feat: background task dispatch system with live UI panel

- agent/task_registry.py: file-based JSONL event registry (cross-process)
- agent/task_worker.py: autonomous LLM loop with weather/time/memory/web tools
- agent/dispatch_mcp.py: MCP tool exposing dispatch_task to the main agent
- agent/agent.py: registers dispatch toolset, polls task events → room data
- web: slide-out task panel (FAB button + badge), live step streaming via
  data channel topic 'tasks', status dots (running/completed/failed)
- Dockerfile: copies new task_*.py and dispatch_mcp.py files

The dispatch MCP runs in its own process; events flow through
/tmp/tasks/events.jsonl which the main agent tails every second and
forwards to the browser. Tasks run up to 10 LLM iterations with tool calls.

agent/task_worker.py

@@ -25,13 +25,16 @@ class TaskWorker:
 
     async def run(self):
         from openai import AsyncOpenAI
-        from agent.task_registry import registry as reg
+        try:
+            from agent.task_registry import registry as reg
+        except ImportError:
+            from task_registry import registry as reg
 
         base_url = os.environ.get("GEMMA_BASE_URL", "http://192.168.86.2:8023/v1")
         model = os.environ.get("GEMMA_MODEL", "gemma-4-e4b")
         api_key = os.environ.get("GEMMA_API_KEY", "not-needed")
 
-        await reg.add_step(self.task_id, "thinking", f"Starting: {self.description}")
+        reg.add_step(self.task_id, "thinking", f"Starting: {self.description}")
 
         client = AsyncOpenAI(base_url=base_url, api_key=api_key)
         tools = self._load_tools()
@@ -44,10 +47,10 @@ class TaskWorker:
         max_iterations = 10
         for iteration in range(max_iterations):
             if self._cancelled:
-                await reg.fail(self.task_id, "Cancelled")
+                reg.fail(self.task_id, "Cancelled")
                 return
 
-            await reg.add_step(
+            reg.add_step(
                 self.task_id, "thinking", f"Step {iteration + 1}: analyzing..."
             )
 
@@ -61,7 +64,7 @@ class TaskWorker:
                     extra_body={"chat_template_kwargs": {"enable_thinking": False}},
                 )
             except Exception as e:
-                await reg.fail(self.task_id, f"LLM error: {e}")
+                reg.fail(self.task_id, f"LLM error: {e}")
                 return
 
             msg = response.choices[0].message
@@ -74,13 +77,13 @@ class TaskWorker:
                         tool_args = json.loads(tc.function.arguments)
                     except json.JSONDecodeError:
                         tool_args = {}
-                    await reg.add_step(
+                    reg.add_step(
                         self.task_id,
                         "tool_call",
                         f"{tool_name}({json.dumps(tool_args)})",
                     )
                     result = await self._execute_tool(tool_name, tool_args)
-                    await reg.add_step(self.task_id, "tool_result", str(result)[:500])
+                    reg.add_step(self.task_id, "tool_result", str(result)[:500])
                     messages.append(
                         {
                             "role": "tool",
@@ -91,13 +94,13 @@ class TaskWorker:
             else:
                 text = (msg.content or "").strip()
                 if not text:
-                    await reg.fail(self.task_id, "LLM returned empty response")
+                    reg.fail(self.task_id, "LLM returned empty response")
                     return
-                await reg.add_step(self.task_id, "text", text)
-                await reg.complete(self.task_id, text)
+                reg.add_step(self.task_id, "text", text)
+                reg.complete(self.task_id, text)
                 return
 
-        await reg.fail(self.task_id, f"Reached max iterations ({max_iterations})")
+        reg.fail(self.task_id, f"Reached max iterations ({max_iterations})")
 
     def _system_prompt(self) -> str:
         return (