// 简化版核心循环伪代码（基于 OpenClaw 源码）
// 文件：src/core/agent-loop.ts

class AutonomousAgent {
  private memory: MemorySystem;
  private orchestrator: Orchestrator;
  private skillsRuntime: SkillsRuntime;
  private eventBus: EventBus;
  
  // 核心循环入口
  async startCycle(trigger: Trigger): Promise<void> {
    try {
      // ========== 阶段 1: 感知 ==========
      const context = await this.perceive(trigger);
      
      // ========== 阶段 2: 思考 ==========
      const plan = await this.think(context);
      
      // ========== 阶段 3: 行动 ==========
      const results = await this.act(plan);
      
      // ========== 阶段 4: 反馈 ==========
      await this.feedback(results, context);
      
    } catch (error) {
      await this.handleError(error);
    }
  }
  
  // 阶段 1: 感知 - 收集上下文信息
  private async perceive(trigger: Trigger): Promise<AgentContext> {
    // 1. 提取触发信息
    const triggerData = await this.extractTriggerData(trigger);
    
    // 2. 从记忆系统检索相关历史
    const relevantMemories = await this.memory.retrieve({
      query: triggerData.content,
      limit: 10,
      timeRange: 'last_24h'
    });
    
    // 3. 获取用户画像和偏好
    const userProfile = await this.memory.getUserProfile(trigger.userId);
    
    // 4. 检查当前系统状态
    const systemState = await this.getSystemState();
    
    return {
      trigger: triggerData,
      memories: relevantMemories,
      userProfile,
      systemState,
      timestamp: Date.now()
    };
  }
  
  // 阶段 2: 思考 - 制定执行计划
  private async think(context: AgentContext): Promise<ExecutionPlan> {
    // 1. 构建 Prompt
    const prompt = this.buildPlanningPrompt(context);
    
    // 2. 调用 LLM 进行推理
    const llmResponse = await this.orchestrator.callLLM({
      model: context.userProfile.preferredModel,
      prompt: prompt,
      temperature: 0.3, // 较低温度确保规划稳定性
      maxTokens: 2000
    });
    
    // 3. 解析 LLM 输出的计划
    const plan = this.parsePlanFromLLM(llmResponse);
    
    // 4. 验证计划的可行性
    await this.validatePlan(plan);
    
    return plan;
  }
  
  // 阶段 3: 行动 - 执行计划中的步骤
  private async act(plan: ExecutionPlan): Promise<ActionResult[]> {
    const results: ActionResult[] = [];
    
    // 按照依赖顺序执行每个步骤
    for (const step of plan.steps) {
      try {
        // 1. 准备执行环境（沙箱）
        const sandbox = await this.skillsRuntime.createSandbox({
          permissions: step.requiredPermissions,
          timeout: step.timeout || 30000
        });
        
        // 2. 加载并执行 Skill
        const skillResult = await sandbox.execute(step.skillName, step.parameters);
        
        // 3. 记录执行结果
        results.push({
          stepId: step.id,
          success: true,
          output: skillResult.output,
          duration: skillResult.duration,
          timestamp: Date.now()
        });
        
        // 4. 如果是关键步骤，检查是否需要调整后续计划
        if (step.isCritical) {
          const shouldContinue = await this.evaluateIntermediateResult(skillResult);
          if (!shouldContinue) {
            break; // 中止后续步骤
          }
        }
        
      } catch (error) {
        // 记录失败并决定是否重试
        const retryDecision = await this.handleStepError(step, error);
        if (retryDecision.shouldRetry) {
          // 重试逻辑...
        } else {
          results.push({
            stepId: step.id,
            success: false,
            error: error.message,
            timestamp: Date.now()
          });
          if (step.isBlocking) {
            break; // 阻塞性错误，停止执行
          }
        }
      }
    }
    
    return results;
  }
  
  // 阶段 4: 反馈 - 评估结果并学习
  private async feedback(results: ActionResult[], context: AgentContext): Promise<void> {
    // 1. 汇总所有结果
    const summary = this.summarizeResults(results);
    
    // 2. 判断整体任务是否成功
    const overallSuccess = results.every(r => r.success);
    
    // 3. 将经验存入长期记忆
    await this.memory.store({
      type: 'experience',
      content: {
        trigger: context.trigger,
        plan: context.plan,
        results: results,
        success: overallSuccess,
        lessons: this.extractLessons(results)
      },
      embeddings: await this.generateEmbeddings(summary)
    });
    
    // 4. 向用户发送反馈
    await this.notifyUser({
      userId: context.trigger.userId,
      message: this.formatUserMessage(summary),
      attachments: this.generateAttachments(results)
    });
    
    // 5. 如果失败且可学习，触发强化学习流程
    if (!overallSuccess && this.canLearnFromFailure(results)) {
      await this.triggerReinforcementLearning(context, results);
    }
  }
}