diff --git a/GramAddict/core/navigation/planner.py b/GramAddict/core/navigation/planner.py
index 25e4f93..5caa82a 100644
--- a/GramAddict/core/navigation/planner.py
+++ b/GramAddict/core/navigation/planner.py
@@ -101,7 +101,17 @@ class GoalPlanner:
                 if count >= 2:  # MAX_RETRIES is 2 in goap
                     avoid_actions.add(act)
 
-        # ── 1. HD Map Routing (Primary Strategy) ──
+        # ── 1. Brain-Driven Decision Making (Primary Strategy) ──
+        # The user explicitly wants the AI to be the primary driver of goals.
+        from GramAddict.core.navigation.brain import ask_brain_for_action
+
+        brain_action = ask_brain_for_action(goal, screen_type.name, available, explored_nav_actions)
+        if brain_action:
+            logger.info(f"🧠 [Brain] Decided dynamically to execute: '{brain_action}'")
+            return brain_action
+
+        # ── 2. HD Map Routing (Fallback) ──
+        # If the Brain doesn't know what to do, try the deterministic topological map.
         target_screen = ScreenTopology.goal_to_target_screen(goal)
         if target_screen and target_screen != screen_type:
             route = ScreenTopology.find_route(screen_type, target_screen, avoid_actions=avoid_actions)
@@ -116,21 +126,12 @@ class GoalPlanner:
                         )
                         return next_action
                     else:
-                        logger.warning(f"🛡️ [HD Map] Route action '{next_action}' is trapped. Falling back to brain.")
+                        logger.warning(f"🛡️ [HD Map] Route action '{next_action}' is trapped. Skipping HD Map.")
                 else:
                     logger.debug(
-                        f"🛡️ [HD Map] Route action '{next_action}' already explored and failed. Falling back to brain."
+                        f"🛡️ [HD Map] Route action '{next_action}' already explored and failed. Skipping HD Map."
                     )
 
-        # ── 1.5 Brain-Driven Decision Making (Preventing Trapped States) ──
-        # If HD Map didn't yield a valid action, or we're stuck, ask the AI brain.
-        from GramAddict.core.navigation.brain import ask_brain_for_action
-
-        brain_action = ask_brain_for_action(goal, screen_type.name, available, explored_nav_actions)
-        if brain_action:
-            logger.info(f"🧠 [Brain] Decided dynamically to execute: '{brain_action}'")
-            return brain_action
-
         # ── 2. Learned Knowledge (Qdrant) ──
         required_screens = self.knowledge.get_requirements(goal)