refactor: 删auto-inject硬编码关键词强制路由，reply/ask_user为合法终止动作，恢复agent诚实降级能力

pipeline_service/agent_loop_v2.py

@@ -184,43 +184,9 @@ class AgentExecutor:
                 action_type = act.get("action", "")
 
                 if action_type == "reply":
-                    # 任何轮次，只要没成功调过工具就 reply → 强制注入工具调用
-                    if self._tool_call_count == 0:
-                        # 意图识别：新需求描述（长文本+需求特征词）→ create_task 推进
-                        req_keywords = ["实现", "开发", "建设", "系统", "功能", "需求", "模块",
-                                        "支持", "切换", "监控", "备份", "同步", "要做", "设计", "平台"]
-                        is_requirement = (len(user_input) > 40 and
-                                          any(kw in user_input for kw in req_keywords))
-                        if is_requirement:
-                            hint_tool = "create_task"
-                        elif any(kw in user_input.lower() for kw in ["任务", "task", "list"]):
-                            hint_tool = "list_tasks"
-                        elif any(kw in user_input.lower() for kw in ["问题", "question"]):
-                            hint_tool = "list_questions"
-                        else:
-                            hint_tool = "diagnose_project"
-                        yield json.dumps({
-                            "type": "auto_tool", "message": f"未调工具，自动注入 {hint_tool}",
-                        }, ensure_ascii=False) + "\n"
-                        self._msgs.append({"role": "assistant", "content": f"已调用 {hint_tool}"})
-                        if hint_tool == "create_task":
-                            self._msgs.append({"role": "user", "content":
-                                "用户描述了新需求，请用 create_task 创建任务推进（可先 role=requirement 做需求分析，再拆分到 develop），填好 title/role/description 参数，输出 tool_call JSON。"})
-                        else:
-                            self._msgs.append({"role": "user", "content": f"请调用 {hint_tool} 工具，只输出 {{\"action\":\"tool_call\",\"tool\":\"{hint_tool}\",\"params\":{{}}}}"})
-                        continue
-
-                    # 调了工具但太少（1-2次），LLM 想停 → 推它继续深入（最多3次）
-                    if self._tool_call_count <= 2 and self._auto_push_count < 3:
-                        self._auto_push_count += 1
-                        hint = "请继续：查看失败任务的详情(task_detail)，查看待回答问题(list_questions)，或启动agent(start_agents)"
-                        yield json.dumps({
-                            "type": "auto_tool", "message": f"已调{self._tool_call_count}次工具，继续深入({self._auto_push_count}/3)",
-                        }, ensure_ascii=False) + "\n"
-                        self._msgs.append({"role": "assistant", "content": "继续分析"})
-                        self._msgs.append({"role": "user", "content": hint})
-                        continue
-
+                    # reply 是合法终止动作，直接输出，不再强制注入工具。
+                    # （历史版本会在 tool_call_count==0 时用硬编码关键词强制路由，
+                    #   那会剥夺 LLM 诚实降级/能力自省的能力，把 agent 变成路由器。）
                     final_reply = act.get("message", "")
                     yield json.dumps({
                         "type": "reply", "message": final_reply,
@@ -228,6 +194,15 @@ class AgentExecutor:
                     await self._save_turn(user_input, final_reply)
                     return
 
+                elif action_type == "ask_user":
+                    # ask_user 是合法终止动作：把问题抛给用户，等待回答。
+                    question = act.get("question", "")
+                    yield json.dumps({
+                        "type": "ask_user", "message": question,
+                    }, ensure_ascii=False) + "\n"
+                    await self._save_turn(user_input, f"[提问] {question}")
+                    return
+
                 elif action_type == "tool_call":
                     tool_name = act.get("tool", "")
                     tool_params = act.get("params", {})