feat: realtime WebSocket layer for live events and streaming logs

- Backend: ConnectionManager, Redis-backed bus with in-process fallback,
  channel authorization, and typed emit helpers wired into workers
  (ai_runtime, sandbox_gateway), workforce/sandbox/billing/audit/error services.
- Frontend: single shared WS client with backoff + heartbeat, RealtimeProvider,
  useLiveChannel/useLiveEvent hooks, live indicators on task detail, manager
  chat, dashboard, traces, sandbox terminal, admin errors, and billing.
- Contracts: WsEvent/WsServerFrame/WsClientFrame types mirroring the port.

All 255 backend tests pass; web typecheck and build pass.

apps/api/app/workers/ai_runtime.py

@@ -14,6 +14,7 @@ the service's business.
 from __future__ import annotations
 
 from app.application.container import build_workforce_service
+from app.application.ports.realtime import emit_worker_status
 from app.core.errors import AppError
 from app.domain.audit.models import ErrorSeverity, ErrorSource
 from app.domain.workforce.models import TaskStatus
@@ -87,6 +88,13 @@ class AiRuntimeWorker:
 
                 outcome.processed += 1
                 outcome.note(f"task {task.id} -> run {run.id} ({run.status})")
+                # This worker is its own process: without a push here, a run
+                # started by the queue is invisible until someone reloads.
+                await emit_worker_status(
+                    organization_id=task.organization_id,
+                    worker=self.name,
+                    message=f"کار «{current.title}» در حال اجراست (اجرا {run.id}).",
+                )
 
         return outcome