Google I/O 2026：Agentic Era 时代的多智能体系统架构与自进化技术

# Gemini 3.5 Flash企业应用示例
# 来源：Google Cloud Documentation & Antiquity Antigravity 2.0 SDK

from google import genai
from google.genai import types

# 初始化客户端
client = genai.Client(
    api_key=os.environ["GEMINI_API_KEY"],
    http_options=types.HTTPOptions(api_version="v1alpha")
)

# 创建高速代理任务
def create_long_running_agent_task(prompt: str, max_steps: int = 100):
    """
    创建长时程代理任务
    适用于：代码生成、多步骤推理、跨系统自动化
    """
    response = client.models.generate_content(
        model="gemini-3.5-flash",
        contents=prompt,
        config=types.GenerateContentConfig(
            # 启用代理模式
            automatic_function_calling=types.AutomaticFunctionCalling(
                maximum_calls=max_steps
            ),
            # 工具配置
            tools=[
                types.Tool(code_execution=types.CodeExecutionConfig()),
                types.Tool(file_utils=types.FileUtilsConfig()),
                types.Tool(google_search=types.GoogleSearchConfig()),
            ],
            # 思考预算（平衡速度与质量）
            thinking_config=types.ThinkingConfig(
                thinking_budget=2048  # 3.5 Flash优化的思考预算
            )
        )
    )
    return response

# 示例：批量代码重构任务
def batch_code_refactoring(repo_paths: list[str], target_style: str):
    """
    批量代码重构任务
    93个代理并行处理26亿Token的典型场景
    """
    tasks = [
        create_long_running_agent_task(
            prompt=f"请重构 {repo_path} 中的所有Python文件，"
                  f"应用Google风格指南，目标：{target_style}"
        )
        for repo_path in repo_paths
    ]
    
    # 并行执行
    results = client.models.generate_content_stream(
        model="gemini-3.5-flash",
        contents=tasks,  # 批量处理
        config=types.GenerateContentConfig(
            automatic_function_calling=types.AutomaticFunctionCalling(
                max_calls=50
            ),
            thinking_config=types.ThinkingConfig(thinking_budget=1024)
        )
    )
    return results

# Gemini Omni 世界模型API示例
# 来源：Google DeepMind Gemini Omni Technical Documentation

from google.deepmind import omnimodel
import numpy as np

# 初始化世界模型
world_model = omnimodel.WorldModel.from_pretrained(
    model_name="gemini-omni-1.0",
    weights_path="gs://bucket/gemini-omni-weights"
)

class PhysicalSimulation:
    """使用世界模型进行物理环境模拟"""
    
    def __init__(self, model: omnimodel.WorldModel):
        self.model = model
        self.physics_rules = self._load_physics_knowledge()
    
    def predict_trajectory(
        self, 
        initial_state: dict,
        forces: list[dict],
        time_steps: int
    ) -> list[dict]:
        """
        预测物体在多步骤力作用下的轨迹
        世界模型理解动能、势能、重力等物理规律
        """
        state = initial_state.copy()
        trajectory = [state]
        
        for t in range(time_steps):
            # 构建物理模拟提示
            prompt = self._build_physics_prompt(state, forces[t])
            
            # 调用世界模型预测下一步状态
            prediction = self.model.predict_next_state(
                current_state=state,
                applied_forces=forces[t],
                physics_constraints=self.physics_rules
            )
            
            state = prediction.next_state
            trajectory.append(state)
        
        return trajectory
    
    def simulate_robot_planning(
        self,
        robot_description: dict,
        task: str,
        environment: str
    ) -> dict:
        """
        机器人任务规划
        世界模型理解物理约束和动作后果
        """
        planning_prompt = f"""
        机器人配置：{robot_description}
        任务目标：{task}
        环境描述：{environment}
        
        请规划机器人的动作序列，确保：
        1. 理解物理约束（重力、摩擦力、惯性）
        2. 预测每个动作的后果
        3. 优化能量消耗
        """
        
        return self.model.generate_action_plan(
            prompt=planning_prompt,
            mode="physical_reasoning"
        )

# 示例：预测抛物运动
simulation = PhysicalSimulation(world_model)
initial_state = {
    "position": {"x": 0, "y": 0, "z": 100},
    "velocity": {"x": 20, "y": 0, "z": 0},
    "mass": 1.0,
    "shape": "sphere"
}
gravity_force = {"type": "gravity", "magnitude": 9.8, "direction": (0, 0, -1)}

trajectory = simulation.predict_trajectory(
    initial_state=initial_state,
    forces=[gravity_force] * 100,
    time_steps=100
)

# Supervisor Agent核心实现
# 来源：基于Antigravity 2.0 SDK的代理编排架构

from dataclasses import dataclass, field
from enum import Enum
from typing import Callable, Awaitable
import asyncio

class TaskComplexity(Enum):
    """任务复杂度分级"""
    SIMPLE = 1          # 单轮响应
    MODERATE = 2        # 2-5步多步推理
    COMPLEX = 3         # 5-20步复杂任务
    SUBAGENT = 4        # 需要子代理协同

@dataclass
class AgentCapability:
    """代理能力描述"""
    name: str
    description: str
    supported_complexity: list[TaskComplexity]
    tool_definitions: list[dict]
    estimated_cost_per_step: float

@dataclass 
class ExecutionPlan:
    """执行计划"""
    plan_id: str
    complexity: TaskComplexity
    assigned_agents: list[str]
    tool_sequence: list[dict]
    estimated_steps: int
    fallback_strategy: str

class SupervisorAgent:
    """
    主管代理：负责任务规划与路由
    Agentic AI系统的核心编排组件
    """
    
    def __init__(
        self,
        model_client,
        agent_registry: dict[str, AgentCapability],
        tool_registry: list[dict]
    ):
        self.model = model_client
        self.agents = agent_registry
        self.tools = tool_registry
        self.execution_history = []
    
    async def plan_and_route(
        self, 
        user_request: str,
        context: dict = None
    ) -> ExecutionPlan:
        """
        核心功能：分析请求并规划执行路线
        """
        # Step 1: 复杂度分析
        complexity_prompt = f"""
        分析以下请求的复杂度等级：
        请求：{user_request}
        上下文：{context}
        
        评估标准：
        - 需要多少推理步骤？
        - 是否需要外部工具调用？
        - 是否需要多代理协同？
        - 是否需要长期记忆检索？
        """
        
        complexity_response = await self.model.generate(
            prompt=complexity_prompt,
            output_schema=TaskComplexity
        )
        complexity = complexity_response.parsed
        
        # Step 2: 代理选择
        eligible_agents = [
            name for name, cap in self.agents.items()
            if complexity in cap.supported_complexity
        ]
        
        # Step 3: 工具规划
        tools_prompt = f"""
        基于请求"{user_request}"，规划所需工具调用序列。
        可用工具：{self.tools}
        """
        
        tools_response = await self.model.generate(
            prompt=tools_prompt,
            output_schema=list[dict]
        )
        tool_sequence = tools_response.parsed
        
        # Step 4: 生成执行计划
        plan = ExecutionPlan(
            plan_id=self._generate_plan_id(),
            complexity=complexity,
            assigned_agents=eligible_agents,
            tool_sequence=tool_sequence,
            estimated_steps=len(tool_sequence) + 1,
            fallback_strategy=self._generate_fallback(complexity)
        )
        
        return plan
    
    async def execute_with_subagents(
        self,
        plan: ExecutionPlan,
        user_request: str
    ) -> dict:
        """
        使用子代理执行复杂任务
        """
        results = {}
        
        if plan.complexity >= TaskComplexity.SUBAGENT:
            # 并行启动多个子代理
            async def run_agent(agent_name: str):
                agent_cap = self.agents[agent_name]
                return await self._run_single_agent(
                    agent_name,
                    user_request,
                    plan.tool_sequence
                )
            
            # 并行执行
            agent_tasks = [
                run_agent(name) 
                for name in plan.assigned_agents
            ]
            agent_results = await asyncio.gather(*agent_tasks)
            
            # 聚合结果
            results["subagent_outputs"] = dict(
                zip(plan.assigned_agents, agent_results)
            )
            results["final"] = await self._synthesize_outputs(
                results["subagent_outputs"]
            )
        else:
            # 单代理执行
            primary_agent = plan.assigned_agents[0]
            results["final"] = await self._run_single_agent(
                primary_agent,
                user_request,
                plan.tool_sequence
            )
        
        return results

# Search Agent - Gemini Spark实现
# 来源：Google Search Agents Technical Documentation

class SearchAgent:
    """
    24/7持续搜索代理
    核心能力：信息监控、多源聚合、主动通知
    """
    
    def __init__(
        self,
        search_api_client,
        notification_service,
        memory_store
    ):
        self.search = search_api_client
        self.notify = notification_service
        self.memory = memory_store
    
    async def create_persistent_monitor(
        self,
        topic: str,
        criteria: dict,
        notify_channels: list[str]
    ) -> str:
        """
        创建持续监控任务
        示例：监控特定股票的新闻更新
        """
        monitor_id = f"monitor_{uuid.uuid4().hex[:8]}"
        
        # 初始化监控配置
        monitor_config = {
            "id": monitor_id,
            "topic": topic,
            "criteria": criteria,
            "channels": notify_channels,
            "frequency": "realtime",  # 实时/每小时/每日
            "last_check": None,
            "state": "active"
        }
        
        await self.memory.save("monitors", monitor_config)
        
        # 启动后台监控循环
        asyncio.create_task(self._monitoring_loop(monitor_id))
        
        return monitor_id
    
    async def _monitoring_loop(self, monitor_id: str):
        """后台监控循环"""
        while True:
            config = await self.memory.load("monitors", monitor_id)
            
            if config["state"] != "active":
                break
            
            # 执行搜索
            results = await self.search.query(
                query=config["topic"],
                filters=config["criteria"],
                since=config["last_check"]
            )
            
            # 检查新结果
            new_results = self._filter_new_results(
                results,
                config["last_check"]
            )
            
            if new_results:
                # 发送通知
                await self.notify.send(
                    channels=config["channels"],
                    message=self._format_results(new_results),
                    priority="high" if self._is_urgent(new_results) else "normal"
                )
                
                # 更新记忆
                config["last_check"] = datetime.now()
                await self.memory.save("monitors", config)
            
            # 等待下次检查
            await asyncio.sleep(self._get_interval(config["frequency"]))
    
    async def execute_research_task(
        self,
        objective: str,
        depth: str = "standard"  # quick/standard/deep
    ) -> dict:
        """
        执行深度研究任务
        多步骤跨源信息聚合
        """
        research_phases = {
            "quick": ["search", "summarize"],
            "standard": ["search", "explore", "synthesize"],
            "deep": ["search", "explore", "verify", "analyze", "synthesize"]
        }
        
        phases = research_phases[depth]
        context = {}
        
        for phase in phases:
            if phase == "search":
                # 多源搜索
                context["search_results"] = await self._multi_source_search(objective)
                
            elif phase == "explore":
                # 深度探索相关链接
                context["deep_dives"] = await self._explore_sources(
                    context["search_results"]
                )
                
            elif phase == "verify":
                # 交叉验证信息
                context["verified"] = await self._verify_claims(
                    context["deep_dives"]
                )
                
            elif phase == "analyze":
                # 深度分析
                context["analysis"] = await self._analyze_data(context["verified"])
                
            elif phase == "synthesize":
                # 综合输出
                context["final_report"] = await self._synthesize_report(context)
                
            elif phase == "summarize":
                # 快速摘要
                context["summary"] = await self._quick_summary(
                    context["search_results"]
                )
        
        return context.get("final_report") or context.get("summary")

# Coding Agent - Antigravity 2.0实现
# 来源：Google Antigravity SDK Documentation

from google.antigravity import Agent, ToolRegistry, ProjectContext
from dataclasses import dataclass

@dataclass
class CodeTask:
    """代码任务描述"""
    type: str  # generate/refactor/test/analyze/migrate
    language: str
    scope: str  # file/module/project
    requirements: dict
    constraints: dict

class AntigravityCodingAgent:
    """
    Google Antigravity 2.0编码代理
    核心能力：代码生成、重构、测试、跨文件重构
    """
    
    def __init__(
        self,
        model_client,
        tool_registry: ToolRegistry,
        project_context: ProjectContext
    ):
        self.model = model_client
        self.tools = tool_registry
        self.context = project_context
    
    async def generate_project_from_spec(
        self,
        spec: dict,
        output_dir: str
    ) -> dict:
        """
        从规格说明生成完整项目
        这是Antigravity 2.0的核心能力展示
        """
        # Phase 1: 项目结构规划
        structure_plan = await self._plan_project_structure(spec)
        
        # Phase 2: 依赖分析
        dependencies = await self._analyze_dependencies(structure_plan)
        
        # Phase 3: 代码生成（多代理并行）
        generated_files = await self._parallel_generate(structure_plan)
        
        # Phase 4: 集成测试
        test_results = await self._run_integration_tests(generated_files)
        
        # Phase 5: 修复与优化
        if test_results["failures"]:
            fixed_files = await self._fix_failures(
                test_results["failures"],
                generated_files
            )
            generated_files.update(fixed_files)
        
        return {
            "project_path": output_dir,
            "files": list(generated_files.keys()),
            "test_results": test_results,
            "stats": self._calculate_stats(generated_files)
        }
    
    async def _parallel_generate(
        self,
        structure_plan: dict
    ) -> dict[str, str]:
        """
        多代理并行代码生成
        每个子代理负责一个模块
        """
        # 创建子代理池
        agents = []
        for module_spec in structure_plan["modules"]:
            agent = AntigravitySubAgent(
                module_name=module_spec["name"],
                specifications=module_spec,
                context=self.context,
                tools=self.tools
            )
            agents.append(agent)
        
        # 并行生成
        generation_tasks = [
            agent.generate_code() 
            for agent in agents
        ]
        
        results = await asyncio.gather(*generation_tasks)
        
        return dict(zip(
            [a.module_name for a in agents],
            results
        ))
    
    async def refactor_large_scale(
        self,
        scope: str,  # "repo" for entire repository
        target_style: str,
        dry_run: bool = True
    ) -> dict:
        """
        大规模代码重构
        处理包含数百万Token的代码库
        """
        # 获取代码库概览
        codebase_summary = await self.context.get_summary(scope)
        
        # 分析需要重构的文件
        files_to_refactor = await self._analyze_refactoring_targets(
            codebase_summary,
            target_style
        )
        
        # 分批处理（避免Token溢出）
        batch_size = 50  # 每批50个文件
        batches = self._chunk_files(files_to_refactor, batch_size)
        
        results = []
        for batch_num, batch in enumerate(batches):
            # 为每个批次创建专门的代理
            batch_agent = RefactoringBatchAgent(
                files=batch,
                style_guide=target_style,
                context=self.context
            )
            
            batch_result = await batch_agent.execute()
            results.append(batch_result)
            
            # 进度报告
            progress = (batch_num + 1) / len(batches) * 100
            print(f"Progress: {progress:.1f}% ({len(batch)} files)")
        
        return self._aggregate_results(results)

# 自进化多智能体系统核心实现
# 来源：基于Fujitsu EVE-Agent (arXiv:2605.22905)的技术架构

from dataclasses import dataclass
from typing import Optional
from enum import Enum
import numpy as np

class ExperienceType(Enum):
    """经验类型"""
    SUCCESS = "success"
    FAILURE = "failure"  
    HUMAN_CORRECTION = "correction"
    POLICY_CHANGE = "policy_change"
    SPEC_UPDATE = "spec_update"

@dataclass
class Experience:
    """执行经验记录"""
    experience_id: str
    task_type: str
    outcome: ExperienceType
    input_data: dict
    output_data: dict
    human_feedback: Optional[dict]
    timestamp: datetime
    verified: bool = False

@dataclass
class LearnedInsight:
    """学习到的洞察"""
    insight_id: str
    pattern: str
    trigger_conditions: dict
    recommended_action: dict
    confidence_score: float
    source_experiences: list[str]

class SelfEvolvingAgent:
    """
    自进化智能体核心类
    关键技术：持续学习、安全验证、增量改进
    """
    
    def __init__(
        self,
        base_model,
        knowledge_base,
        verification_system,
        config: dict
    ):
        self.model = base_model
        self.knowledge = knowledge_base
        self.verify = verification_system
        self.config = config
        
        # 学习状态
        self.experience_buffer = []
        self.insights = []
        self.improvement_proposals = []
    
    async def execute_task_with_learning(
        self,
        task: dict,
        context: dict = None
    ) -> dict:
        """
        执行任务并持续学习
        """
        # Step 1: 检索相关经验
        relevant_insights = await self.knowledge.retrieve(
            query=task,
            top_k=5
        )
        
        # Step 2: 生成改进后的提示
        enhanced_prompt = self._enhance_prompt_with_insights(
            base_prompt=task,
            insights=relevant_insights
        )
        
        # Step 3: 执行任务
        result = await self.model.generate(enhanced_prompt)
        
        # Step 4: 验证结果
        verification = await self.verify.check(
            input_task=task,
            output=result
        )
        
        # Step 5: 记录经验
        experience = Experience(
            experience_id=str(uuid.uuid4()),
            task_type=self._classify_task(task),
            outcome=ExperienceType.SUCCESS if verification.valid 
                    else ExperienceType.FAILURE,
            input_data=task,
            output_data=result,
            human_feedback=None,
            timestamp=datetime.now()
        )
        
        await self._record_experience(experience)
        
        # Step 6: 生成改进建议（异步）
        if not verification.valid:
            asyncio.create_task(
                self._generate_improvement_proposal(experience)
            )
        
        return result
    
    async def _generate_improvement_proposal(
        self,
        failed_experience: Experience
    ) -> LearnedInsight:
        """
        从失败经验中生成改进建议
        核心创新：分析失败原因并提出可操作的改进
        """
        # 构建分析提示
        analysis_prompt = f"""
        分析以下失败经验，提取可操作的改进建议：
        
        失败任务：{failed_experience.input_data}
        失败输出：{failed_experience.output_data}
        验证结果：{failed_experience}
        
        请分析：
        1. 失败的根本原因是什么？
        2. 哪些提示词/参数需要调整？
        3. 新的触发条件是什么？
        4. 推荐的具体行动是什么？
        """
        
        analysis = await self.model.generate(analysis_prompt)
        
        # 提取洞察
        insight = LearnedInsight(
            insight_id=str(uuid.uuid4()),
            pattern=analysis["pattern"],
            trigger_conditions=analysis["conditions"],
            recommended_action=analysis["action"],
            confidence_score=analysis["confidence"],
            source_experiences=[failed_experience.experience_id]
        )
        
        # 安全验证（不直接应用）
        safety_check = await self.verify.safety_validation(insight)
        
        if safety_check.approved:
            # 仅在安全验证通过后才添加到知识库
            await self.knowledge.add_insight(insight)
            self.insights.append(insight)
        
        return insight
    
    async def process_human_feedback(
        self,
        experience_id: str,
        feedback: dict
    ) -> None:
        """
        处理人类反馈并更新知识
        """
        experience = await self.knowledge.get_experience(experience_id)
        experience.human_feedback = feedback
        experience.outcome = ExperienceType.HUMAN_CORRECTION
        
        # 从人类纠正中学习
        insight = await self._learn_from_correction(experience, feedback)
        
        if insight:
            await self.knowledge.add_insight(insight)


class MultiAgentCollaboration:
    """
    多智能体协作框架
    Fujitsu Takane的核心能力
    """
    
    def __init__(self, agents: list[SelfEvolvingAgent]):
        self.agents = {agent.id: agent for agent in agents}
        self.shared_knowledge = SharedKnowledgeStore()
        self.coordination = AgentCoordinator()
    
    async def execute_complex_task(
        self,
        task: dict,
        agent_roles: dict
    ) -> dict:
        """
        协调多智能体执行复杂任务
        """
        # Step 1: 任务分解
        subtasks = await self._decompose_task(task, agent_roles)
        
        # Step 2: 分配智能体
        assignments = self._assign_agents(subtasks, agent_roles)
        
        # Step 3: 并行执行
        results = await asyncio.gather(*[
            self._execute_subtask(agent_id, subtask)
            for agent_id, subtask in assignments.items()
        ])
        
        # Step 4: 结果聚合
        aggregated = await self._aggregate_results(results)
        
        # Step 5: 跨智能体知识共享
        await self._share_knowledge(results)
        
        return aggregated
    
    async def _share_knowledge(
        self,
        results: list[dict]
    ) -> None:
        """
        跨智能体知识共享机制
        一个智能体的经验可以惠及其他智能体
        """
        for result in results:
            if "learned_insights" in result:
                for insight in result["learned_insights"]:
                    # 广播到所有智能体
                    for agent in self.agents.values():
                        if insight.is_relevant_to(agent.specialization):
                            await agent.knowledge.add_insight(insight)

# Takane LLM领域适配实现
# 来源：Fujitsu Kozuchi AI Platform

class TakaneDomainAdapter:
    """
    Takane领域适配器
    核心技术：从业务执行结果中持续学习
    """
    
    def __init__(self, base_model, domain: str):
        self.base_model = base_model
        self.domain = domain
        self.performance_history = []
        self.optimization_cycles = 0
    
    async def auto_enhance(
        self,
        training_data: list[dict],
        evaluation_criteria: dict,
        target_improvement: float = 0.1
    ) -> dict:
        """
        自动增强领域特定模型
        多智能体协同执行数据选择、训练条件调整、评估、改进
        """
        print(f"Starting auto-enhancement for {self.domain}")
        
        best_model = self.base_model
        best_accuracy = 0.0
        iteration = 0
        max_iterations = 20
        
        while iteration < max_iterations:
            # 数据选择智能体
            selected_data = await self._select_training_data(
                training_data,
                current_model=best_model
            )
            
            # 训练条件调整智能体
            training_config = await self._optimize_training_config(
                current_accuracy=best_accuracy,
                target=target_improvement
            )
            
            # 训练模型
            candidate_model = await self._train_model(
                data=selected_data,
                config=training_config
            )
            
            # 评估智能体
            evaluation = await self._evaluate_model(
                candidate_model,
                criteria=evaluation_criteria
            )
            
            if evaluation["accuracy"] > best_accuracy:
                best_model = candidate_model
                best_accuracy = evaluation["accuracy"]
                print(f"Iteration {iteration}: New best accuracy = {best_accuracy:.2%}")
            else:
                # 分析失败原因
                failure_analysis = await self._analyze_failure(
                    candidate_model,
                    evaluation
                )
                await self._generate_improvement(failure_analysis)
            
            # 检查是否达到目标
            if best_accuracy >= target_improvement:
                break
            
            iteration += 1
        
        return {
            "final_model": best_model,
            "final_accuracy": best_accuracy,
            "iterations": iteration,
            "improvement": best_accuracy - 0.0  # 相对于基线
        }
    
    async def apply_to_medical_records(
        self,
        unstructured_text: str
    ) -> dict:
        """
        医疗记录结构化提取示例
        从非结构化病历中提取诊断名称、进展阶段、治疗策略
        """
        extraction_prompt = f"""
        从以下医疗记录中提取结构化信息：
        
        原始文本：
        {unstructured_text}
        
        请提取：
        1. 诊断名称（ICD-10编码）
        2. 疾病进展阶段（如适用）
        3. 治疗策略
        4. 用药方案
        5. 随访计划
        """
        
        result = await self.base_model.generate(extraction_prompt)
        return self._parse_structured_output(result)


# 实际应用示例
async def demonstrate_takane_enhancement():
    """
    演示Takane自动增强流程
    """
    # 初始化
    base_model = load_base_model("gemini-pro")
    adapter = TakaneDomainAdapter(
        base_model=base_model,
        domain="healthcare"
    )
    
    # 加载医疗数据
    medical_records = load_medical_dataset("hospital_records.jsonl")
    
    # 定义评估标准
    evaluation_criteria = {
        "diagnostic_accuracy": 0.95,
        "completeness": 0.90,
        "format_correctness": 0.98
    }
    
    # 执行自动增强
    result = await adapter.auto_enhance(
        training_data=medical_records,
        evaluation_criteria=evaluation_criteria,
        target_improvement=0.28  # Fujitsu报告的28个百分点提升
    )
    
    print(f"Final accuracy: {result['final_accuracy']:.2%}")
    print(f"Total iterations: {result['iterations']}")

# MCP (Model Context Protocol) 客户端实现
# 来源：Anthropic MCP Documentation

from typing import Any
import json

class MCPClient:
    """
    Model Context Protocol客户端
    实现AI系统间的通用互操作
    """
    
    def __init__(self, server_url: str, auth_token: str):
        self.server_url = server_url
        self.auth_token = auth_token
        self.tools = []
        self.context_cache = {}
    
    async def connect(self) -> None:
        """连接到MCP服务器"""
        async with aiohttp.ClientSession() as session:
            # 获取可用工具列表
            response = await session.get(
                f"{self.server_url}/tools",
                headers={"Authorization": f"Bearer {self.auth_token}"}
            )
            self.tools = await response.json()
    
    async def call_tool(
        self,
        tool_name: str,
        arguments: dict
    ) -> Any:
        """
        跨AI系统调用工具
        标准化接口，不同AI可互操作
        """
        # 规范化请求
        mcp_request = {
            "jsonrpc": "2.0",
            "method": "tools/call",
            "params": {
                "name": tool_name,
                "arguments": arguments
            },
            "id": str(uuid.uuid4())
        }
        
        # 发送请求
        async with aiohttp.ClientSession() as session:
            response = await session.post(
                f"{self.server_url}/rpc",
                json=mcp_request,
                headers={"Authorization": f"Bearer {self.auth_token}"}
            )
            result = await response.json()
        
        return result["result"]
    
    async def share_context(
        self,
        context_type: str,
        data: Any
    ) -> None:
        """
        跨AI系统共享上下文
        实现多代理协作的基础
        """
        self.context_cache[context_type] = {
            "data": data,
            "timestamp": datetime.now(),
            "source": "mcp_client"
        }
        
        # 广播上下文更新
        await self._broadcast_context_update(context_type)


# 跨平台工具注册示例
async def register_cross_platform_tools():
    """注册可在多个AI系统间共享的工具"""
    mcp_client = MCPClient(
        server_url="https://mcp.anthropic.com",
        auth_token=os.environ["MCP_TOKEN"]
    )
    await mcp_client.connect()
    
    # 注册工具定义（Agents.md格式）
    tools = [
        {
            "name": "search_web",
            "description": "Search the web for information",
            "input_schema": {
                "type": "object",
                "properties": {
                    "query": {"type": "string"},
                    "max_results": {"type": "integer"}
                }
            }
        },
        {
            "name": "execute_code",
            "description": "Execute Python code in sandbox",
            "input_schema": {
                "type": "object", 
                "properties": {
                    "code": {"type": "string"},
                    "language": {"type": "string"}
                }
            }
        },
        {
            "name": "read_file",
            "description": "Read a file from filesystem",
            "input_schema": {
                "type": "object",
                "properties": {
                    "path": {"type": "string"},
                    "encoding": {"type": "string"}
                }
            }
        }
    ]
    
    for tool in tools:
        await mcp_client.register_tool(tool)

# Microsoft Copilot Studio Computer Use SDK
# 来源：Microsoft TechCommunity (GA: 2026-05-13)

from microsoft.copilot import ComputerUseAgent
from azure.identity import DefaultAzureCredential
from microsoft.purview import PurviewClient

class EnterpriseComputerAgent:
    """
    企业级Computer Use代理
    特性：Purview日志、Key Vault凭证、Step-by-step计费
    """
    
    def __init__(
        self,
        credentials: DefaultAzureCredential,
        purview_client: PurviewClient
    ):
        self.agent = ComputerUseAgent(
            credential=credentials,
            billing_model="per_step"
        )
        self.purview = purview_client
        self.session_config = {
            "log_to_purview": True,
            "credential_source": "key_vault"
        }
    
    async def execute_automated_workflow(
        self,
        workflow_definition: dict,
        initial_data: dict = None
    ) -> dict:
        """
        执行自动化工作流
        典型场景：跨多个系统完成业务流程
        """
        # 创建会话
        session = await self.agent.create_session(
            config=self.session_config
        )
        
        try:
            # 执行工作流步骤
            results = []
            current_state = initial_data or {}
            
            for step in workflow_definition["steps"]:
                # 执行单个步骤
                step_result = await session.execute_step(
                    action=step["action"],
                    target=step["target"],
                    parameters=step.get("params", {}),
                    context=current_state
                )
                
                results.append({
                    "step": step["name"],
                    "status": step_result.status,
                    "output": step_result.output
                })
                
                # 更新状态
                current_state.update(step_result.state_updates)
                
                # 中断检查（可配置人工审核）
                if step.get("require_human_approval"):
                    approval = await self._wait_for_approval(
                        session.id,
                        step_result
                    )
                    if not approval.approved:
                        return {"status": "rejected", "step": step["name"]}
            
            return {
                "status": "completed",
                "results": results,
                "final_state": current_state
            }
            
        finally:
            # 关闭会话（Purview已记录完整日志）
            await session.close()
    
    async def fill_business_form(
        self,
        form_template: dict,
        data_source: str
    ) -> dict:
        """
        填写业务表单示例
        从数据源提取信息并填入目标系统
        """
        workflow = {
            "name": "form_filling_workflow",
            "steps": [
                {
                    "name": "extract_data",
                    "action": "read_document",
                    "target": data_source
                },
                {
                    "name": "parse_information",
                    "action": "extract_fields",
                    "target": "document"
                },
                {
                    "name": "navigate_to_form",
                    "action": "open_url",
                    "target": form_template["url"]
                },
                {
                    "name": "fill_fields",
                    "action": "fill_form",
                    "target": "form",
                    "params": {"fields": form_template["fields"]}
                },
                {
                    "name": "submit",
                    "action": "click_button",
                    "target": "submit_button"
                }
            ]
        }
        
        return await self.execute_automated_workflow(workflow)


# 计费示例
def calculate_computer_use_cost(steps_count: int) -> float:
    """
    计算Computer Use成本
    Microsoft标准模型：5 Copilot Credits/步
    约$0.04/步（预付费）
    """
    credits_per_step = 5
    cents_per_credit = 0.008  # $0.008/credit (预付费套餐)
    
    total_cost_cents = steps_count * credits_per_step * cents_per_credit
    return total_cost_cents / 100  # 转换为美元

# 示例：4步填表任务成本
example_cost = calculate_computer_use_cost(steps_count=4)
print(f"4步填表任务成本: ${example_cost:.4f}")  # ~$0.0016

# Anthropic Claude Computer Use实现
# 来源：Anthropic Project Glasswing

from anthropic import Anthropic
from tools import bash, read_file, write_file, browser

class ClaudeComputerAgent:
    """
    Claude Computer Use代理
    支持浏览器、文件系统操作
    配合MCP实现安全的企业部署
    """
    
    def __init__(self, api_key: str):
        self.client = Anthropic(api_key=api_key)
        self.available_tools = {
            "bash": bash,
            "read_file": read_file,
            "write_file": write_file,
            "browser": browser
        }
    
    async def execute_with_tools(
        self,
        task: str,
        tool_permissions: list[str] = None
    ) -> dict:
        """
        带工具执行的Claude代理任务
        """
        # 限制可用工具（安全策略）
        allowed_tools = tool_permissions or list(self.available_tools.keys())
        tools = {
            name: self.available_tools[name]
            for name in allowed_tools
        }
        
        # 执行对话
        message = await self.client.messages.create(
            model="claude-sonnet-4.6",
            max_tokens=4096,
            messages=[
                {
                    "role": "user",
                    "content": task
                }
            ],
            tools=[
                self._format_tool_def(name) 
                for name in allowed_tools
            ]
        )
        
        # 处理工具调用
        while message.stop_reason == "tool_use":
            tool_results = []
            
            for tool_use in message.tool_calls:
                tool_name = tool_use.name
                tool_args = tool_use.input
                
                # 执行工具
                result = await self._execute_tool(tool_name, tool_args)
                tool_results.append({
                    "type": "tool_result",
                    "tool_use_id": tool_use.id,
                    "content": result
                })
            
            # 继续对话
            message = await self.client.messages.create(
                model="claude-sonnet-4.6",
                max_tokens=4096,
                messages=[
                    {"role": "user", "content": task},
                    message.to_dict(),
                    {"role": "user", "content": tool_results}
                ],
                tools=[self._format_tool_def(name) for name in allowed_tools]
            )
        
        return {
            "response": message.content,
            "tool_calls_count": message.usage.total_tokens
        }