Google I/O 2026: Agentic Era - Multi-Agent System Architecture and Self-Evolution Technology

# Gemini 3.5 Flash Enterprise Application Example
# Source: Google Cloud Documentation & Antigravity 2.0 SDK

from google import genai
from google.genai import types

# Initialize client
client = genai.Client(
    api_key=os.environ["GEMINI_API_KEY"],
    http_options=types.HTTPOptions(api_version="v1alpha")
)

# Create high-speed agent task
def create_long_running_agent_task(prompt: str, max_steps: int = 100):
    """
    Create a long-duration agent task
    Suitable for: code generation, multi-step reasoning, cross-system automation
    """
    response = client.models.generate_content(
        model="gemini-3.5-flash",
        contents=prompt,
        config=types.GenerateContentConfig(
            # Enable agent mode
            automatic_function_calling=types.AutomaticFunctionCalling(
                maximum_calls=max_steps
            ),
            # Tool configuration
            tools=[
                types.Tool(code_execution=types.CodeExecutionConfig()),
                types.Tool(file_utils=types.FileUtilsConfig()),
                types.Tool(google_search=types.GoogleSearchConfig()),
            ],
            # Thinking budget (balance speed and quality)
            thinking_config=types.ThinkingConfig(
                thinking_budget=2048  # 3.5 Flash optimized thinking budget
            )
        )
    )
    return response

# Example: Batch code refactoring task
def batch_code_refactoring(repo_paths: list[str], target_style: str):
    """
    Batch code refactoring task
    Typical scenario: 93 agents processing 2.6 billion tokens in parallel
    """
    tasks = [
        create_long_running_agent_task(
            prompt=f"Please refactor all Python files in {repo_path}, "
                  f"applying Google style guide, target: {target_style}"
        )
        for repo_path in repo_paths
    ]
    
    # Execute in parallel
    results = client.models.generate_content_stream(
        model="gemini-3.5-flash",
        contents=tasks,  # Batch processing
        config=types.GenerateContentConfig(
            automatic_function_calling=types.AutomaticFunctionCalling(
                max_calls=50
            ),
            thinking_config=types.ThinkingConfig(thinking_budget=1024)
        )
    )
    return results

# Gemini Omni World Model API Example
# Source: Google DeepMind Gemini Omni Technical Documentation

from google.deepmind import omnimodel
import numpy as np

# Initialize world model
world_model = omnimodel.WorldModel.from_pretrained(
    model_name="gemini-omni-1.0",
    weights_path="gs://bucket/gemini-omni-weights"
)

class PhysicalSimulation:
    """Simulate physical environments using world models"""
    
    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]:
        """
        Predict object trajectory under multi-step force applications
        World model understands physics: kinetic energy, potential energy, gravity
        """
        state = initial_state.copy()
        trajectory = [state]
        
        for t in range(time_steps):
            # Build physics simulation prompt
            prompt = self._build_physics_prompt(state, forces[t])
            
            # Call world model to predict next state
            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:
        """
        Robot task planning
        World model understands physical constraints and action consequences
        """
        planning_prompt = f"""
        Robot configuration: {robot_description}
        Task objective: {task}
        Environment description: {environment}
        
        Please plan the robot's action sequence, ensuring:
        1. Understanding of physical constraints (gravity, friction, inertia)
        2. Prediction of consequences for each action
        3. Optimization of energy consumption
        """
        
        return self.model.generate_action_plan(
            prompt=planning_prompt,
            mode="physical_reasoning"
        )

# Example: Predict projectile motion
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 Core Implementation
# Source: Agent orchestration architecture based on Antigravity 2.0 SDK

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

class TaskComplexity(Enum):
    """Task complexity classification"""
    SIMPLE = 1          # Single-turn response
    MODERATE = 2        # 2-5 step multi-step reasoning
    COMPLEX = 3         # 5-20 step complex tasks
    SUBAGENT = 4        # Requires sub-agent coordination

@dataclass
class AgentCapability:
    """Agent capability description"""
    name: str
    description: str
    supported_complexity: list[TaskComplexity]
    tool_definitions: list[dict]
    estimated_cost_per_step: float

@dataclass 
class ExecutionPlan:
    """Execution plan"""
    plan_id: str
    complexity: TaskComplexity
    assigned_agents: list[str]
    tool_sequence: list[dict]
    estimated_steps: int
    fallback_strategy: str

class SupervisorAgent:
    """
    Supervisor Agent: Responsible for task planning and routing
    Core orchestration component of Agentic AI systems
    """
    
    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:
        """
        Core function: Analyze requests and plan execution routes
        """
        # Step 1: Complexity analysis
        complexity_prompt = f"""
        Analyze the complexity level of the following request:
        Request: {user_request}
        Context: {context}
        
        Evaluation criteria:
        - How many reasoning steps are needed?
        - Are external tool calls required?
        - Is multi-agent coordination needed?
        - Is long-term memory retrieval required?
        """
        
        complexity_response = await self.model.generate(
            prompt=complexity_prompt,
            output_schema=TaskComplexity
        )
        complexity = complexity_response.parsed
        
        # Step 2: Agent selection
        eligible_agents = [
            name for name, cap in self.agents.items()
            if complexity in cap.supported_complexity
        ]
        
        # Step 3: Tool planning
        tools_prompt = f"""
        Based on request "{user_request}", plan the required tool call sequence.
        Available tools: {self.tools}
        """
        
        tools_response = await self.model.generate(
            prompt=tools_prompt,
            output_schema=list[dict]
        )
        tool_sequence = tools_response.parsed
        
        # Step 4: Generate execution plan
        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:
        """
        Execute complex tasks using sub-agents
        """
        results = {}
        
        if plan.complexity >= TaskComplexity.SUBAGENT:
            # Launch multiple sub-agents in parallel
            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
                )
            
            # Parallel execution
            agent_tasks = [
                run_agent(name) 
                for name in plan.assigned_agents
            ]
            agent_results = await asyncio.gather(*agent_tasks)
            
            # Aggregate results
            results["subagent_outputs"] = dict(
                zip(plan.assigned_agents, agent_results)
            )
            results["final"] = await self._synthesize_outputs(
                results["subagent_outputs"]
            )
        else:
            # Single agent execution
            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 Implementation
# Source: Google Search Agents Technical Documentation

class SearchAgent:
    """
    24/7 Persistent Search Agent
    Core capabilities: Information monitoring, multi-source aggregation, proactive notification
    """
    
    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:
        """
        Create a persistent monitoring task
        Example: Monitor news updates for specific stocks
        """
        monitor_id = f"monitor_{uuid.uuid4().hex[:8]}"
        
        # Initialize monitoring configuration
        monitor_config = {
            "id": monitor_id,
            "topic": topic,
            "criteria": criteria,
            "channels": notify_channels,
            "frequency": "realtime",  # realtime/hourly/daily
            "last_check": None,
            "state": "active"
        }
        
        await self.memory.save("monitors", monitor_config)
        
        # Start background monitoring loop
        asyncio.create_task(self._monitoring_loop(monitor_id))
        
        return monitor_id
    
    async def _monitoring_loop(self, monitor_id: str):
        """Background monitoring loop"""
        while True:
            config = await self.memory.load("monitors", monitor_id)
            
            if config["state"] != "active":
                break
            
            # Execute search
            results = await self.search.query(
                query=config["topic"],
                filters=config["criteria"],
                since=config["last_check"]
            )
            
            # Check for new results
            new_results = self._filter_new_results(
                results,
                config["last_check"]
            )
            
            if new_results:
                # Send notification
                await self.notify.send(
                    channels=config["channels"],
                    message=self._format_results(new_results),
                    priority="high" if self._is_urgent(new_results) else "normal"
                )
                
                # Update memory
                config["last_check"] = datetime.now()
                await self.memory.save("monitors", config)
            
            # Wait for next check
            await asyncio.sleep(self._get_interval(config["frequency"]))
    
    async def execute_research_task(
        self,
        objective: str,
        depth: str = "standard"  # quick/standard/deep
    ) -> dict:
        """
        Execute deep research task
        Multi-step cross-source information aggregation
        """
        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":
                # Multi-source search
                context["search_results"] = await self._multi_source_search(objective)
                
            elif phase == "explore":
                # Deep exploration of related links
                context["deep_dives"] = await self._explore_sources(
                    context["search_results"]
                )
                
            elif phase == "verify":
                # Cross-validation of information
                context["verified"] = await self._verify_claims(
                    context["deep_dives"]
                )
                
            elif phase == "analyze":
                # Deep analysis
                context["analysis"] = await self._analyze_data(context["verified"])
                
            elif phase == "synthesize":
                # Comprehensive output
                context["final_report"] = await self._synthesize_report(context)
                
            elif phase == "summarize":
                # Quick summary
                context["summary"] = await self._quick_summary(
                    context["search_results"]
                )
        
        return context.get("final_report") or context.get("summary")

# Coding Agent - Antigravity 2.0 Implementation
# Source: Google Antigravity SDK Documentation

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

@dataclass
class CodeTask:
    """Code task description"""
    type: str  # generate/refactor/test/analyze/migrate
    language: str
    scope: str  # file/module/project
    requirements: dict
    constraints: dict

class AntigravityCodingAgent:
    """
    Google Antigravity 2.0 Coding Agent
    Core capabilities: Code generation, refactoring, testing, cross-file refactoring
    """
    
    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:
        """
        Generate complete project from specification
        This is the core capability showcase of Antigravity 2.0
        """
        # Phase 1: Project structure planning
        structure_plan = await self._plan_project_structure(spec)
        
        # Phase 2: Dependency analysis
        dependencies = await self._analyze_dependencies(structure_plan)
        
        # Phase 3: Code generation (multi-agent parallel)
        generated_files = await self._parallel_generate(structure_plan)
        
        # Phase 4: Integration testing
        test_results = await self._run_integration_tests(generated_files)
        
        # Phase 5: Fix and optimize
        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]:
        """
        Multi-agent parallel code generation
        Each sub-agent responsible for one module
        """
        # Create sub-agent pool
        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)
        
        # Parallel generation
        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:
        """
        Large-scale code refactoring
        Process codebases containing millions of tokens
        """
        # Get codebase overview
        codebase_summary = await self.context.get_summary(scope)
        
        # Analyze files needing refactoring
        files_to_refactor = await self._analyze_refactoring_targets(
            codebase_summary,
            target_style
        )
        
        # Batch processing (avoid token overflow)
        batch_size = 50  # 50 files per batch
        batches = self._chunk_files(files_to_refactor, batch_size)
        
        results = []
        for batch_num, batch in enumerate(batches):
            # Create specialized agent for each batch
            batch_agent = RefactoringBatchAgent(
                files=batch,
                style_guide=target_style,
                context=self.context
            )
            
            batch_result = await batch_agent.execute()
            results.append(batch_result)
            
            # Progress report
            progress = (batch_num + 1) / len(batches) * 100
            print(f"Progress: {progress:.1f}% ({len(batch)} files)")
        
        return self._aggregate_results(results)

# Self-Evolving Multi-Agent System Core Implementation
# Source: Technical architecture based on Fujitsu EVE-Agent (arXiv:2605.22905)

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

class ExperienceType(Enum):
    """Experience types"""
    SUCCESS = "success"
    FAILURE = "failure"  
    HUMAN_CORRECTION = "correction"
    POLICY_CHANGE = "policy_change"
    SPEC_UPDATE = "spec_update"

@dataclass
class Experience:
    """Execution experience record"""
    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:
    """Learned insight"""
    insight_id: str
    pattern: str
    trigger_conditions: dict
    recommended_action: dict
    confidence_score: float
    source_experiences: list[str]

class SelfEvolvingAgent:
    """
    Self-Evolving Agent Core Class
    Key technologies: Continuous learning, safety verification, incremental improvement
    """
    
    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
        
        # Learning state
        self.experience_buffer = []
        self.insights = []
        self.improvement_proposals = []
    
    async def execute_task_with_learning(
        self,
        task: dict,
        context: dict = None
    ) -> dict:
        """
        Execute task with continuous learning
        """
        # Step 1: Retrieve relevant experience
        relevant_insights = await self.knowledge.retrieve(
            query=task,
            top_k=5
        )
        
        # Step 2: Generate enhanced prompt
        enhanced_prompt = self._enhance_prompt_with_insights(
            base_prompt=task,
            insights=relevant_insights
        )
        
        # Step 3: Execute task
        result = await self.model.generate(enhanced_prompt)
        
        # Step 4: Verify result
        verification = await self.verify.check(
            input_task=task,
            output=result
        )
        
        # Step 5: Record experience
        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: Generate improvement suggestions (async)
        if not verification.valid:
            asyncio.create_task(
                self._generate_improvement_proposal(experience)
            )
        
        return result
    
    async def _generate_improvement_proposal(
        self,
        failed_experience: Experience
    ) -> LearnedInsight:
        """
        Generate improvement suggestions from failed experience
        Core innovation: Analyze failure causes and propose actionable improvements
        """
        # Build analysis prompt
        analysis_prompt = f"""
        Analyze the following failed experience and extract actionable improvement suggestions:
        
        Failed task: {failed_experience.input_data}
        Failed output: {failed_experience.output_data}
        Verification result: {failed_experience}
        
        Please analyze:
        1. What is the root cause of the failure?
        2. Which prompts/parameters need adjustment?
        3. What are the new trigger conditions?
        4. What specific actions are recommended?
        """
        
        analysis = await self.model.generate(analysis_prompt)
        
        # Extract insight
        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]
        )
        
        # Safety verification (do not apply directly)
        safety_check = await self.verify.safety_validation(insight)
        
        if safety_check.approved:
            # Only add to knowledge base after safety verification passes
            await self.knowledge.add_insight(insight)
            self.insights.append(insight)
        
        return insight
    
    async def process_human_feedback(
        self,
        experience_id: str,
        feedback: dict
    ) -> None:
        """
        Process human feedback and update knowledge
        """
        experience = await self.knowledge.get_experience(experience_id)
        experience.human_feedback = feedback
        experience.outcome = ExperienceType.HUMAN_CORRECTION
        
        # Learn from human correction
        insight = await self._learn_from_correction(experience, feedback)
        
        if insight:
            await self.knowledge.add_insight(insight)


class MultiAgentCollaboration:
    """
    Multi-Agent Collaboration Framework
    Core capability of Fujitsu Takane
    """
    
    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:
        """
        Coordinate multi-agents to execute complex tasks
        """
        # Step 1: Task decomposition
        subtasks = await self._decompose_task(task, agent_roles)
        
        # Step 2: Assign agents
        assignments = self._assign_agents(subtasks, agent_roles)
        
        # Step 3: Execute in parallel
        results = await asyncio.gather(*[
            self._execute_subtask(agent_id, subtask)
            for agent_id, subtask in assignments.items()
        ])
        
        # Step 4: Aggregate results
        aggregated = await self._aggregate_results(results)
        
        # Step 5: Cross-agent knowledge sharing
        await self._share_knowledge(results)
        
        return aggregated
    
    async def _share_knowledge(
        self,
        results: list[dict]
    ) -> None:
        """
        Cross-agent knowledge sharing mechanism
        One agent's experience benefits other agents
        """
        for result in results:
            if "learned_insights" in result:
                for insight in result["learned_insights"]:
                    # Broadcast to all agents
                    for agent in self.agents.values():
                        if insight.is_relevant_to(agent.specialization):
                            await agent.knowledge.add_insight(insight)

# Takane LLM Domain Adaptation Implementation
# Source: Fujitsu Kozuchi AI Platform

class TakaneDomainAdapter:
    """
    Takane Domain Adapter
    Core technology: Continuous learning from business execution results
    """
    
    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:
        """
        Auto-enhance domain-specific model
        Multi-agents collaborate on data selection, training condition adjustment, evaluation, improvement
        """
        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:
            # Data selection agent
            selected_data = await self._select_training_data(
                training_data,
                current_model=best_model
            )
            
            # Training condition adjustment agent
            training_config = await self._optimize_training_config(
                current_accuracy=best_accuracy,
                target=target_improvement
            )
            
            # Train model
            candidate_model = await self._train_model(
                data=selected_data,
                config=training_config
            )
            
            # Evaluation agent
            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:
                # Analyze failure
                failure_analysis = await self._analyze_failure(
                    candidate_model,
                    evaluation
                )
                await self._generate_improvement(failure_analysis)
            
            # Check if target reached
            if best_accuracy >= target_improvement:
                break
            
            iteration += 1
        
        return {
            "final_model": best_model,
            "final_accuracy": best_accuracy,
            "iterations": iteration,
            "improvement": best_accuracy - 0.0  # Relative to baseline
        }
    
    async def apply_to_medical_records(
        self,
        unstructured_text: str
    ) -> dict:
        """
        Medical record structured extraction example
        Extract diagnostic names, progression stages, treatment strategies from unstructured medical records
        """
        extraction_prompt = f"""
        Extract structured information from the following medical record:
        
        Raw text:
        {unstructured_text}
        
        Please extract:
        1. Diagnostic name (ICD-10 code)
        2. Disease progression stage (if applicable)
        3. Treatment strategy
        4. Medication regimen
        5. Follow-up plan
        """
        
        result = await self.base_model.generate(extraction_prompt)
        return self._parse_structured_output(result)


# Practical Application Example
async def demonstrate_takane_enhancement():
    """
    Demonstrate Takane auto-enhancement process
    """
    # Initialize
    base_model = load_base_model("gemini-pro")
    adapter = TakaneDomainAdapter(
        base_model=base_model,
        domain="healthcare"
    )
    
    # Load medical data
    medical_records = load_medical_dataset("hospital_records.jsonl")
    
    # Define evaluation criteria
    evaluation_criteria = {
        "diagnostic_accuracy": 0.95,
        "completeness": 0.90,
        "format_correctness": 0.98
    }
    
    # Execute auto-enhancement
    result = await adapter.auto_enhance(
        training_data=medical_records,
        evaluation_criteria=evaluation_criteria,
        target_improvement=0.28  # 28 percentage points improvement reported by Fujitsu
    )
    
    print(f"Final accuracy: {result['final_accuracy']:.2%}")
    print(f"Total iterations: {result['iterations']}")

# MCP (Model Context Protocol) Client Implementation
# Source: Anthropic MCP Documentation

from typing import Any
import json

class MCPClient:
    """
    Model Context Protocol client
    Implements universal interoperability between AI systems
    """
    
    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:
        """Connect to MCP server"""
        async with aiohttp.ClientSession() as session:
            # Get available tools list
            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:
        """
        Cross-AI system tool calling
        Standardized interface, different AIs can interoperate
        """
        # Normalize request
        mcp_request = {
            "jsonrpc": "2.0",
            "method": "tools/call",
            "params": {
                "name": tool_name,
                "arguments": arguments
            },
            "id": str(uuid.uuid4())
        }
        
        # Send request
        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:
        """
        Cross-AI system context sharing
        Foundation for multi-agent collaboration
        """
        self.context_cache[context_type] = {
            "data": data,
            "timestamp": datetime.now(),
            "source": "mcp_client"
        }
        
        # Broadcast context update
        await self._broadcast_context_update(context_type)


# Cross-platform tool registration example
async def register_cross_platform_tools():
    """Register tools shareable across multiple AI systems"""
    mcp_client = MCPClient(
        server_url="https://mcp.anthropic.com",
        auth_token=os.environ["MCP_TOKEN"]
    )
    await mcp_client.connect()
    
    # Register tool definitions (Agents.md format)
    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
# Source: Microsoft TechCommunity (GA: 2026-05-13)

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

class EnterpriseComputerAgent:
    """
    Enterprise-grade Computer Use agent
    Features: Purview logging, Key Vault credentials, Step-by-step billing
    """
    
    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:
        """
        Execute automated workflow
        Typical scenario: Complete business process across multiple systems
        """
        # Create session
        session = await self.agent.create_session(
            config=self.session_config
        )
        
        try:
            # Execute workflow steps
            results = []
            current_state = initial_data or {}
            
            for step in workflow_definition["steps"]:
                # Execute single step
                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
                })
                
                # Update state
                current_state.update(step_result.state_updates)
                
                # Approval check (configurable human review)
                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:
            # Close session (Purview has recorded complete logs)
            await session.close()
    
    async def fill_business_form(
        self,
        form_template: dict,
        data_source: str
    ) -> dict:
        """
        Fill business form example
        Extract information from data source and fill into target system
        """
        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)


# Billing example
def calculate_computer_use_cost(steps_count: int) -> float:
    """
    Calculate Computer Use cost
    Microsoft standard model: 5 Copilot Credits/step
    Approximately $0.04/step (prepaid)
    """
    credits_per_step = 5
    cents_per_credit = 0.008  # $0.008/credit (prepaid plan)
    
    total_cost_cents = steps_count * credits_per_step * cents_per_credit
    return total_cost_cents / 100  # Convert to USD

# Example: 4-step form filling task cost
example_cost = calculate_computer_use_cost(steps_count=4)
print(f"4-step form filling task cost: ${example_cost:.4f}")  # ~$0.0016

# Anthropic Claude Computer Use Implementation
# Source: Anthropic Project Glasswing

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

class ClaudeComputerAgent:
    """
    Claude Computer Use agent
    Supports browser, filesystem operations
    Secure enterprise deployment with MCP
    """
    
    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:
        """
        Claude agent task with tool execution
        """
        # Restrict available tools (security policy)
        allowed_tools = tool_permissions or list(self.available_tools.keys())
        tools = {
            name: self.available_tools[name]
            for name in allowed_tools
        }
        
        # Execute conversation
        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
            ]
        )
        
        # Process tool calls
        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
                
                # Execute tool
                result = await self._execute_tool(tool_name, tool_args)
                tool_results.append({
                    "type": "tool_result",
                    "tool_use_id": tool_use.id,
                    "content": result
                })
            
            # Continue conversation
            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
        }