从openclaw到国产龙虾，AI智能体工具技术架构全景分析

class EnterpriseTool(BaseTool):
    """企业级自定义工具开发模板"""
    
    def __init__(self, config: Dict[str, Any]):
        """
        初始化工具
        
        Args:
            config: 工具配置，包含API密钥、端点等
        """
        self.name = "enterprise_data_analyzer"
        self.version = "1.0.0"
        self.description = "企业数据分析和报表生成工具"
        self.parameters = {
            "data_source": {
                "type": "string", 
                "description": "数据源标识",
                "required": True
            },
            "analysis_type": {
                "type": "string",
                "enum": ["trend", "comparison", "forecast"],
                "description": "分析类型"
            },
            "time_range": {
                "type": "object",
                "properties": {
                    "start": {"type": "string", "format": "date"},
                    "end": {"type": "string", "format": "date"}
                }
            }
        }
        self.config = config
        self._init_client()
    
    async def execute(self, **kwargs) -> Dict[str, Any]:
        """
        执行工具逻辑
        
        Returns:
            包含执行结果和状态的字典
        """
        try:
            # 1. 参数验证
            self._validate_parameters(kwargs)
            
            # 2. 数据获取
            raw_data = await self._fetch_data(kwargs)
            
            # 3. 数据处理
            processed_data = self._process_data(raw_data)
            
            # 4. 分析执行
            analysis_result = await self._perform_analysis(
                processed_data, 
                kwargs["analysis_type"]
            )
            
            # 5. 结果格式化
            formatted_result = self._format_result(analysis_result)
            
            return {
                "status": "success",
                "data": formatted_result,
                "metadata": {
                    "processing_time": self._get_processing_time(),
                    "data_points": len(raw_data)
                }
            }
            
        except ValidationError as e:
            return {"status": "error", "message": f"参数验证失败: {str(e)}"}
        except DataFetchError as e:
            return {"status": "error", "message": f"数据获取失败: {str(e)}"}
        except AnalysisError as e:
            return {"status": "error", "message": f"分析执行失败: {str(e)}"}
        except Exception as e:
            logger.error(f"工具执行异常: {str(e)}", exc_info=True)
            return {"status": "error", "message": "内部服务器错误"}
    
    def _validate_parameters(self, params: Dict[str, Any]) -> None:
        """参数验证逻辑"""
        required_params = [p for p, spec in self.parameters.items() 
                         if spec.get("required", False)]
        
        for param in required_params:
            if param not in params:
                raise ValidationError(f"缺少必要参数: {param}")
    
    async def _fetch_data(self, params: Dict[str, Any]) -> List[Dict]:
        """从数据源获取数据"""
        # 实现具体的数据获取逻辑
        pass
    
    def _process_data(self, raw_data: List[Dict]) -> pd.DataFrame:
        """数据处理和清洗"""
        # 实现数据处理逻辑
        pass
    
    async def _perform_analysis(self, data: pd.DataFrame, 
                               analysis_type: str) -> Dict[str, Any]:
        """执行数据分析"""
        # 实现分析逻辑
        pass
    
    def _format_result(self, result: Dict[str, Any]) -> Dict[str, Any]:
        """结果格式化"""
        # 实现结果格式化逻辑
        pass

# 复杂任务分解模板
complex_task_prompt = """
请按以下步骤处理任务：

1. 任务理解阶段
   - 明确用户的核心需求
   - 识别任务的关键要素
   - 评估任务的复杂程度

2. 规划分解阶段
   - 将复杂任务分解为子任务
   - 确定子任务的执行顺序
   - 识别任务间的依赖关系

3. 执行监控阶段
   - 按顺序执行子任务
   - 监控每个子任务的执行状态
   - 处理执行过程中的异常

4. 结果整合阶段
   - 汇总各子任务的结果
   - 验证结果的完整性和准确性
   - 以用户友好的格式呈现最终结果

当前任务：{task_description}
请开始执行。
"""

few_shot_examples = [
    {
        "input": "分析上季度销售数据，找出表现最好的三个产品",
        "output": {
            "steps": [
                "1. 从销售系统获取上季度销售数据",
                "2. 按产品汇总销售额",
                "3. 按销售额降序排序",
                "4. 选取前三名产品",
                "5. 计算各产品的市场份额和增长率"
            ],
            "tools": ["sales_data_fetcher", "data_analyzer", "report_generator"],
            "expected_output": "包含前三名产品详细数据的表格和分析报告"
        }
    },
    # 更多示例...
]

import pytest
from unittest.mock import AsyncMock, MagicMock

class TestEnterpriseTool:
    """企业工具测试类"""
    
    @pytest.fixture
    def tool(self):
        """创建测试工具实例"""
        config = {
            "api_key": "test_key",
            "endpoint": "http://test.endpoint"
        }
        return EnterpriseTool(config)
    
    @pytest.mark.asyncio
    async def test_parameter_validation(self, tool):
        """测试参数验证"""
        # 测试缺少必要参数
        with pytest.raises(ValidationError):
            await tool.execute(analysis_type="trend")
        
        # 测试无效参数
        with pytest.raises(ValidationError):
            await tool.execute(
                data_source="sales",
                analysis_type="invalid_type"
            )
    
    @pytest.mark.asyncio
    async def test_data_fetch(self, tool, mocker):
        """测试数据获取"""
        # Mock数据获取函数
        mock_data = [{"product": "A", "sales": 100}]
        mocker.patch.object(
            tool, 
            '_fetch_data', 
            return_value=mock_data
        )
        
        result = await tool.execute(
            data_source="sales",
            analysis_type="trend",
            time_range={"start": "2024-01-01", "end": "2024-03-31"}
        )
        
        assert result["status"] == "success"
        assert "data" in result
    
    @pytest.mark.asyncio
    async def test_error_handling(self, tool, mocker):
        """测试错误处理"""
        # Mock异常情况
        mocker.patch.object(
            tool,
            '_fetch_data',
            side_effect=DataFetchError("数据源不可用")
        )
        
        result = await tool.execute(
            data_source="sales",
            analysis_type="trend"
        )
        
        assert result["status"] == "error"
        assert "数据获取失败" in result["message"]

class IntegrationTestSuite:
    """集成测试套件"""
    
    def test_end_to_end_workflow(self):
        """端到端工作流测试"""
        # 1. 初始化测试环境
        test_env = self.setup_test_environment()
        
        # 2. 执行完整工作流
        workflow_result = self.execute_workflow(test_env)
        
        # 3. 验证结果
        self.validate_results(workflow_result)
        
        # 4. 清理测试环境
        self.cleanup_test_environment(test_env)
    
    def test_concurrent_execution(self):
        """并发执行测试"""
        # 模拟多用户并发场景
        concurrent_users = 100
        results = self.run_concurrent_tasks(concurrent_users)
        
        # 验证系统稳定性
        assert all(r["status"] == "success" for r in results)
        assert self.check_system_metrics()  # 检查系统指标
    
    def test_failure_recovery(self):
        """故障恢复测试"""
        # 模拟各种故障场景
        failure_scenarios = [
            "network_timeout",
            "service_unavailable",
            "disk_full",
            "memory_exhausted"
        ]
        
        for scenario in failure_scenarios:
            self.simulate_failure(scenario)
            recovery_result = self.test_recovery()
            assert recovery_result["recovered"] == True

def analyze_requirements():
    requirements = {
        "technical_team": {
            "size": "small/medium/large",
            "skill_level": "beginner/intermediate/expert",
            "devops_capability": True/False
        },
        "business_scenario": {
            "sensitivity": "high/medium/low",
            "integration_needs": ["im", "crm", "erp"],
            "scale": "department/company/enterprise"
        },
        "security_requirements": {
            "compliance": ["等保2.0", "GDPR", "其他"],
            "data_sovereignty": True/False,
            "audit_trail": True/False
        },
        "budget_constraints": {
            "upfront_investment": "预算范围",
            "ongoing_cost": "承受能力",
            "roi_expectation": "回报预期"
        }
    }
    return calculate_weights(requirements)

def evaluate_solutions(requirements, solutions):
    scores = {}
    
    for solution in solutions:
        score = 0
        
        # 技术团队匹配度
        tech_match = calculate_tech_match(
            solution["technical_complexity"],
            requirements["technical_team"]
        )
        score += tech_match * 0.3
        
        # 业务场景适配
        business_match = calculate_business_match(
            solution["features"],
            requirements["business_scenario"]
        )
        score += business_match * 0.25
        
        # 安全合规性
        security_match = calculate_security_match(
            solution["security_features"],
            requirements["security_requirements"]
        )
        score += security_match * 0.25
        
        # 成本效益
        cost_effectiveness = calculate_cost_effectiveness(
            solution["pricing"],
            requirements["budget_constraints"]
        )
        score += cost_effectiveness * 0.2
        
        scores[solution["name"]] = score
    
    return sorted(scores.items(), key=lambda x: x[1], reverse=True)

def conduct_poc(selected_solution):
    poc_plan = {
        "duration": "2-4周",
        "team": ["业务代表", "技术负责人", "安全专家"],
        "scenarios": [
            {
                "name": "核心工作流验证",
                "success_criteria": ["完成时间<5分钟", "准确率>95%"]
            },
            {
                "name": "安全合规测试",
                "success_criteria": ["通过安全扫描", "符合合规要求"]
            },
            {
                "name": "性能压力测试",
                "success_criteria": ["支持100并发", "响应时间<2秒"]
            }
        ],
        "evaluation_metrics": [
            "功能完整性",
            "性能表现", 
            "安全合规",
            "用户体验",
            "集成难度"
        ]
    }
    return poc_plan

technical_risks = {
    "vendor_lockin": {
        "probability": "medium",
        "impact": "high",
        "mitigation": [
            "采用开放标准协议",
            "设计抽象层隔离供应商依赖",
            "定期评估替代方案"
        ]
    },
    "technical_debt": {
        "probability": "high", 
        "impact": "medium",
        "mitigation": [
            "建立代码质量门禁",
            "定期架构评审",
            "技术债追踪和管理"
        ]
    },
    "skill_gap": {
        "probability": "high",
        "impact": "high",
        "mitigation": [
            "制定培训计划",
            "建立知识库",
            "外部专家咨询"
        ]
    }
}

security_risks = {
    "data_leakage": {
        "probability": "low",
        "impact": "critical",
        "mitigation": [
            "实施最小权限原则",
            "数据加密传输和存储",
            "定期安全审计"
        ]
    },
    "unauthorized_access": {
        "probability": "medium",
        "impact": "high",
        "mitigation": [
            "多因素认证",
            "基于角色的访问控制",
            "操作审计日志"
        ]
    },
    "model_poisoning": {
        "probability": "low",
        "impact": "high",
        "mitigation": [
            "输入验证和过滤",
            "模型监控和异常检测",
            "定期模型更新"
        ]
    }
}

business_risks = {
    "low_adoption": {
        "probability": "medium",
        "impact": "medium",
        "mitigation": [
            "用户参与式设计",
            "渐进式推广策略",
            "持续用户反馈收集"
        ]
    },
    "unmet_expectations": {
        "probability": "high",
        "impact": "medium",
        "mitigation": [
            "明确成功标准",
            "定期进度沟通",
            "灵活调整实施计划"
        ]
    },
    "integration_failure": {
        "probability": "medium",
        "impact": "high",
        "mitigation": [
            "详细的集成测试",
            "分阶段集成策略",
            "回滚计划准备"
        ]
    }
}

technical_metrics = {
    "system_performance": {
        "response_time": "<2秒（P95）",
        "availability": ">99.9%",
        "error_rate": "<0.1%"
    },
    "development_efficiency": {
        "tool_development_time": "减少50%",
        "integration_complexity": "降低60%",
        "maintenance_effort": "减少40%"
    },
    "quality_metrics": {
        "test_coverage": ">80%",
        "defect_density": "<0.5/千行代码",
        "security_vulnerabilities": "零高危漏洞"
    }
}

business_metrics = {
    "productivity_improvement": {
        "task_completion_time": "减少30-50%",
        "automation_rate": "达到40-60%",
        "error_reduction": "减少70-80%"
    },
    "user_adoption": {
        "active_users": "月增长20%",
        "user_satisfaction": "NPS > 50",
        "retention_rate": ">80%"
    },
    "business_impact": {
        "cost_reduction": "降低20-30%",
        "revenue_impact": "间接贡献5-10%增长",
        "innovation_acceleration": "新产品上线时间减少40%"
    }
}