Agent 产品设计原则与交互范式

import time
import json
import math
import random
from typing import Dict, List, Any, Optional, Tuple, Set
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
import numpy as np
from collections import deque, defaultdict
import threading
import uuid
from abc import ABC, abstractmethod

class DesignPrinciple(Enum):
    """设计原则"""
    HUMAN_CENTERED = "human_centered"
    TRANSPARENT = "transparent"
    CONTROLLABLE = "controllable"
    PROGRESSIVE = "progressive"
    TRUSTWORTHY = "trustworthy"

class InteractionParadigm(Enum):
    """交互范式"""
    CONVERSATIONAL = "conversational"
    PROACTIVE = "proactive"
    MULTIMODAL = "multimodal"
    COLLABORATIVE = "collaborative"

class ExperienceDimension(Enum):
    """体验维度"""
    TRUST = "trust"
    EXPECTATION = "expectation"
    ERROR_RECOVERY = "error_recovery"
    EMOTIONAL_CONNECTION = "emotional_connection"
    SATISFACTION = "satisfaction"

@dataclass
class DesignGuideline:
    """设计指南"""
    principle: DesignPrinciple
    guideline: str
    description: str
    examples: List[str]
    anti_patterns: List[str]
    priority: int  # 1-5

@dataclass
class InteractionPattern:
    """交互模式"""
    paradigm: InteractionParadigm
    pattern_name: str
    description: str
    use_cases: List[str]
    implementation_tips: List[str]
    pitfalls: List[str]

@dataclass
class ExperienceMetric:
    """体验指标"""
    dimension: ExperienceDimension
    metric_name: str
    measurement_method: str
    target_value: float
    current_value: float
    trend: str  # improving, stable, declining

@dataclass
class ProductEvaluation:
    """产品评估"""
    product_name: str
    evaluation_date: datetime
    design_score: float
    interaction_score: float
    experience_score: float
    overall_score: float
    strengths: List[str]
    weaknesses: List[str]
    recommendations: List[str]

class AgentProductDesigner:
    """
    Agent 产品设计师
    
    支持:
    1. 设计原则评估
    2. 交互模式推荐
    3. 体验指标监控
    4. 改进建议生成
    """
    
    def __init__(self):
        self.design_guidelines = self._initialize_guidelines()
        self.interaction_patterns = self._initialize_patterns()
        self.experience_metrics = self._initialize_metrics()
    
    def _initialize_guidelines(self) -> List[DesignGuideline]:
        """初始化设计指南"""
        return [
            DesignGuideline(
                principle=DesignPrinciple.HUMAN_CENTERED,
                guideline="始终以用户需求为中心",
                description="设计决策应基于真实用户需求和场景，而非技术能力",
                examples=["用户明确请求时才执行操作", "提供简单易懂的默认选项", "尊重用户偏好和习惯"],
                anti_patterns=["强制用户使用高级功能", "忽视用户反馈", "技术驱动而非需求驱动"],
                priority=5
            ),
            DesignGuideline(
                principle=DesignPrinciple.TRANSPARENT,
                guideline="保持系统行为透明",
                description="用户应清楚了解 Agent 在做什么、为什么这样做",
                examples=["显示思考过程", "解释决策依据", "公开能力边界"],
                anti_patterns=["黑箱操作", "隐瞒不确定性", "夸大能力"],
                priority=5
            ),
            DesignGuideline(
                principle=DesignPrinciple.CONTROLLABLE,
                guideline="赋予用户控制权",
                description="用户应能随时干预、停止或修改 Agent 行为",
                examples=["提供撤销功能", "允许手动覆盖", "设置自动化级别"],
                anti_patterns=["无法中断执行", "强制自动化", "忽视用户干预"],
                priority=5
            ),
            DesignGuideline(
                principle=DesignPrinciple.PROGRESSIVE,
                guideline="渐进式披露信息",
                description="根据用户需求和上下文逐步展示信息，避免信息过载",
                examples=["默认显示关键信息", "按需展开详情", "分层展示复杂度"],
                anti_patterns=["一次性展示所有信息", "隐藏重要信息", "信息架构混乱"],
                priority=4
            ),
            DesignGuideline(
                principle=DesignPrinciple.TRUSTWORTHY,
                guideline="建立可信赖形象",
                description="通过一致性、可靠性和诚实建立用户信任",
                examples=["承认错误", "保持一致行为", "不承诺无法实现的功能"],
                anti_patterns=["前后矛盾", "推卸责任", "过度承诺"],
                priority=5
            )
        ]
    
    def _initialize_patterns(self) -> List[InteractionPattern]:
        """初始化交互模式"""
        return [
            InteractionPattern(
                paradigm=InteractionParadigm.CONVERSATIONAL,
                pattern_name="自然对话流",
                description="模拟人类自然对话的交互模式",
                use_cases=["客服问答", "个人助理", "教育辅导"],
                implementation_tips=["使用自然语言", "保持上下文记忆", "支持多轮对话"],
                pitfalls=["过度拟人化", "忽视效率", "对话冗长"]
            ),
            InteractionPattern(
                paradigm=InteractionParadigm.PROACTIVE,
                pattern_name="智能主动建议",
                description="基于上下文主动提供建议和帮助",
                use_cases=["日程管理", "健康提醒", "风险预警"],
                implementation_tips=["基于明确规则触发", "提供关闭选项", "控制频率"],
                pitfalls=["过度打扰", "误判意图", "侵犯隐私"]
            ),
            InteractionPattern(
                paradigm=InteractionParadigm.MULTIMODAL,
                pattern_name="多模态融合",
                description="结合文本、语音、图像等多种交互方式",
                use_cases=["复杂任务", "无障碍访问", "沉浸式体验"],
                implementation_tips=["无缝切换模态", "保持一致性", "考虑场景适配"],
                pitfalls=["模态冲突", "增加复杂度", "忽视可访问性"]
            ),
            InteractionPattern(
                paradigm=InteractionParadigm.COLLABORATIVE,
                pattern_name="人机协作",
                description="人类与 Agent 共同参与决策和执行",
                use_cases=["创意工作", "复杂分析", "关键决策"],
                implementation_tips=["明确分工", "支持人类主导", "提供协作工具"],
                pitfalls=["角色模糊", "过度依赖", "责任不清"]
            )
        ]
    
    def _initialize_metrics(self) -> Dict[ExperienceDimension, ExperienceMetric]:
        """初始化体验指标"""
        return {
            ExperienceDimension.TRUST: ExperienceMetric(
                dimension=ExperienceDimension.TRUST,
                metric_name="信任指数",
                measurement_method="用户调查 + 行为分析",
                target_value=8.5,
                current_value=7.2,
                trend="improving"
            ),
            ExperienceDimension.EXPECTATION: ExperienceMetric(
                dimension=ExperienceDimension.EXPECTATION,
                metric_name="期望匹配度",
                measurement_method="任务完成率 + 满意度",
                target_value=9.0,
                current_value=7.8,
                trend="stable"
            ),
            ExperienceDimension.ERROR_RECOVERY: ExperienceMetric(
                dimension=ExperienceDimension.ERROR_RECOVERY,
                metric_name="错误恢复率",
                measurement_method="错误后任务继续率",
                target_value=85.0,
                current_value=72.0,
                trend="improving"
            ),
            ExperienceDimension.EMOTIONAL_CONNECTION: ExperienceMetric(
                dimension=ExperienceDimension.EMOTIONAL_CONNECTION,
                metric_name="情感连接度",
                measurement_method="NPS + 情感分析",
                target_value=7.5,
                current_value=6.3,
                trend="stable"
            ),
            ExperienceDimension.SATISFACTION: ExperienceMetric(
                dimension=ExperienceDimension.SATISFACTION,
                metric_name="整体满意度",
                measurement_method="CSAT 调查",
                target_value=9.0,
                current_value=8.1,
                trend="improving"
            )
        }
    
    def evaluate_design(self, 
                       product_features: List[str],
                       target_users: str,
                       use_cases: List[str]) -> ProductEvaluation:
        """评估产品设计"""
        # 评估设计原则遵循度
        design_score = self._evaluate_design_principles(product_features)
        
        # 评估交互模式适配度
        interaction_score = self._evaluate_interaction_patterns(use_cases)
        
        # 评估用户体验质量
        experience_score = self._evaluate_experience(target_users)
        
        # 计算综合得分
        overall_score = (design_score * 0.3 + interaction_score * 0.35 + experience_score * 0.35)
        
        # 分析优势与劣势
        strengths, weaknesses = self._analyze_strengths_weaknesses(
            design_score, interaction_score, experience_score
        )
        
        # 生成改进建议
        recommendations = self._generate_recommendations(
            design_score, interaction_score, experience_score
        )
        
        return ProductEvaluation(
            product_name="Agent Product",
            evaluation_date=datetime.now(),
            design_score=round(design_score, 2),
            interaction_score=round(interaction_score, 2),
            experience_score=round(experience_score, 2),
            overall_score=round(overall_score, 2),
            strengths=strengths,
            weaknesses=weaknesses,
            recommendations=recommendations
        )
    
    def _evaluate_design_principles(self, features: List[str]) -> float:
        """评估设计原则"""
        score = 0.0
        max_score = len(DesignPrinciple) * 10
        
        # 简化评估：检查特征是否遵循设计原则
        principle_checks = {
            DesignPrinciple.HUMAN_CENTERED: any("user" in f.lower() for f in features),
            DesignPrinciple.TRANSPARENT: any("explain" in f.lower() or "transparent" in f.lower() for f in features),
            DesignPrinciple.CONTROLLABLE: any("control" in f.lower() or "override" in f.lower() for f in features),
            DesignPrinciple.PROGRESSIVE: any("progressive" in f.lower() or "layered" in f.lower() for f in features),
            DesignPrinciple.TRUSTWORTHY: any("trust" in f.lower() or "reliable" in f.lower() for f in features)
        }
        
        for principle, implemented in principle_checks.items():
            if implemented:
                score += 10
            else:
                score += 5  # 基础分
        
        return (score / max_score) * 10
    
    def _evaluate_interaction_patterns(self, use_cases: List[str]) -> float:
        """评估交互模式"""
        score = 0.0
        max_score = len(use_cases) * 10
        
        for use_case in use_cases:
            # 检查是否有合适的交互模式
            matched_patterns = 0
            for pattern in self.interaction_patterns:
                if any(uc in use_case.lower() for uc in pattern.use_cases):
                    matched_patterns += 1
            
            # 每个用例至少匹配一个模式得满分
            case_score = min(10, matched_patterns * 5)
            score += case_score
        
        return (score / max_score) * 10 if max_score > 0 else 5.0
    
    def _evaluate_experience(self, target_users: str) -> float:
        """评估用户体验"""
        # 基于当前指标计算体验得分
        total_score = 0.0
        max_score = len(self.experience_metrics) * 10
        
        for metric in self.experience_metrics.values():
            # 将当前值转换为目标值的比例
            ratio = metric.current_value / metric.target_value if metric.target_value > 0 else 0
            score = min(10, ratio * 10)
            total_score += score
        
        return (total_score / max_score) * 10
    
    def _analyze_strengths_weaknesses(self,
                                     design_score: float,
                                     interaction_score: float,
                                     experience_score: float) -> Tuple[List[str], List[str]]:
        """分析优势与劣势"""
        strengths = []
        weaknesses = []
        
        if design_score >= 8.0:
            strengths.append("设计原则遵循良好")
        elif design_score < 6.0:
            weaknesses.append("设计原则需要加强")
        
        if interaction_score >= 8.0:
            strengths.append("交互模式适配优秀")
        elif interaction_score < 6.0:
            weaknesses.append("交互模式需要优化")
        
        if experience_score >= 8.0:
            strengths.append("用户体验质量高")
        elif experience_score < 6.0:
            weaknesses.append("用户体验需要提升")
        
        # 具体分析
        if self.experience_metrics[ExperienceDimension.TRUST].current_value >= 8.0:
            strengths.append("用户信任度高")
        else:
            weaknesses.append("用户信任度需提升")
        
        if self.experience_metrics[ExperienceDimension.ERROR_RECOVERY].current_value >= 80:
            strengths.append("错误恢复机制完善")
        else:
            weaknesses.append("错误恢复机制需改进")
        
        return strengths, weaknesses
    
    def _generate_recommendations(self,
                                 design_score: float,
                                 interaction_score: float,
                                 experience_score: float) -> List[str]:
        """生成改进建议"""
        recommendations = []
        
        if design_score < 7.0:
            recommendations.append("加强设计原则培训，确保团队理解以人为中心的设计理念")
            recommendations.append("建立设计审查机制，定期检查产品是否符合透明、可控原则")
        
        if interaction_score < 7.0:
            recommendations.append("优化交互模式选择，根据用例匹配最合适的交互范式")
            recommendations.append("减少交互摩擦，提升对话自然度和流畅性")
        
        if experience_score < 7.0:
            recommendations.append("建立用户体验监控体系，持续追踪关键指标")
            recommendations.append("加强错误处理设计，提升用户容错体验")
        
        # 通用建议
        recommendations.append("定期进行用户调研，深入了解真实需求和痛点")
        recommendations.append("建立 A/B 测试机制，数据驱动设计优化")
        recommendations.append("加强跨团队协作，确保设计、开发、运营一致性")
        
        return recommendations[:5]  # 返回前 5 条建议
    
    def get_design_guidelines(self, 
                             principle: Optional[DesignPrinciple] = None) -> List[DesignGuideline]:
        """获取设计指南"""
        if principle is None:
            return sorted(self.design_guidelines, key=lambda x: x.priority, reverse=True)
        
        return [g for g in self.design_guidelines if g.principle == principle]
    
    def get_interaction_patterns(self,
                                paradigm: Optional[InteractionParadigm] = None) -> List[InteractionPattern]:
        """获取交互模式"""
        if paradigm is None:
            return self.interaction_patterns
        
        return [p for p in self.interaction_patterns if p.paradigm == paradigm]
    
    def get_experience_dashboard(self) -> Dict[str, Any]:
        """获取体验仪表盘"""
        dashboard = {
            "metrics": {},
            "overall_health": "",
            "priority_areas": []
        }
        
        total_score = 0.0
        for dim, metric in self.experience_metrics.items():
            ratio = metric.current_value / metric.target_value if metric.target_value > 0 else 0
            dashboard["metrics"][dim.value] = {
                "current": metric.current_value,
                "target": metric.target_value,
                "ratio": round(ratio, 2),
                "trend": metric.trend
            }
            total_score += ratio
        
        avg_ratio = total_score / len(self.experience_metrics)
        
        if avg_ratio >= 0.9:
            dashboard["overall_health"] = "优秀"
        elif avg_ratio >= 0.7:
            dashboard["overall_health"] = "良好"
        elif avg_ratio >= 0.5:
            dashboard["overall_health"] = "一般"
        else:
            dashboard["overall_health"] = "需改进"
        
        # 识别优先改进领域
        sorted_metrics = sorted(
            self.experience_metrics.items(),
            key=lambda x: x[1].current_value / x[1].target_value if x[1].target_value > 0 else 0
        )
        
        dashboard["priority_areas"] = [dim.value for dim, _ in sorted_metrics[:2]]
        
        return dashboard


# 使用示例
if __name__ == "__main__":
    print("=== Agent 产品设计原则与交互范式 ===\n")
    
    print("=== 创建产品设计师 ===")
    
    designer = AgentProductDesigner()
    
    print(f"\n=== 获取设计指南 ===")
    
    guidelines = designer.get_design_guidelines()
    
    print(f"设计指南总数：{len(guidelines)}")
    print("\n前 3 条高优先级指南:")
    for i, guideline in enumerate(guidelines[:3], 1):
        print(f"{i}. [{guideline.principle.value}] {guideline.guideline}")
        print(f"   描述：{guideline.description}")
        print(f"   示例：{', '.join(guideline.examples[:2])}")
        print(f"   反模式：{', '.join(guideline.anti_patterns[:2])}")
        print()
    
    print(f"\n=== 获取交互模式 ===")
    
    patterns = designer.get_interaction_patterns()
    
    print(f"交互模式总数：{len(patterns)}")
    print("\n所有交互模式:")
    for pattern in patterns:
        print(f"• [{pattern.paradigm.value}] {pattern.pattern_name}")
        print(f"  描述：{pattern.description}")
        print(f"  用例：{', '.join(pattern.use_cases[:2])}")
        print(f"  提示：{', '.join(pattern.implementation_tips[:2])}")
        print()
    
    print(f"\n=== 获取体验仪表盘 ===")
    
    dashboard = designer.get_experience_dashboard()
    
    print(f"整体健康度：{dashboard['overall_health']}")
    print(f"\n各维度指标:")
    for dim, data in dashboard['metrics'].items():
        print(f"  {dim}: {data['current']}/{data['target']} ({data['ratio']*100:.0f}%) - {data['trend']}")
    
    print(f"\n优先改进领域：{', '.join(dashboard['priority_areas'])}")
    
    print(f"\n=== 评估产品设计 ===")
    
    product_features = [
        "user-centered design",
        "explainable AI",
        "user control override",
        "progressive disclosure",
        "transparent decision making"
    ]
    
    target_users = "企业知识工作者"
    
    use_cases = [
        "客服问答自动化",
        "日程管理助手",
        "数据分析报告",
        "创意写作辅助"
    ]
    
    evaluation = designer.evaluate_design(
        product_features=product_features,
        target_users=target_users,
        use_cases=use_cases
    )
    
    print(f"产品名称：{evaluation.product_name}")
    print(f"评估日期：{evaluation.evaluation_date.strftime('%Y-%m-%d')}")
    print(f"\n各项得分:")
    print(f"  设计原则：{evaluation.design_score}/10")
    print(f"  交互模式：{evaluation.interaction_score}/10")
    print(f"  用户体验：{evaluation.experience_score}/10")
    print(f"  综合得分：{evaluation.overall_score}/10")
    
    print(f"\n优势:")
    for strength in evaluation.strengths:
        print(f"  ✓ {strength}")
    
    print(f"\n劣势:")
    for weakness in evaluation.weaknesses:
        print(f"  ✗ {weakness}")
    
    print(f"\n改进建议:")
    for i, rec in enumerate(evaluation.recommendations[:3], 1):
        print(f"  {i}. {rec}")
    
    print(f"\n关键观察:")
    print("1. 设计原则：以人为中心、透明可信、可控可解释、渐进式披露")
    print("2. 交互范式：对话式、主动式、多模态、协作式")
    print("3. 用户体验：信任建立、期望管理、错误恢复、情感连接")
    print("4. 持续优化：数据驱动、用户反馈、A/B 测试")
    print("\nAgent 产品设计的使命：从工具到伙伴，从被动到主动，从单向到协作")