feat(F): F-PERM permissions, F-APPR approval, F-AIUSE metadata, F-TRANS transparency, F-DATA-POL data policy, F-OVERSIGHT decision record, F-DRY dry-run, F-AUDIT audit log

- F-PERM: app/ai/agent_permissions.py (230 lines) — AgentPermissionContext, resolve_agent_permissions(), filter_visible_agents(), check_agent_execute_permission(), optimistic locking
- F-APPR: app/core/approval.py (160 lines) + app/routes/approvals.py (305 lines) + migration 0123 — ApprovalRequest model, CRUD API, approve/reject/expire
- F-AIUSE: app/ai/ai_use_case.py (156 lines) — AIUseCaseMetadata Pydantic model, validate_ai_use_case()
- F-TRANS: app/ai/transparency.py (60 lines) — mark_as_ai_generated(), is_ai_participant()
- F-DATA-POL: app/ai/data_policy.py (210 lines) — enforce_data_policy() with SENSITIVE_FIELDS + provider compliance
- F-OVERSIGHT: app/ai/oversight.py (108 lines) — DecisionRecord, create_decision_record()
- F-DRY: agent_loop.py updated with dry_run parameter
- F-AUDIT: agent_loop.py updated with audit log for tool calls
- agent_routes.py: AI use case metadata endpoints added
- main.py: approval routes registered
- All Python compile checks pass

app/ai/agent_loop.py

@@ -148,6 +148,7 @@ async def run_react_loop(
     timeout_seconds: int = 300,
     trace_id: str | None = None,
     on_step: Callable | None = None,
+    dry_run: bool = False,
 ) -> ReActResult:
     """Execute a ReAct loop: LLM reasoning → tool execution → repeat.
 
@@ -164,6 +165,9 @@ async def run_react_loop(
         timeout_seconds: Overall timeout (default 300).
         trace_id: Optional trace ID for correlation.
         on_step: Optional async callback fired after each step.
+        dry_run: When True, tool execution is simulated — tool handlers are
+            NOT called. A mock result is returned instead and steps are still
+            logged with real LLM cost.
 
     Returns:
         ReActResult with final content, steps, cost, and status.
@@ -193,6 +197,38 @@ async def run_react_loop(
         "db": db,
     }
 
+    # Audit helper — records every tool call in the audit log.
+    async def _audit_tool_call(
+        step_number: int,
+        tool_name: str,
+        arguments: dict[str, Any],
+        result: str,
+        cost_usd: float,
+    ) -> None:
+        """Create an audit log entry for a single tool call."""
+        try:
+            from app.core.audit import log_audit
+
+            await log_audit(
+                db=db,
+                tenant_id=tenant_id,
+                user_id=user_id,
+                action="agent.tool_call",
+                entity_type="agent_run",
+                entity_id=agent_run_id,
+                details={
+                    "agent_run_id": str(agent_run_id) if agent_run_id else None,
+                    "step_number": step_number,
+                    "tool_name": tool_name,
+                    "arguments": arguments,
+                    "result": result[:2000],
+                    "cost_usd": cost_usd,
+                    "dry_run": dry_run,
+                },
+            )
+        except Exception:
+            logger.exception("Failed to audit tool call '%s'", tool_name)
+
     for step_num in range(1, max_steps + 1):
         # ── Timeout check ──
         elapsed = time.monotonic() - start_time
@@ -327,9 +363,27 @@ async def run_react_loop(
                 args = {}
                 logger.warning("Invalid JSON arguments for tool '%s': %s", tool_name, tc["arguments"])
 
-            observation = await _execute_tool(tool_registry, tool_name, args, tool_context)
+            if dry_run:
+                observation = json.dumps(
+                    {
+                        "dry_run": True,
+                        "would_execute": tool_name,
+                        "arguments": args,
+                    }
+                )
+            else:
+                observation = await _execute_tool(tool_registry, tool_name, args, tool_context)
             observations.append(observation)
 
+            # Audit every tool call (real or simulated)
+            await _audit_tool_call(
+                step_number=step_num,
+                tool_name=tool_name,
+                arguments=args,
+                result=observation,
+                cost_usd=cost_usd / len(tool_calls) if tool_calls else cost_usd,
+            )
+
             # Feed tool result back into conversation
             full_messages.append({
                 "role": "tool",