perf(loop): parallelize tool execution with asyncio.gather

Tool calls from a single LLM response are independent by design —
the model batches them precisely because they can run concurrently.
Replace the serial for-loop with asyncio.gather so N tools complete
in max(time_i) instead of sum(time_i).

Made-with: Cursor

nanobot/agent/loop.py

@@ -243,11 +243,19 @@ class AgentLoop:
                     thinking_blocks=response.thinking_blocks,
                 )
 
-                for tool_call in response.tool_calls:
-                    tools_used.append(tool_call.name)
-                    args_str = json.dumps(tool_call.arguments, ensure_ascii=False)
-                    logger.info("Tool call: {}({})", tool_call.name, args_str[:200])
-                    result = await self.tools.execute(tool_call.name, tool_call.arguments)
+                for tc in response.tool_calls:
+                    tools_used.append(tc.name)
+                    args_str = json.dumps(tc.arguments, ensure_ascii=False)
+                    logger.info("Tool call: {}({})", tc.name, args_str[:200])
+
+                # Execute all tool calls concurrently — the LLM batches
+                # independent calls in a single response on purpose.
+                results = await asyncio.gather(*(
+                    self.tools.execute(tc.name, tc.arguments)
+                    for tc in response.tool_calls
+                ))
+
+                for tool_call, result in zip(response.tool_calls, results):
                     messages = self.context.add_tool_result(
                         messages, tool_call.id, tool_call.name, result
                     )