) {
-  try { await api(`v1/automations/proposals/${id}/${action}`,{method:"POST",body:JSON.stringify({expected_revision:revision})}); await loadProposals(); }
-  catch(e) { alert(e.message); }
+  try {
+    await api(`v1/automations/proposals/${id}/${action}`, {method: "POST", body: JSON.stringify({expected_revision: revision})});
+    await loadProposals();
+  } catch (error) {
+    alert(error.message);
  }
+}
+
 async function loadProposals() {
  const box = document.getElementById("proposals");
  try {
    const rows = await api("v1/automations/proposals");
-    box.innerHTML=rows.length?`<table><tr><th>Name</th><th>Status</th><th>Aktion</th></tr>${rows.map(x=>`<tr><td>${x.alias}</td><td>${x.status}</td><td>${x.status==="draft"?`<button onclick="decide('${x.proposal_id}',${x.revision},'approve')">Freigeben</button><button class="secondary" onclick="decide('${x.proposal_id}',${x.revision},'reject')">Ablehnen</button>`:`${x.status==="approved"?`<a href="v1/automations/proposals/${x.proposal_id}/yaml">YAML laden</a>`:"-"}`}</td></tr>`).join("")}</table>`:"<p>Keine Entwürfe.</p>";
-  } catch(e) { box.textContent=e.message; }
+    box.innerHTML = rows.length ? `
+      <table>
+        <tr><th>Name</th><th>Status</th><th>Aktion</th></tr>
+        ${rows.map(item => `
+          <tr>
+            <td>${item.alias}</td>
+            <td>${item.status}</td>
+            <td>${item.status === "draft"
+              ? `<button onclick="decide('${item.proposal_id}',${item.revision},'approve')">Freigeben</button><button class="secondary" onclick="decide('${item.proposal_id}',${item.revision},'reject')">Ablehnen</button>`
+              : item.status === "approved"
+                ? `<a href="v1/automations/proposals/${item.proposal_id}/yaml">YAML laden</a>`
+                : "-"}</td>
+          </tr>
+        `).join("")}
+      </table>` : "<p>Keine Entwürfe.</p>";
+  } catch (error) {
+    box.textContent = error.message;
  }
-loadStatus(); loadProposals();
+}
+
+loadOverview();
 </script>
 </body>
 </html>
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -9,9 +9,15 @@ services:
    environment:
      SILLYHOME_MODEL_STORE: /app/data/models
      SILLYHOME_AUTOMATION_STORE: /app/data/automations
+      SILLYHOME_ACTUATOR_STORE: /app/data/actuators
+      SILLYHOME_HISTORY_DAYS: 14
+      SILLYHOME_MIN_TRAINING_POINTS: 24
+      SILLYHOME_RETRAIN_STALE_HOURS: 24
+      SILLYHOME_RECONCILE_INTERVAL_SECONDS: 900
    volumes:
      - model-data:/app/data/models
      - automation-data:/app/data/automations
+      - actuator-data:/app/data/actuators
    read_only: true
    tmpfs:
      - /tmp
@@ -24,3 +30,4 @@ services:
 volumes:
  model-data:
  automation-data:
+  actuator-data:
--- a/docs/ha_data.md
+++ b/docs/ha_data.md
@@ -14,6 +14,15 @@ Trainings- und Erklärungsprozesse.
 - `actuator`: mögliche Automationsziele, nicht als Trainingssensor verwendet
 - `unsupported`: noch nicht klassifizierte Entity-Typen

+Zusätzlich reichert `HaReader` verfügbare Metadaten wie `friendly_name`,
+Bereich und Gerät aus Home Assistant an. Für die aktor-zentrierte Zuordnung
+nutzt SillyHome Next bevorzugt:
+
+- `area_id` und `area_name`
+- `device_id` und `device_name`
+- Friendly Names und Entity-ID-Tokens
+- Domain und `device_class`
+
 Optionale Query-Parameter:

 - `domain=sensor` kann mehrfach angegeben werden
@@ -32,7 +41,8 @@ Historische Zustände werden über Home Assistants
 Die Normalisierung übernimmt nur endliche numerische Zustände. `unknown`,
 `unavailable`, nichtnumerische Werte, `NaN` und unendliche Werte werden nicht
 als Trainingsdaten verwendet. Ergebnisse werden je Entity chronologisch
-sortiert.
+sortiert. Binäre Kontext-Entities werden bewusst nicht in numerische
+Trainingsreihen konvertiert.

 ## Datenschutz und Betrieb

--- a/docs/ml_api.md
+++ b/docs/ml_api.md
@@ -1,7 +1,7 @@
 # ML-Serving-API

 Diese Dokumentation beschreibt die REST-Endpunkte der aktuellen
-Modell-Artefakt- und Vorhersage-Schnittstelle.
+Modell-Artefakt-, Vorhersage- und aktor-zentrierten Lifecycle-Schnittstelle.

 Das Serving verwendet ein lokal trainiertes statistisches Baseline-Modell.

@@ -15,6 +15,8 @@ Das Serving verwendet ein lokal trainiertes statistisches Baseline-Modell.
 - Einzelvorhersage: `/predict`
 - Batchvorhersage: `/batch`

+Die aktor-zentrierte API liegt unter `/v1/actuators`.
+
 Der Standard-Start erfolgt über `uvicorn app.main:app`, danach stehen HA- und
 ML-Routen in derselben Anwendung bereit.

@@ -168,14 +170,45 @@ Batch-Vorhersage für mehrere Sensorwerte.
 - `422 Unprocessable Content`: Sensor wird vom Modell nicht unterstützt oder Eingabe ist ungültig.
 - `503 Service Unavailable`: Registry ist nicht initialisiert.

+## Aktuator-zentrierte API
+
+### `GET /v1/actuators/discovery`
+
+Listet unterstützte Aktuatoren mit angereicherter HA-Metadatenbasis.
+
+### `POST /v1/actuators`
+
+Registriert einen Aktuator, ermittelt passende numerische Sensoren und
+Kontext-Entities, trainiert bei ausreichender History automatisch ein Modell und
+liefert Zuordnung, Confidence, Evidenz und Lifecycle-Status zurück.
+
+**Request**
+```json
+{
+  "actuator_entity_id": "light.abstellkammer",
+  "enabled": true
+}
+```
+
+### `POST /v1/actuators/{actuator_entity_id}/override`
+
+Persistiert manuelle Overrides. Diese haben Vorrang vor der automatischen
+Heuristik und überstehen Neustarts.
+
+### `POST /v1/actuators/reconciliation/run`
+
+Führt eine sichere globale Reconciliation aus. Die periodische Add-on-Schleife
+ruft denselben idempotenten Ablauf auf, startet aber keine Services in Home
+Assistant.
+
 ## Betrieb

-Die produktive App lädt Artefakte aus `SILLYHOME_MODEL_STORE`. Neue Artefakte
-werden über `/ml/retrain`, `RetrainingService` oder direkt über
-`ModelRegistry.register(...)` registriert. Die Registry speichert validiertes
-JSON atomisch und lädt es beim Neustart. Die API sollte nur in einem
-vertrauenswürdigen Netz oder hinter einem authentifizierenden Reverse Proxy
-erreichbar sein.
+Die produktive App lädt Artefakte aus `SILLYHOME_MODEL_STORE`. Aktuator-,
+Override- und Reconciliation-Zustände liegen atomisch in
+`SILLYHOME_ACTUATOR_STORE`. Neue Artefakte werden über `/ml/retrain`,
+`RetrainingService` oder den aktor-zentrierten Lifecycle registriert. Die API
+sollte nur in einem vertrauenswürdigen Netz oder hinter einem
+authentifizierenden Reverse Proxy erreichbar sein.

 ## Verweise

--- a/docs/ml_training.md
+++ b/docs/ml_training.md
@@ -3,10 +3,19 @@
 SillyHome Next trainiert ein lokales statistisches Baseline-Modell pro Sensor
 und Merkmal. Es benötigt keine Cloud und keine externe ML-Laufzeit.

+Seit `v0.4.0` ist der bevorzugte Weg aktor-zentriert: ein bestätigter Aktuator
+wird mit einem numerischen Primärsensor verknüpft, die Historie dieses Sensors
+wird automatisch geladen und in ein deterministisches Artefakt überführt.
+
 ## 1. Daten sammeln

 Alle Trainingsvektoren werden über `FeatureStore.add(...)` oder `add_batch(...)` eingepflegt. Jeder Vektor enthält eine Sensor-ID sowie ein Dictionary mit Merkmalen.

+Im Normalbetrieb erzeugt die Reconciliation diese Vektoren selbst aus realer
+Home-Assistant-History. Das Trainingsmerkmal heißt dabei immer `value`.
+Binäre Kontextsensoren bleiben Kontext und werden nicht als numerische Samples
+missverstanden.
+
 ## 2. Statistisches Artefakt erzeugen

 ```python
@@ -61,6 +70,21 @@ zustandslose Funktion `retrain_model(registry, artifact_id, vectors)` aufrufen.
 Der Service startet bewusst keinen eigenen Hintergrundprozess. Über
 `POST /ml/retrain` kann derselbe Ablauf per API angestoßen werden.

+## 6. Autonomer Lebenszyklus
+
+Der `ActuatorReconciliationService` verwaltet pro konfiguriertem Aktuator:
+
+- die automatische Sensor- und Kontextzuordnung mit Score, Confidence und Evidenz
+- persistente manuelle Overrides
+- den Modellstatus (`trained`, `pending_history`, `review_required`, `archived`, ...)
+- ein Audit-Protokoll mit Gründen für Training, Retraining oder Archivierung
+
+Retraining erfolgt nur, wenn:
+
+- genügend nutzbare numerische Historie vorliegt
+- die aktuelle Zuordnung eindeutig oder manuell bestätigt ist
+- die Historie sich materiell verändert hat oder das Modell als stale gilt
+
 ## Hinweise
 - Für reproduzierbare Sensor-Reihenfolgen wird in `TrainingPipeline.run(...)` eine sortierte Sensor-Liste verwendet.
 - Fehlende Trainingsdaten lösen `ValueError` aus; nicht registrierte Artefakte lösen `KeyError` aus.
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "sillyhome-next"
-version = "0.3.0"
+version = "0.4.0"
 description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant"
 requires-python = ">=3.11"
 dependencies = [
--- a/tests/actuators/test_actuator_store.py
+++ b/tests/actuators/test_actuator_store.py
@@ -0,0 +1,18 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+from app.actuators.models import ReconciliationState
+from app.actuators.store import ActuatorStore
+
+
+def test_actuator_store_persists_record_and_reconciliation_state(tmp_path: Path) -> None:
+    store = ActuatorStore(tmp_path)
+    store.configure("light.abstellkammer")
+    state = ReconciliationState(last_summary="ok", configured_actuators=1)
+
+    store.save_reconciliation_state(state)
+
+    restarted = ActuatorStore(tmp_path)
+    assert restarted.get("light.abstellkammer").actuator_entity_id == "light.abstellkammer"
+    assert restarted.load_reconciliation_state().last_summary == "ok"
--- a/tests/actuators/test_lifecycle.py
+++ b/tests/actuators/test_lifecycle.py
@@ -0,0 +1,238 @@
+from __future__ import annotations
+
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+
+from app.actuators.lifecycle import ActuatorReconciliationService
+from app.actuators.models import (
+    AssignmentSource,
+    LifecycleStatus,
+    ManualOverride,
+    model_id_for_actuator,
+)
+from app.actuators.store import ActuatorStore
+from app.config import Settings
+from app.ha.discovery import DiscoveredEntity
+from app.ha.discovery import discover_entities
+from app.ha.history import EntityHistorySeries, NumericHistoryPoint
+from app.ha.models import HaEntitySummary
+from app.ha.reader import HaReader
+from app.ml.registry.model_registry import ModelRegistry
+
+
+class FakeActuatorReader(HaReader):
+    def __init__(
+        self,
+        entities: list[HaEntitySummary],
+        history_by_entity: dict[str, list[NumericHistoryPoint]],
+    ) -> None:
+        self._entities = entities
+        self._history_by_entity = history_by_entity
+
+    def read_entities(self) -> list[HaEntitySummary]:
+        return list(self._entities)
+
+    def discover(
+        self,
+        domains: set[str] | None = None,
+        learnable: bool | None = None,
+    ) -> list[DiscoveredEntity]:
+        return discover_entities(self._entities, domains=domains, learnable=learnable)
+
+    def read_history(
+        self,
+        entity_ids: list[str],
+        start_time: datetime,
+        end_time: datetime,
+    ) -> list[EntityHistorySeries]:
+        series: list[EntityHistorySeries] = []
+        for entity_id in entity_ids:
+            points = [
+                point
+                for point in self._history_by_entity.get(entity_id, [])
+                if start_time <= point.timestamp <= end_time
+            ]
+            if points:
+                series.append(EntityHistorySeries(entity_id=entity_id, points=points))
+        return series
+
+
+def _points(count: int, start: datetime, value: float) -> list[NumericHistoryPoint]:
+    return [
+        NumericHistoryPoint(timestamp=start + timedelta(hours=index), value=value + index)
+        for index in range(count)
+    ]
+
+
+def _service(
+    tmp_path: Path,
+    entities: list[HaEntitySummary],
+    history_by_entity: dict[str, list[NumericHistoryPoint]],
+) -> ActuatorReconciliationService:
+    return ActuatorReconciliationService(
+        ha_reader=FakeActuatorReader(entities, history_by_entity),
+        store=ActuatorStore(tmp_path / "actuators"),
+        registry=ModelRegistry(tmp_path / "models"),
+        settings=Settings(
+            ha_url="http://ha.local",
+            ha_token="token",
+            model_store=str(tmp_path / "models"),
+            automation_store=str(tmp_path / "automations"),
+            actuator_store=str(tmp_path / "actuators"),
+            history_days=14,
+            min_training_points=5,
+            retrain_stale_hours=24,
+            reconcile_interval_seconds=900,
+        ),
+    )
+
+
+def test_reconciliation_auto_assigns_and_trains_numeric_model(tmp_path: Path) -> None:
+    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
+    entities = [
+        HaEntitySummary(
+            entity_id="light.abstellkammer",
+            domain="light",
+            friendly_name="Abstellkammer Licht",
+            area_name="Abstellkammer",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.abstellkammer_illuminance",
+            domain="sensor",
+            device_class="illuminance",
+            state_class="measurement",
+            unit_of_measurement="lx",
+            friendly_name="Abstellkammer Helligkeit",
+            area_name="Abstellkammer",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.abstellkammer_motion",
+            domain="binary_sensor",
+            device_class="motion",
+            friendly_name="Abstellkammer Bewegung",
+            area_name="Abstellkammer",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.kitchen_temperature",
+            domain="sensor",
+            device_class="temperature",
+            state_class="measurement",
+            unit_of_measurement="°C",
+            friendly_name="Kueche Temperatur",
+            area_name="Kueche",
+        ),
+    ]
+    service = _service(
+        tmp_path,
+        entities,
+        {
+            "sensor.abstellkammer_illuminance": _points(8, start, 10.0),
+            "sensor.kitchen_temperature": _points(8, start, 18.0),
+        },
+    )
+
+    record = service.configure_actuator("light.abstellkammer")
+
+    assert record.assignment.selected_numeric_entity_id == "sensor.abstellkammer_illuminance"
+    assert record.assignment.selected_context_entity_ids == ["binary_sensor.abstellkammer_motion"]
+    assert record.assignment.review_required is False
+    assert record.lifecycle.status is LifecycleStatus.TRAINED
+    artifact = service._registry.load_artifact(model_id_for_actuator("light.abstellkammer"))
+    assert artifact.supported_sensors == ("sensor.abstellkammer_illuminance",)
+    assert "binary_sensor.abstellkammer_motion" not in artifact.supported_sensors
+
+
+def test_reconciliation_requires_review_for_ambiguous_sensor_mapping(tmp_path: Path) -> None:
+    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
+    entities = [
+        HaEntitySummary(
+            entity_id="switch.garage_pump",
+            domain="switch",
+            friendly_name="Garage Pumpe",
+            area_name="Garage",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.garage_power",
+            domain="sensor",
+            device_class="power",
+            state_class="measurement",
+            unit_of_measurement="W",
+            friendly_name="Garage Leistung",
+            area_name="Garage",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.garage_energy",
+            domain="sensor",
+            device_class="energy",
+            state_class="measurement",
+            unit_of_measurement="kWh",
+            friendly_name="Garage Energie",
+            area_name="Garage",
+        ),
+    ]
+    service = _service(
+        tmp_path,
+        entities,
+        {
+            "sensor.garage_power": _points(8, start, 10.0),
+            "sensor.garage_energy": _points(8, start, 11.0),
+        },
+    )
+
+    record = service.configure_actuator("switch.garage_pump")
+
+    assert record.assignment.review_required is True
+    assert record.lifecycle.status is LifecycleStatus.REVIEW_REQUIRED
+
+
+def test_manual_override_persists_and_wins_after_restart(tmp_path: Path) -> None:
+    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
+    entities = [
+        HaEntitySummary(
+            entity_id="light.abstellkammer",
+            domain="light",
+            friendly_name="Abstellkammer Licht",
+            area_name="Abstellkammer",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.abstellkammer_illuminance",
+            domain="sensor",
+            device_class="illuminance",
+            state_class="measurement",
+            unit_of_measurement="lx",
+            friendly_name="Abstellkammer Helligkeit",
+            area_name="Abstellkammer",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.abstellkammer_power",
+            domain="sensor",
+            device_class="power",
+            state_class="measurement",
+            unit_of_measurement="W",
+            friendly_name="Abstellkammer Leistung",
+            area_name="Abstellkammer",
+        ),
+    ]
+    history = {
+        "sensor.abstellkammer_illuminance": _points(8, start, 10.0),
+        "sensor.abstellkammer_power": _points(8, start, 30.0),
+    }
+    service = _service(tmp_path, entities, history)
+    service.configure_actuator("light.abstellkammer")
+
+    updated = service.set_override(
+        "light.abstellkammer",
+        ManualOverride(
+            numeric_entity_id="sensor.abstellkammer_power",
+            context_entity_ids=[],
+            note="Manuelle Leistungs-Zuordnung",
+        ),
+    )
+
+    restarted = _service(tmp_path, entities, history)
+    record = restarted.reconcile_actuator("light.abstellkammer")
+
+    assert updated.assignment.source is AssignmentSource.MANUAL
+    assert record.assignment.selected_numeric_entity_id == "sensor.abstellkammer_power"
+    assert record.manual_override is not None
+    assert record.manual_override.numeric_entity_id == "sensor.abstellkammer_power"
--- a/tests/api/test_actuators.py
+++ b/tests/api/test_actuators.py
@@ -0,0 +1,135 @@
+from __future__ import annotations
+
+from datetime import datetime, timedelta
+from pathlib import Path
+
+from fastapi.testclient import TestClient
+
+from app.actuators.lifecycle import ActuatorReconciliationService
+from app.actuators.store import ActuatorStore
+from app.config import Settings
+from app.ha.discovery import DiscoveredEntity
+from app.ha.discovery import discover_entities
+from app.ha.history import EntityHistorySeries, NumericHistoryPoint
+from app.ha.models import HaEntitySummary
+from app.ha.reader import HaReader
+from app.main import app
+from app.ml.registry.model_registry import ModelRegistry
+
+
+class FakeHaReader(HaReader):
+    def __init__(self, entities: list[HaEntitySummary], history: dict[str, list[float]]) -> None:
+        self._entities = entities
+        self._history = history
+
+    def read_entities(self) -> list[HaEntitySummary]:
+        return list(self._entities)
+
+    def discover(
+        self,
+        domains: set[str] | None = None,
+        learnable: bool | None = None,
+    ) -> list[DiscoveredEntity]:
+        return discover_entities(self._entities, domains=domains, learnable=learnable)
+
+    def read_history(
+        self,
+        entity_ids: list[str],
+        start_time: datetime,
+        end_time: datetime,
+    ) -> list[EntityHistorySeries]:
+        base = start_time
+        return [
+            EntityHistorySeries(
+                entity_id=entity_id,
+                points=[
+                    NumericHistoryPoint(
+                        timestamp=base + timedelta(hours=index),
+                        value=value,
+                    )
+                    for index, value in enumerate(self._history.get(entity_id, []))
+                ],
+            )
+            for entity_id in entity_ids
+            if entity_id in self._history
+        ]
+
+
+def _install_service(tmp_path: Path) -> None:
+    entities = [
+        HaEntitySummary(
+            entity_id="light.abstellkammer",
+            domain="light",
+            friendly_name="Abstellkammer Licht",
+            area_name="Abstellkammer",
+        ),
+        HaEntitySummary(
+            entity_id="sensor.abstellkammer_illuminance",
+            domain="sensor",
+            device_class="illuminance",
+            state_class="measurement",
+            unit_of_measurement="lx",
+            friendly_name="Abstellkammer Helligkeit",
+            area_name="Abstellkammer",
+        ),
+        HaEntitySummary(
+            entity_id="binary_sensor.abstellkammer_motion",
+            domain="binary_sensor",
+            device_class="motion",
+            friendly_name="Abstellkammer Bewegung",
+            area_name="Abstellkammer",
+        ),
+    ]
+    settings = Settings(
+        ha_url="http://ha.local",
+        ha_token="token",
+        model_store=str(tmp_path / "models"),
+        automation_store=str(tmp_path / "automations"),
+        actuator_store=str(tmp_path / "actuators"),
+        history_days=14,
+        min_training_points=5,
+        retrain_stale_hours=24,
+        reconcile_interval_seconds=900,
+    )
+    app.state.registry = ModelRegistry(tmp_path / "models")
+    app.state.actuator_store = ActuatorStore(tmp_path / "actuators")
+    app.state.ha_reader = FakeHaReader(
+        entities,
+        {"sensor.abstellkammer_illuminance": [10, 11, 12, 13, 14, 15]},
+    )
+    app.state.actuator_service = ActuatorReconciliationService(
+        ha_reader=app.state.ha_reader,
+        store=app.state.actuator_store,
+        registry=app.state.registry,
+        settings=settings,
+    )
+
+
+def test_actuator_api_configures_reconciles_and_overrides(tmp_path: Path) -> None:
+    with TestClient(app) as client:
+        _install_service(tmp_path)
+
+        created = client.post("/v1/actuators", json={"actuator_entity_id": "light.abstellkammer"})
+        assert created.status_code == 201
+        assert created.json()["assignment"]["selected_numeric_entity_id"] == (
+            "sensor.abstellkammer_illuminance"
+        )
+
+        listed = client.get("/v1/actuators")
+        assert listed.status_code == 200
+        assert listed.json()[0]["lifecycle"]["status"] == "trained"
+
+        override = client.post(
+            "/v1/actuators/light.abstellkammer/override",
+            json={
+                "numeric_entity_id": "sensor.abstellkammer_illuminance",
+                "context_entity_ids": ["binary_sensor.abstellkammer_motion"],
+                "note": "Explizit bestaetigt",
+            },
+        )
+        assert override.status_code == 200
+        assert override.json()["assignment"]["source"] == "manual"
+
+        reconciliation = client.post("/v1/actuators/reconciliation/run")
+        assert reconciliation.status_code == 200
+        assert reconciliation.json()["trained_models"] == 1
--- a/tests/api/test_entities.py
+++ b/tests/api/test_entities.py
@@ -78,6 +78,11 @@ def test_entities_returns_reader_data() -> None:
            "state_class": None,
            "device_class": None,
            "unit_of_measurement": None,
+            "friendly_name": None,
+            "area_id": None,
+            "area_name": None,
+            "device_id": None,
+            "device_name": None,
        }
    ]

--- a/tests/ha/test_ha_client.py
+++ b/tests/ha/test_ha_client.py
@@ -88,6 +88,26 @@ def test_get_history_calls_home_assistant_history_api() -> None:
    assert call.kwargs["params"]["end_time"] == "2026-06-02T00:00:00+00:00"


+def test_list_entity_metadata_calls_template_api() -> None:
+    response = _response()
+    response.text = (
+        '[{"entity_id":"sensor.temperature","area_name":"Kueche","device_name":"Thermometer"}]'
+    )
+    client = HaClient(HaClientSettings(url="http://ha.local", token="test-token"))
+    client._session.post = Mock(return_value=response)  # type: ignore[method-assign]
+
+    metadata = client.list_entity_metadata(["sensor.temperature"])
+
+    assert metadata == {
+        "sensor.temperature": {
+            "area_id": None,
+            "area_name": "Kueche",
+            "device_id": None,
+            "device_name": "Thermometer",
+        }
+    }
+
+
@pytest.mark.parametrize(
    ("entity_ids", "start", "end"),
    [
--- a/tests/ha/test_ha_reader.py
+++ b/tests/ha/test_ha_reader.py
@@ -44,6 +44,16 @@ class FakeHaClient(HaClient):
            ]
        ]

+    def list_entity_metadata(self, entity_ids: list[str]) -> dict[str, dict[str, str | None]]:
+        return {
+            "sensor.temperature": {
+                "area_id": "kitchen",
+                "area_name": "Kueche",
+                "device_id": "device-1",
+                "device_name": "Thermometer",
+            }
+        }
+

 def test_ha_reader_returns_summaries() -> None:
    reader = HaReader(FakeHaClient())
@@ -53,6 +63,8 @@ def test_ha_reader_returns_summaries() -> None:
    assert domains == {"sensor", "light"}
    sensor = next(item for item in summaries if item.entity_id == "sensor.temperature")
    assert sensor.unit_of_measurement == "°C"
+    assert sensor.area_name == "Kueche"
+    assert sensor.device_name == "Thermometer"


 def test_ha_reader_discovers_learnable_sensors() -> None:
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -10,6 +10,11 @@ def test_load_settings_reads_documented_environment(monkeypatch: MonkeyPatch) ->
    monkeypatch.setenv("SILLYHOME_HA_TOKEN", "secret")
    monkeypatch.setenv("SILLYHOME_MODEL_STORE", "/tmp/models")
    monkeypatch.setenv("SILLYHOME_AUTOMATION_STORE", "/tmp/automations")
+    monkeypatch.setenv("SILLYHOME_ACTUATOR_STORE", "/tmp/actuators")
+    monkeypatch.setenv("SILLYHOME_HISTORY_DAYS", "7")
+    monkeypatch.setenv("SILLYHOME_MIN_TRAINING_POINTS", "12")
+    monkeypatch.setenv("SILLYHOME_RETRAIN_STALE_HOURS", "48")
+    monkeypatch.setenv("SILLYHOME_RECONCILE_INTERVAL_SECONDS", "600")

    settings = load_settings()

@@ -17,4 +22,9 @@ def test_load_settings_reads_documented_environment(monkeypatch: MonkeyPatch) ->
    assert settings.ha_token == "secret"
    assert settings.model_store == "/tmp/models"
    assert settings.automation_store == "/tmp/automations"
+    assert settings.actuator_store == "/tmp/actuators"
+    assert settings.history_days == 7
+    assert settings.min_training_points == 12
+    assert settings.retrain_stale_hours == 48
+    assert settings.reconcile_interval_seconds == 600
    assert settings.ha_configured