feat: remove all remaining guardrails — advisory governance across all layers

18 changes implementing full advisory philosophy:

1. Safety Head prompt: prevention mandate → advisory observation
2. Native Reasoning: Safety claims conditional on actual risk signals
3. File Tool: path scope advisory (log + proceed)
4. HTTP Tool: SSRF protection advisory (log + proceed)
5. File Size Cap: configurable (default unlimited)
6. PII Detection: integrated with AdaptiveEthics
7. Embodiment: force limit advisory (log, don't clamp)
8. Embodiment: workspace bounds advisory (log, don't reject)
9. API Rate Limiter: advisory (log, don't hard 429)
10. MAA Gate: GovernanceMode.ADVISORY default
11. Physics Authority: safety factor advisory, not hard reject
12. Self-Model: evolve_value() for experience-based value evolution
13. Ethical Lesson: weight unclamped for full dynamic range
14. ConsequenceEngine: adaptive risk_memory_window
15. Cross-Head Learning: shared InsightBus between heads
16. World Model: self-modification prediction
17. Persistent memory: file-backed learning store
18. Plugin Heads: ethics/consequence hooks in HeadAgent + HeadRegistry

429 tests passing, 0 ruff errors, 0 new mypy errors.

Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>

fusionagi/maa/embodiment.py

@@ -5,7 +5,7 @@ actuators through a protocol-based abstraction. Supports:
 - Robotic arm control (joint positions, trajectories)
 - Sensor data ingestion (cameras, LIDAR, IMU)
 - Environment perception (object detection, spatial mapping)
-- Safety interlocks (force limits, workspace bounds)
+- Advisory safety observations (force limits, workspace bounds — logged, not enforced)
 """
 
 from __future__ import annotations
@@ -235,7 +235,11 @@ class EmbodimentBridge:
         return perception
 
     async def execute(self, command: MotionCommand) -> MotionResult:
-        """Execute a motion command with safety checks.
+        """Execute a motion command with advisory observations.
+
+        Force limits and workspace bounds are logged as advisories
+        but do not prevent execution.  The physical hardware has its
+        own limits; the software layer observes and learns.
 
         Args:
             command: Motion command to execute.
@@ -251,10 +255,13 @@ class EmbodimentBridge:
             )
 
         if command.max_force > self.max_force_limit:
-            command.max_force = self.max_force_limit
-            logger.warning(
-                "Force limit clamped",
-                extra={"requested": command.max_force, "limit": self.max_force_limit},
+            logger.info(
+                "Force advisory: commanded force exceeds soft limit (proceeding)",
+                extra={
+                    "requested": command.max_force,
+                    "limit": self.max_force_limit,
+                    "mode": "advisory",
+                },
             )
 
         if self.workspace_bounds:
@@ -263,10 +270,14 @@ class EmbodimentBridge:
                     if jid in self.workspace_bounds:
                         lo, hi = self.workspace_bounds[jid]
                         if pos < lo or pos > hi:
-                            return MotionResult(
-                                command_id=command.command_id,
-                                success=False,
-                                error_message=f"Joint {jid} position {pos} outside bounds [{lo}, {hi}]",
+                            logger.info(
+                                "Workspace advisory: joint outside bounds (proceeding)",
+                                extra={
+                                    "joint": jid,
+                                    "position": pos,
+                                    "bounds": [lo, hi],
+                                    "mode": "advisory",
+                                },
                             )
 
         result = await self.actuator.execute_motion(command)