Google Gemini 3.5 Autonomous Agent Framework: I/O 2026 Leads a New Wave of Enterprise Automation

from google.generativeai import GenerativeModel
from google.generativeai import types

# Initialize Gemini 3.5
model = GenerativeModel(
    model_name="gemini-3.5-pro",
    tools=[
        # Available tool definitions
        types.Tool(
            function_declarations=[
                {
                    "name": "search_database",
                    "description": "Search the company database for relevant records",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "query": {"type": "string"},
                            "limit": {"type": "integer"}
                        }
                    }
                },
                {
                    "name": "send_email",
                    "description": "Send an email to specified recipients",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "to": {"type": "string"},
                            "subject": {"type": "string"},
                            "body": {"type": "string"}
                        }
                    }
                }
            ]
        )
    ],
    system_instruction="""
    You are a professional AI assistant representing a Fortune 500 company.
    Your role is to help users complete complex tasks autonomously.
    
    Guidelines:
    - Always verify sensitive operations before execution
    - Provide clear status updates during task execution
    - Ask for clarification when instructions are ambiguous
    - Maintain professional communication tone
    """
)

# Start autonomous Agent session
async def run_autonomous_agent():
    chat = model.start_chat(enable_autonomous_execution=True)
    
    # User only needs to describe the goal; Agent autonomously plans execution path
    response = await chat.send_message_async(
        "Analyze this month's sales data, identify the three fastest-growing product lines,"
        "then send an email to the sales team summarizing these findings."
    )
    
    # Agent will automatically:
    # 1. Call search_database to query sales data
    # 2. Analyze data to find the three fastest-growing product lines
    # 3. Generate email content
    # 4. Call send_email to send the email
    
    print(response.text)

from google.generativeai.reasoning import (
    ReasoningEngine,
    IntentParser,
    TaskDecomposer,
    ExecutionPlanner
)

class AdvancedReasoningEngine(ReasoningEngine):
    """Advanced reasoning engine"""
    
    def __init__(self, model):
        self.model = model
        self.intent_parser = IntentParser(model)
        self.task_decomposer = TaskDecomposer(model)
        self.planner = ExecutionPlanner(model)
    
    async def process_request(self, user_request: str, context: dict) -> dict:
        """Process user request"""
        
        # Step 1: Intent understanding
        intent = await self.intent_parser.parse(
            request=user_request,
            context=context,
            include_constraints=True,
            include_preferences=True
        )
        
        # Step 2: Task decomposition
        tasks = await self.task_decomposer.decompose(
            intent=intent,
            max_depth=5,  # Maximum 5 levels of subtasks
            parallel_threshold=3  # Consider parallelization for >3 subtasks
        )
        
        # Step 3: Execution planning
        execution_plan = await self.planner.create_plan(
            tasks=tasks,
            constraints={
                "max_execution_time": 3600,  # Maximum 1 hour
                "required_approvals": ["financial_data"],  # Operations requiring approval
                "retry_policy": "exponential_backoff"
            }
        )
        
        return {
            "intent": intent,
            "tasks": tasks,
            "execution_plan": execution_plan
        }
    
    async def execute_plan(
        self, 
        plan: ExecutionPlan, 
        progress_callback=None
    ):
        """Execute plan"""
        
        results = {}
        completed_tasks = set()
        
        while not plan.is_complete():
            # Get executable tasks (dependencies satisfied)
            executable_tasks = plan.get_executable_tasks(completed_tasks)
            
            if not executable_tasks:
                # May have circular dependencies or other issues
                raise ExecutionError("No executable tasks available")
            
            # Execute parallelizable tasks in batches
            batch = self._group_parallel_tasks(executable_tasks)
            
            for task_group in batch:
                if len(task_group) == 1:
                    # Execute serially
                    result = await self._execute_single_task(
                        task_group[0], 
                        results
                    )
                else:
                    # Execute in parallel
                    results_list = await asyncio.gather(*[
                        self._execute_single_task(task, results)
                        for task in task_group
                    ])
                    result = self._merge_results(results_list)
                
                results[task_group[0].id] = result
                completed_tasks.add(task_group[0].id)
                
                # Progress callback
                if progress_callback:
                    await progress_callback(
                        completed=len(completed_tasks),
                        total=len(plan.tasks),
                        current=task_group[0].name
                    )
        
        return self._aggregate_results(results, plan)

from google.generativeai.generative_ui import (
    Component,
    DataTable,
    Chart,
    Form,
    ActionButton
)

class DynamicUIEngine:
    """Dynamic UI generation engine"""
    
    def __init__(self, model):
        self.model = model
    
    async def generate_ui_for_task(
        self, 
        task: Task, 
        results: dict
    ) -> list[Component]:
        """Generate UI components for task results"""
        
        components = []
        
        # Generate different UI components based on task type
        if task.type == "data_analysis":
            # Generate data analysis table
            data_table = DataTable(
                data=results["analysis_data"],
                columns=["Product", "Monthly Sales", "MoM Growth", "YoY Growth"],
                sortable=True,
                filterable=True,
                style="professional"
            )
            components.append(data_table)
            
            # Generate trend chart
            chart = Chart(
                data=results["trend_data"],
                type="line",
                title="Monthly Sales Trends",
                show_legend=True,
                interactive=True
            )
            components.append(chart)
            
        elif task.type == "email_compose":
            # Generate email editing form
            form = Form(
                fields=[
                    {"name": "to", "label": "Recipient", "type": "email"},
                    {"name": "cc", "label": "CC", "type": "email_array"},
                    {"name": "subject", "label": "Subject", "type": "text"},
                    {"name": "body", "label": "Content", "type": "richtext"}
                ],
                initial_values={
                    "to": results.get("suggested_recipients", []),
                    "subject": results.get("generated_subject", ""),
                    "body": results.get("generated_body", "")
                }
            )
            components.append(form)
            
            # Generate action buttons
            action_buttons = [
                ActionButton(
                    label="Send Email",
                    action="send",
                    style="primary"
                ),
                ActionButton(
                    label="Edit Content",
                    action="edit",
                    style="secondary"
                ),
                ActionButton(
                    label="Cancel",
                    action="cancel",
                    style="ghost"
                )
            ]
            components.extend(action_buttons)
        
        return components
    
    def render_components(self, components: list[Component]) -> str:
        """Render UI components to HTML"""
        html_parts = ['<div class="generative-ui-container">']
        
        for component in components:
            if isinstance(component, DataTable):
                html_parts.append(self._render_table(component))
            elif isinstance(component, Chart):
                html_parts.append(self._render_chart(component))
            elif isinstance(component, Form):
                html_parts.append(self._render_form(component))
            elif isinstance(component, ActionButton):
                html_parts.append(self._render_button(component))
        
        html_parts.append('</div>')
        return '\n'.join(html_parts)

from google.antigravity import (
    Workflow,
    Task,
    Parallel,
    Sequential,
    Conditional,
    Loop
)

# Define a sales report generation workflow
sales_report_workflow = Workflow(
    name="Monthly Sales Report",
    description="Automatically generate monthly sales report",
    
    tasks=[
        # Task 1: Fetch data (parallel execution)
        Parallel(
            name="fetch_data",
            tasks=[
                Task(
                    name="sales_data",
                    agent="data_agent",
                    action="query_sales",
                    params={"period": "month"}
                ),
                Task(
                    name="inventory_data",
                    agent="data_agent",
                    action="query_inventory",
                    params={"period": "month"}
                ),
                Task(
                    name="customer_data",
                    agent="data_agent",
                    action="query_customers",
                    params={"period": "month"}
                )
            ]
        ),
        
        # Task 2: Analyze data (serial, depends on Task 1)
        Task(
            name="analyze",
            agent="analysis_agent",
            action="analyze_sales",
            depends_on=["fetch_data"],
            output_schema={
                "top_products": list,
                "growth_rate": float,
                "trends": dict
            }
        ),
        
        # Task 3: Conditional execution (decided based on analysis results)
        Conditional(
            name="escalate",
            condition=lambda ctx: ctx["analyze"]["growth_rate"] < 0,
            if_true=Task(
                name="create_alert",
                agent="notification_agent",
                action="send_alert",
                params={"severity": "high"}
            )
        ),
        
        # Task 4: Generate report (depends on Task 2)
        Task(
            name="generate_report",
            agent="report_agent",
            action="create_sales_report",
            depends_on=["analyze"],
            output_format="pdf"
        ),
        
        # Task 5: Loop send notifications
        Loop(
            name="send_notifications",
            iterator="report.recipients",
            task=Task(
                name="send_email",
                agent="notification_agent",
                action="send_email",
                params={
                    "to": "{{loop.value}}",
                    "attachment": "{{report.file}}"
                }
            )
        )
    ],
    
    error_handling={
        "retry_count": 3,
        "fallback": "send_notification_to_admin",
        "timeout": 3600
    }
)

# Execute workflow
async def run_workflow():
    executor = WorkflowExecutor()
    result = await executor.execute(
        workflow=sales_report_workflow,
        context={"period": "2026-05"}
    )
    return result

from google.antigravity.connectors import (
    SalesforceConnector,
    SlackConnector,
    GitHubConnector,
    DatabaseConnector
)

class EnterpriseOrchestrator:
    """Enterprise cross-system orchestrator"""
    
    def __init__(self):
        self.connectors = {
            "salesforce": SalesforceConnector(
                client_id=os.getenv("SF_CLIENT_ID"),
                client_secret=os.getenv("SF_CLIENT_SECRET")
            ),
            "slack": SlackConnector(
                bot_token=os.getenv("SLACK_BOT_TOKEN")
            ),
            "github": GitHubConnector(
                token=os.getenv("GITHUB_TOKEN")
            ),
            "db": DatabaseConnector(
                connection_string=os.getenv("DB_CONNECTION")
            )
        }
    
    async def execute_cross_system_workflow(
        self,
        workflow_id: str
    ):
        """Execute cross-system workflow"""
        
        # Example: Get sales data from Salesforce
        salesforce_data = await self.connectors["salesforce"].query(
            """
            SELECT Account.Name, SUM(Amount) TotalAmount
            FROM Opportunity
            WHERE CloseDate = THIS_MONTH
            GROUP BY Account.Name
            ORDER BY SUM(Amount) DESC
            LIMIT 10
            """
        )
        
        # Analyze data and generate insights
        insights = await self._analyze_sales_data(salesforce_data)
        
        # Create GitHub Issue to record analysis results
        issue = await self.connectors["github"].create_issue(
            repo="sales-team/analysis",
            title=f"Sales Analysis - {datetime.now().strftime('%Y-%m')}",
            body=self._format_insights(insights),
            labels=["automated-analysis"]
        )
        
        # Send Slack notification
        await self.connectors["slack"].send_message(
            channel="#sales-updates",
            text=f"📊 Monthly sales analysis completed! Top 3:\n" + 
                 "\n".join([
                     f"• {i['account']}: ${i['amount']:,.0f}"
                     for i in insights["top_3"]
                 ])
        )
        
        return {
            "salesforce_data": salesforce_data,
            "insights": insights,
            "github_issue": issue,
            "slack_notified": True
        }

from google.spark import (
    BackgroundExecutor,
    StateStore,
    SessionManager
)

class PersistentAgentSession:
    """Persistent Agent session"""
    
    def __init__(self, user_id: str):
        self.user_id = user_id
        self.executor = BackgroundExecutor()
        self.state_store = StateStore(user_id=user_id)
        self.session_manager = SessionManager()
    
    async def create_session(self, session_type: str) -> str:
        """Create a new session"""
        
        # Load historical state (if any)
        historical_state = await self.state_store.get_latest_state()
        
        # Create new session
        session = await self.session_manager.create(
            user_id=self.user_id,
            session_type=session_type,
            initial_state=historical_state or {},
            persistence=SessionPersistence(
                checkpoint_interval=30,  # Save checkpoint every 30 seconds
                state_retention_days=90  # Retain state for 90 days
            )
        )
        
        return session.session_id
    
    async def execute_background_task(
        self,
        task: Task,
        schedule: Schedule = None
    ):
        """Execute background task"""
        
        # If schedule is specified, use scheduled execution
        if schedule:
            await self.executor.schedule(
                task=task,
                at=schedule.at,
                repeat=schedule.repeat
            )
        else:
            # Execute immediately
            await self.executor.submit(task)
    
    async def handle_push_notification(
        self,
        notification: PushNotification
    ):
        """Handle push notification"""
        
        # Parse notification content
        action = self._parse_notification(notification)
        
        # Handle based on action type
        if action.type == "user_response":
            # User response, update session state
            await self.session_manager.update_state(
                session_id=action.session_id,
                updates={"user_response": action.data}
            )
            # Resume previously paused task
            await self.executor.resume(action.task_id)
        
        elif action.type == "system_event":
            # System event, trigger corresponding processing logic
            await self._handle_system_event(action)
        
        elif action.type == "reminder":
            # Reminder, send reminder message to user
            await self._send_reminder(action)

from google.spark.scheduler import (
    IntelligentScheduler,
    PredictionModel,
    ResourceMonitor
)

class SmartTaskScheduler(IntelligentScheduler):
    """Intelligent task scheduler"""
    
    def __init__(self):
        self.prediction_model = PredictionModel()
        self.resource_monitor = ResourceMonitor()
    
    async def schedule_task(
        self,
        task: Task,
        user_context: UserContext
    ) -> Schedule:
        """Intelligently schedule task"""
        
        # Predict user's next active time
        predicted_active_time = await self.prediction_model.predict(
            user_id=user_context.user_id,
            prediction_type="next_active"
        )
        
        # Get current resource status
        resources = await self.resource_monitor.get_current_state()
        
        # Determine scheduling strategy based on task type and context
        if task.urgency == "high":
            # High priority tasks execute immediately
            return Schedule(immediate=True)
        
        elif task.urgency == "normal":
            # Normal priority tasks execute at predicted active time
            return Schedule(
                at=predicted_active_time,
                optimal_window={
                    "start": predicted_active_time - timedelta(minutes=10),
                    "end": predicted_active_time + timedelta(minutes=30)
                }
            )
        
        elif task.urgency == "low":
            # Low priority tasks batch processed
            return Schedule(
                batch=True,
                batch_window="daily",
                batch_time="02:00"  # Execute at 2 AM
            )
        
        else:
            # Unknown priority, default handling
            return Schedule(immediate=True)
    
    async def batch_similar_tasks(
        self,
        tasks: list[Task]
    ) -> list[Task | BatchTask]:
        """Batch merge similar tasks"""
        
        # Group by type and parameter similarity
        groups = {}
        
        for task in tasks:
            key = self._compute_task_signature(task)
            if key not in groups:
                groups[key] = []
            groups[key].append(task)
        
        # Convert large groups to batch tasks
        result = []
        for key, group in groups.items():
            if len(group) >= 3:  # Only batch if >3 similar tasks
                batch = BatchTask(
                    tasks=group,
                    merge_strategy="sum",  # Aggregate results
                    deduplicate=True
                )
                result.append(batch)
            else:
                result.extend(group)
        
        return result

from google.auth import (
    Credentials,
    ServiceAccountCredentials,
    workload_identity
)
from google.auth.transport.requests import AuthorizedSession

class AgentIdentityManager:
    """Agent identity manager"""
    
    def __init__(self, workload_pool: str):
        self.workload_pool = workload_pool
        self.credentials_cache = {}
    
    async def get_agent_credentials(
        self,
        agent_id: str,
        scopes: list[str]
    ) -> Credentials:
        """Get Agent credentials"""
        
        cache_key = f"{agent_id}:{':'.join(sorted(scopes))}"
        
        if cache_key in self.credentials_cache:
            return self.credentials_cache[cache_key]
        
        # Get workload identity credentials using WIMSE
        wimse_provider = workload_identity.PoolProvider(
            pool_name=self.workload_pool
        )
        
        credentials = await wimse_provider.get_credentials(
            audience=f"agent://{agent_id}",
            scopes=scopes,
            token_lifetime=3600  # 1 hour validity
        )
        
        self.credentials_cache[cache_key] = credentials
        return credentials
    
    async def create_authenticated_session(
        self,
        agent_id: str,
        required_scopes: list[str]
    ) -> AuthorizedSession:
        """Create authenticated HTTP session"""
        
        credentials = await self.get_agent_credentials(
            agent_id=agent_id,
            scopes=required_scopes
        )
        
        session = AuthorizedSession(credentials)
        
        # Add Agent identity headers
        session.headers["X-Agent-ID"] = agent_id
        session.headers["X-Agent-Version"] = "3.5"
        
        return session
    
    async def audit_agent_action(
        self,
        agent_id: str,
        action: str,
        resource: str,
        result: str
    ):
        """Audit Agent actions"""
        
        audit_entry = {
            "timestamp": datetime.now().isoformat(),
            "agent_id": agent_id,
            "action": action,
            "resource": resource,
            "result": result,
            "identity_claim": await self._get_identity_claim(agent_id)
        }
        
        # Send to audit log system
        await self._write_audit_log(audit_entry)

from google.security import (
    PromptInjectionDetector,
    OutputValidator,
    SandboxedExecutor
)

class SecurityMiddleware:
    """Security middleware"""
    
    def __init__(self):
        self.prompt_detector = PromptInjectionDetector()
        self.output_validator = OutputValidator()
        self.sandbox = SandboxedExecutor()
    
    async def process_agent_request(
        self,
        request: AgentRequest
    ) -> AgentRequest | RejectedRequest:
        """Security check for Agent requests"""
        
        # Check prompt injection
        injection_result = await self.prompt_detector.analyze(
            text=request.prompt,
            include_context=True
        )
        
        if injection_result.is_malicious:
            # Log security event
            await self._log_security_event(
                event_type="prompt_injection",
                agent_id=request.agent_id,
                details=injection_result.details
            )
            
            # Return rejected request
            return RejectedRequest(
                original_request=request,
                reason="Potential prompt injection detected",
                action_taken="request_blocked"
            )
        
        # Check permission scope
        required_permissions = self._get_required_permissions(request)
        granted_permissions = request.agent_permissions
        
        missing_permissions = set(required_permissions) - set(granted_permissions)
        if missing_permissions:
            return RejectedRequest(
                original_request=request,
                reason=f"Missing permissions: {missing_permissions}",
                action_taken="approval_required"
            )
        
        return request
    
    async def validate_agent_output(
        self,
        output: AgentOutput
    ) -> AgentOutput | SanitizedOutput:
        """Validate Agent output"""
        
        # Run output validator
        validation_result = await self.output_validator.check(
            content=output.content,
            output_type=output.type
        )
        
        if validation_result.has_issues:
            # Sanitize problematic content
            sanitized = await self.output_validator.sanitize(
                content=output.content,
                rules=validation_result.applicable_rules
            )
            
            return SanitizedOutput(
                original=output,
                sanitized_content=sanitized,
                removed_items=validation_result.removed_items
            )
        
        return output

import asyncio
from dataclasses import dataclass
from typing import Optional
from enum import Enum

from google.generativeai import GenerativeModel
from google.antigravity import Workflow, Task
from google.spark import BackgroundExecutor

@dataclass
class PurchaseRequest:
    """Purchase request"""
    requester: str
    department: str
    item: str
    quantity: int
    estimated_cost: float
    justification: str
    urgency: Enum = Enum('Urgency', 'low normal high critical')

class ProcurementAgent:
    """Intelligent procurement Agent"""
    
    def __init__(self):
        self.model = GenerativeModel("gemini-3.5-pro")
        self.workflow_executor = WorkflowExecutor()
        self.background_executor = BackgroundExecutor()
        
        # Approval thresholds
        self.approval_thresholds = {
            "low": 1000,
            "normal": 5000,
            "high": 20000,
            "critical": float("inf")
        }
    
    async def process_purchase_request(
        self,
        request: PurchaseRequest
    ) -> dict:
        """Process purchase request"""
        
        # Determine approval level
        approval_level = self._determine_approval_level(request)
        
        # Build workflow
        workflow = Workflow(
            name="procurement_approval",
            tasks=[
                # Step 1: Validate request
                Task(
                    name="validate_request",
                    agent="validation_agent",
                    action="validate_procurement_request",
                    params={"request": request}
                ),
                
                # Step 2: Budget check
                Task(
                    name="budget_check",
                    agent="finance_agent",
                    action="check_budget_availability",
                    params={
                        "department": request.department,
                        "amount": request.estimated_cost
                    },
                    depends_on=["validate_request"]
                ),
                
                # Step 3: Vendor matching
                Task(
                    name="vendor_matching",
                    agent="vendor_agent",
                    action="find_best_vendor",
                    params={
                        "item": request.item,
                        "quantity": request.quantity
                    },
                    depends_on=["validate_request"]
                ),
                
                # Step 4: Generate approval request
                Task(
                    name="generate_approval",
                    agent="approval_agent",
                    action="create_approval_request",
                    params={
                        "request": request,
                        "level": approval_level,
                        "vendor": "{{vendor_matching.vendor}}"
                    },
                    depends_on=["budget_check", "vendor_matching"]
                ),
                
                # Step 5: Conditional execution of approval
                Conditional(
                    name="approval_execution",
                    condition=lambda ctx: approval_level == "auto",
                    if_true=Task(
                        name="auto_approve",
                        agent="approval_agent",
                        action="auto_approve",
                        params={"request_id": "{{generate_approval.id}}"}
                    ),
                    if_false=Task(
                        name="send_for_approval",
                        agent="notification_agent",
                        action="send_approval_request",
                        params={
                            "approvers": self._get_approvers(approval_level),
                            "request_id": "{{generate_approval.id}}"
                        }
                    )
                ),
                
                # Step 6: Generate order
                Task(
                    name="generate_order",
                    agent="procurement_agent",
                    action="create_purchase_order",
                    depends_on=["approval_execution"],
                    params={
                        "request": request,
                        "vendor": "{{vendor_matching.vendor}}",
                        "approved_amount": "{{budget_check.approved_amount}}"
                    }
                ),
                
                # Step 7: Send notification
                Task(
                    name="notify_requester",
                    agent="notification_agent",
                    action="send_confirmation",
                    depends_on=["generate_order"],
                    params={
                        "to": request.requester,
                        "order_id": "{{generate_order.order_id}}"
                    }
                )
            ]
        )
        
        # Execute workflow
        result = await self.workflow_executor.execute(workflow)
        
        return result
    
    def _determine_approval_level(self, request: PurchaseRequest) -> str:
        """Determine approval level"""
        amount = request.estimated_cost
        
        if amount <= self.approval_thresholds["low"]:
            return "auto"  # Auto approve
        elif amount <= self.approval_thresholds["normal"]:
            return "manager"  # Manager approval
        elif amount <= self.approval_thresholds["high"]:
            return "director"  # Director approval
        else:
            return "executive"  # Executive approval
    
    def _get_approvers(self, level: str) -> list[str]:
        """Get approver list"""
        approvers = {
            "manager": ["manager@company.com"],
            "director": ["director@company.com", "finance@company.com"],
            "executive": ["cfo@company.com", "ceo@company.com"]
        }
        return approvers.get(level, [])

# Usage example
async def main():
    agent = ProcurementAgent()
    
    request = PurchaseRequest(
        requester="john.doe@company.com",
        department="Engineering",
        item="Developer Workstation",
        quantity=10,
        estimated_cost=15000,
        justification="Team expansion - need 10 new developer machines",
        urgency="normal"
    )
    
    result = await agent.process_purchase_request(request)
    print(f"Purchase request processing result: {result}")

if __name__ == "__main__":
    asyncio.run(main())