Google Agent Executor & Substrate: A Revolutionary Breakthrough in Open-Source Production-Grade AI Agent Runtime

# Traditional model: Stateless request-response
def handle_request(user_input: str) -> str:
    # Every request has a completely new context
    context = load_context()  # Manual loading required
    response = llm.generate(context, user_input)
    save_context(context)  # Manual saving required
    return response

# Agent Executor model: Persistent state
from google.agent_executor import AgentExecutor, WorkflowState

class MyAgentWorkflow:
    def __init__(self):
        self.executor = AgentExecutor(
            checkpoint_enabled=True,
            event_logging=True,
            durable_execution=True
        )
    
    async def run(self, task_id: str, initial_input: dict):
        """
        Start a persistent workflow.
        The workflow can be interrupted and resumed at any point.
        """
        workflow = await self.executor.create_workflow(
            workflow_id=task_id,
            initial_state={
                "input": initial_input,
                "step": 0,
                "memory": [],
                "results": {}
            }
        )
        
        # Workflow continues execution, supporting interruption and recovery
        result = await workflow.execute()
        return result

# Event log example
event_log = [
    WorkflowCreated(timestamp="2026-05-28T10:00:00Z", workflow_id="wf_001"),
    StepStarted(step_id=1, step_name="analyze_requirement", 
                input={"query": "Analyze sales data"}),
    ToolCallRequested(tool="sql_executor", 
                      params={"query": "SELECT * FROM sales"}),
    ToolCallCompleted(tool="sql_executor", 
                      result={"rows": 1500, "columns": 12}),
    StateSnapshot(state={"analysis": {...}, "confidence": 0.85}),
    # Workflow may be interrupted at this point
]

# Resume execution
await workflow.resume(from_event=4)

from google.agent_executor.snapshot import StateSnapshotManager

class AdvancedSnapshotManager(StateSnapshotManager):
    """Advanced snapshot manager"""
    
    def __init__(self, storage_backend, retention_count=10):
        self.storage = storage_backend
        self.retention_count = retention_count
        self.snapshots = {}
    
    async def create_snapshot(self, workflow_id: str, state: dict) -> str:
        """Create a state snapshot"""
        snapshot_id = generate_snapshot_id()
        snapshot = {
            "workflow_id": workflow_id,
            "snapshot_id": snapshot_id,
            "timestamp": current_timestamp(),
            "state": state,
            "checksum": compute_checksum(state)
        }
        
        # Store snapshot
        await self.storage.put(
            f"snapshots/{workflow_id}/{snapshot_id}", 
            snapshot
        )
        
        # Clean up old snapshots
        await self._cleanup_old_snapshots(workflow_id, snapshot_id)
        
        return snapshot_id
    
    async def restore_snapshot(self, workflow_id: str, 
                              snapshot_id: str) -> dict:
        """Restore from a specific snapshot"""
        snapshot = await self.storage.get(
            f"snapshots/{workflow_id}/{snapshot_id}"
        )
        if not snapshot:
            raise ValueError(f"Snapshot {snapshot_id} not found")
        
        # Verify snapshot integrity
        if compute_checksum(snapshot["state"]) != snapshot["checksum"]:
            raise CorruptionError("Snapshot checksum mismatch")
        
        return snapshot["state"]
    
    async def get_latest_snapshot(self, workflow_id: str) -> Optional[dict]:
        """Get the latest snapshot"""
        snapshot_ids = await self.storage.list(
            f"snapshots/{workflow_id}"
        )
        if not snapshot_ids:
            return None
        
        latest_id = max(snapshot_ids)
        return await self.restore_snapshot(workflow_id, latest_id)

from google.agent_executor.branching import TrajectoryBrancher

class TestDifferentStrategies:
    """Branching tests for different strategies"""
    
    def __init__(self, executor: AgentExecutor):
        self.executor = executor
        self.brancher = TrajectoryBrancher(executor)
    
    async def run_ab_test(self, workflow_id: str, 
                          strategy_a: str, strategy_b: str):
        """
        Create A/B testing branches based on historical state
        """
        # Get current workflow's latest state
        current_state = await self.executor.get_state(workflow_id)
        
        # Create Branch A: Use Strategy A
        branch_a_id = await self.brancher.create_branch(
            parent_workflow_id=workflow_id,
            parent_snapshot_id=current_state["snapshot_id"],
            branch_id="strategy_a_test",
            override_config={"strategy": strategy_a}
        )
        
        # Create Branch B: Use Strategy B
        branch_b_id = await self.brancher.create_branch(
            parent_workflow_id=workflow_id,
            parent_snapshot_id=current_state["snapshot_id"],
            branch_id="strategy_b_test",
            override_config={"strategy": strategy_b}
        )
        
        # Execute both branches in parallel
        result_a = await self.executor.execute_workflow(branch_a_id)
        result_b = await self.executor.execute_workflow(branch_b_id)
        
        # Compare results
        return {
            "strategy_a_result": result_a,
            "strategy_b_result": result_b,
            "winner": self._determine_winner(result_a, result_b)
        }

from google.agent_executor.approval import (
    ApprovalManager, 
    ApprovalRequest
)

class EnterpriseApprovalWorkflow:
    """Enterprise approval workflow"""
    
    def __init__(self):
        self.approval_mgr = ApprovalManager()
    
    async def run_with_approvals(self, task: dict):
        """Workflow execution with approvals"""
        workflow = await self.executor.create_workflow(
            initial_state={"task": task}
        )
        
        # Define steps requiring approval
        approval_points = {
            "financial_transfer": {
                "threshold": 10000,
                "approvers": ["finance_manager", "ceo"],
                "timeout": 3600  # 1 hour timeout
            },
            "data_deletion": {
                "threshold": "any",
                "approvers": ["dpo", "data_owner"],
                "timeout": 7200
            }
        }
        
        async for step in workflow.step_generator():
            # Check if approval is required
            if step.name in approval_points:
                approval_req = ApprovalRequest(
                    workflow_id=workflow.id,
                    step_id=step.id,
                    action=step.name,
                    details=step.details,
                    required_approvers=approval_points[step.name]["approvers"],
                    timeout=approval_points[step.name]["timeout"]
                )
                
                # Pause workflow, wait for approval
                approved = await self.approval_mgr.request_approval(approval_req)
                
                if not approved:
                    workflow.cancel(
                        f"Rejected by {approval_req.approvers}"
                    )
                    return {
                        "status": "rejected",
                        "reason": approval_req.rejection_reason
                    }
            
            # Continue execution
            await workflow.execute_step(step)
        
        return workflow.final_result

# Agent Substrate CRD (Custom Resource Definition) Example
apiVersion: agent.googleapis.com/v1
kind: AgentGroup
metadata:
  name: customer-service-agents
  namespace: production
spec:
  agentType: "customer-support"
  replicaCount: 100
  maxConcurrentTasks: 10
  resources:
    requests:
      cpu: "500m"
      memory: "512Mi"
    limits:
      cpu: "2000m"
      memory: "2Gi"
  persistence:
    enabled: true
    storageClass: "fast-ssd"
    accessMode: "ReadWriteMany"
  scaling:
    minReplicas: 10
    maxReplicas: 1000
    targetCPUUtilization: 70
  security:
    serviceAccount: "agent-sa"
    networkPolicy: "agent-network-policy"
    podSecurityPolicy: "restricted"

from kubernetes import client
from agent_substrate.snapshot import PodSnapshotManager

class AgentPodSnapshotManager(PodSnapshotManager):
    """Agent Pod snapshot manager"""
    
    def __init__(self, k8s_client: client.ApiClient):
        self.k8s = k8s_client
        self.snapshot_api = client.CustomObjectsApi(k8s_client)
    
    async def create_pod_snapshot(self, pod_name: str, 
                                  namespace: str) -> str:
        """Create a Pod snapshot"""
        # Get complete Pod spec
        pod = self.k8s.read_namespaced_pod(pod_name, namespace)
        
        # Capture all Pod Volumes
        volumes = self._capture_volumes(pod.spec.volumes)
        
        # Capture Pod network state
        network_state = await self._capture_network_state(pod)
        
        # Capture Agent memory state
        agent_state = await self._capture_agent_state(pod)
        
        snapshot = {
            "apiVersion": "agent.googleapis.com/v1",
            "kind": "AgentPodSnapshot",
            "metadata": {
                "generateName": f"{pod_name}-snapshot-"
            },
            "spec": {
                "originalPod": pod.metadata.name,
                "originalNode": pod.spec.node_name,
                "volumes": volumes,
                "networkState": network_state,
                "agentState": agent_state
            }
        }
        
        # Create snapshot resource
        snapshot_obj = self.snapshot_api.create_namespaced_custom_object(
            group="agent.googleapis.com",
            version="v1",
            namespace=namespace,
            plural="agentpodsnapshots",
            body=snapshot
        )
        
        return snapshot_obj["metadata"]["name"]
    
    async def restore_pod_from_snapshot(
        self, 
        snapshot_name: str, 
        namespace: str,
        target_node: str = None
    ) -> str:
        """Restore Pod from snapshot"""
        snapshot = self.snapshot_api.get_namespaced_custom_object(
            group="agent.googleapis.com",
            version="v1",
            namespace=namespace,
            plural="agentpodsnapshots",
            name=snapshot_name
        )
        
        # Reconstruct Pod spec
        new_pod_spec = self._reconstruct_pod_spec(snapshot["spec"])
        
        # Set node affinity if target specified
        if target_node:
            new_pod_spec.affinity = client.V1Affinity(
                node_affinity=client.V1NodeAffinity(
                    required_during_scheduling_ignored_during_execution=(
                        client.V1NodeSelector(
                            node_selector_terms=[
                                client.V1NodeSelectorTerm(
                                    match_expressions=[
                                        client.V1NodeSelectorRequirement(
                                            key="kubernetes.io/hostname",
                                            operator="In",
                                            values=[target_node]
                                        )
                                    ]
                                )
                            ]
                        )
                    )
                )
            )
        
        # Create new Pod
        new_pod = client.V1Pod(
            api_version="v1",
            kind="Pod",
            metadata=client.V1ObjectMeta(
                generate_name=f"{snapshot['spec']['originalPod']}-restored-"
            ),
            spec=new_pod_spec
        )
        
        created_pod = self.k8s.create_namespaced_pod(namespace, new_pod)
        return created_pod.metadata.name

from agent_substrate.scheduler import AgentScheduler
from agent_substrate.load_balancer import AgentLoadBalancer

class HyperscaleAgentManager:
    """Hyperscale Agent manager"""
    
    def __init__(
        self,
        scheduler: AgentScheduler,
        load_balancer: AgentLoadBalancer,
        state_store,
        cache_layers
    ):
        self.scheduler = scheduler
        self.load_balancer = load_balancer
        self.state_store = state_store
        self.cache = cache_layers
    
    async def register_agent(self, agent: AgentMetadata) -> str:
        """Register a new Agent"""
        # Generate unique ID
        agent_id = self.scheduler.generate_agent_id()
        
        # Check cache
        cached = await self.cache.get(f"agent:{agent.type}")
        if cached and cached.count < cached.capacity:
            # Assign directly to existing Pod
            return await self._assign_to_pod(agent_id, cached.pod_id)
        
        # Register in state store
        agent_record = {
            "id": agent_id,
            "type": agent.type,
            "version": agent.version,
            "registered_at": current_timestamp(),
            "status": "active"
        }
        await self.state_store.put(f"agents/{agent_id}", agent_record)
        
        # Update index cache
        await self.cache.put(f"agent_index:{agent.type}", agent_id)
        
        return agent_id
    
    async def route_tool_call(self, 
                             tool_call: ToolCallRequest) -> ToolCallResponse:
        """Route tool call to appropriate Agent"""
        # L1 cache: Check recent routes for hot Tools
        cache_key = f"route:{tool_call.tool_name}:{tool_call.tenant_id}"
        cached_route = await self.cache.get(cache_key)
        
        if cached_route:
            return await self._execute_tool_call(
                cached_route.agent_id, 
                tool_call
            )
        
        # Query state store for available Agents
        agent_ids = await self.state_store.get(
            f"agent_index:{tool_call.tool_name}"
        )
        
        if not agent_ids:
            raise NoAvailableAgentError(
                f"No agent available for tool: {tool_call.tool_name}"
            )
        
        # Load balance selection
        selected_agent = await self.load_balancer.select(
            candidates=agent_ids,
            criteria=tool_call.routing_criteria
        )
        
        # Cache routing result
        await self.cache.put(cache_key, selected_agent, ttl=60)
        
        return await self._execute_tool_call(selected_agent, tool_call)

from agent_substrate.security import (
    AgentSecurityManager,
    SandboxConfig,
    NetworkPolicy,
    IAMPolicy
)

class SecureAgentDeployment:
    """Secure Agent deployment configuration"""
    
    def __init__(self):
        self.security_mgr = AgentSecurityManager()
    
    def create_sandbox_config(self) -> SandboxConfig:
        """Create sandbox configuration"""
        return SandboxConfig(
            runtime="gke-sandbox",  # or "kata-containers"
            filesystem_readonly=True,
            filesystem_whitelist=[
                "/tmp/agent_workspace",
                "/var/agent_data"
            ],
            network_mode="none",  # Completely disable network
            allowed_networks=[
                "internal-mesh",
                "agent-api-backend"
            ],
            resources=ResourceLimits(
                cpu_period=100000,
                cpu_quota=200000,
                memory_limit=2 * 1024 * 1024 * 1024,  # 2GB
                pids_limit=100
            ),
            syscalls_filter=[
                # Allowed system calls
                "read", "write", "open", "close",
                "brk", "mmap", "mprotect",
                "sched_yield", "nanosleep"
                # Forbidden dangerous system calls
            ]
        )
    
    def create_network_policy(self) -> NetworkPolicy:
        """Create network policy"""
        return NetworkPolicy(
            policy_type="default_deny",  # Default deny all
            ingress_rules=[
                # Only allow traffic from API Gateway
                AllowedSource(
                    pod_selector={"component": "api-gateway"},
                    ports=[8080]
                )
            ],
            egress_rules=[
                # Only allow access to specific external services
                AllowedDestination(
                    pod_selector={"component": "agent-backend"},
                    ports=[8080, 9090]
                ),
                AllowedDestination(
                    dns=["internal-mesh.svc.cluster.local"],
                    ports=[443]
                )
            ]
        )
    
    def create_iam_policy(self) -> IAMPolicy:
        """Create IAM policy"""
        return IAMPolicy(
            service_account="agent-service-account",
            role_bindings=[
                RoleBinding(
                    role="roles/agent.tool-access",
                    # Only allow access to specific tools
                    allowed_tools=["sql-query", "file-read", "http-request"]
                ),
                RoleBinding(
                    role="roles/data.reader",
                    # Only allow reading specific data sources
                    allowed_resources=[
                        "projects/*/datasets/reporting_data",
                        "projects/*/buckets/reports"
                    ]
                ),
                # Denied operations
                DeniedBinding(
                    role="roles/owner",
                    reason="Agent must never have owner privileges"
                )
            ]
        )

from langchain.agents import AgentExecutor as LCAgentExecutor
from google.agent_executor import AgentExecutor, Adapter

# Create Agent Executor adapter
adapter = Adapter(
    executor=AgentExecutor(
        checkpoint_enabled=True,
        event_logging=True
    )
)

# Use LangChain's ReAct Agent
agent = create_langchain_react_agent(
    model=ChatGoogleGenerativeAI(model="gemini-2.0"),
    tools=my_tools,
    prompt=my_prompt
)

# Run through adapter, supporting persistence
chain = LCAgentExecutor(
    agent=agent,
    tools=my_tools,
    adapter=adapter  # Inject Agent Executor capabilities
)

# Execute, supporting interruption and recovery
result = await chain.arun("Analyze this month's sales data")

from langgraph.graph import StateGraph
from google.agent_executor import AgentExecutor, CheckpointSaver

# Define state graph
graph = StateGraph(SalesAnalysisState)

graph.add_node("research", research_node)
graph.add_node("analyze", analyze_node)
graph.add_node("report", report_node)

graph.set_entry_point("research")
graph.add_edge("research", "analyze")
graph.add_edge("analyze", "report")
graph.set_finish_point("report")

# Compile, enabling Agent Executor checkpoints
compiled_graph = graph.compile(
    checkpoint=CheckpointSaver(
        executor=AgentExecutor(checkpoint_enabled=True)
    )
)

# Run, state automatically persists
async for state in compiled_graph.astream(initial_state):
    print(state)

from agent_substrate.deployment import (
    DeploymentConfig,
    CloudProvider,
    HybridConfig
)

# GKE deployment configuration
gke_config = DeploymentConfig(
    provider=CloudProvider.GKE,
    project="my-gcp-project",
    region="us-central1",
    features=[
        "autopilot",
        "security_posture",
        "binary_authorization"
    ]
)

# Hybrid cloud configuration
hybrid_config = HybridConfig(
    control_plane=DeploymentConfig(
        provider=CloudProvider.GKE,
        project="my-gcp-project",
        region="us-central1"
    ),
    data_planes=[
        DataPlaneConfig(
            provider=CloudProvider.AWS,
            region="us-east-1",
            agent_count=10000
        ),
        DataPlaneConfig(
            provider=CloudProvider.AZURE,
            region="eastus",
            agent_count=10000
        ),
        DataPlaneConfig(
            provider=CloudProvider.SELF_HOSTED,
            location="on-prem-datacenter",
            agent_count=50000
        )
    ],
    data_routing=DataRoutingPolicy(
        prefer_local=True,
        fallback_cloud=CloudProvider.GKE
    )
)

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

from google.agent_executor import AgentExecutor, WorkflowState
from agent_substrate import AgentGroup, ScalingConfig
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.tools import tool

# ========== Tool Definitions ==========

@dataclass
class CustomerContext:
    """Customer context"""
    customer_id: str
    tier: str  # 'basic', 'premium', 'enterprise'
    account_age_days: int
    previous_tickets: list = field(default_factory=list)

@tool
def lookup_customer(customer_id: str) -> dict:
    """Query customer information"""
    # In actual implementation, call CRM API
    return {
        "customer_id": customer_id,
        "tier": "premium",
        "account_age_days": 365,
        "previous_tickets": ["TICKET-001", "TICKET-002"]
    }

@tool
def get_order_history(customer_id: str) -> list:
    """Get order history"""
    return [
        {"order_id": "ORD-001", "date": "2026-05-01", "amount": 299.99},
        {"order_id": "ORD-002", "date": "2026-05-15", "amount": 149.99}
    ]

@tool
def create_support_ticket(
    customer_id: str,
    subject: str,
    description: str,
    priority: str
) -> str:
    """Create support ticket"""
    ticket_id = f"TICKET-{hash(subject) % 100000}"
    return ticket_id

@tool
def send_email(to: str, subject: str, body: str) -> bool:
    """Send email"""
    return True

# ========== Agent Implementation ==========

class CustomerServiceAgent:
    """Intelligent customer service Agent"""
    
    def __init__(self):
        # Initialize LLM
        self.llm = ChatGoogleGenerativeAI(
            model="gemini-2.0-pro",
            temperature=0.7
        )
        
        # Initialize Agent Executor
        self.executor = AgentExecutor(
            checkpoint_enabled=True,
            event_logging=True,
            durable_execution=True
        )
        
        # Tool list
        self.tools = [
            lookup_customer,
            get_order_history,
            create_support_ticket,
            send_email
        ]
        
        # Business rules
        self.escalation_threshold = {
            "basic": 500,  # Amount over 500 needs escalation
            "premium": 1000,
            "enterprise": float("inf")  # No escalation
        }
    
    def _build_system_prompt(self, customer_tier: str) -> str:
        """Build system prompt"""
        tier_prompts = {
            "basic": "You are a professional customer service representative.",
            "premium": "You are a senior customer service agent.",
            "enterprise": "You are an enterprise customer service expert."
        }
        
        return f"""
{tier_prompts.get(customer_tier, tier_prompts['basic'])}

Workflow:
1. First query customer information to understand background
2. Collect necessary information based on customer issue type
3. Provide solution or create ticket
4. For high-value customers (orders over ${self.escalation_threshold.get(customer_tier, 500)}),
   automatically escalate to senior agent

Communication principles:
- Use friendly, professional tone
- Explain complex issues in simple language
- Never promise what you cannot deliver
"""
    
    async def handle_customer_request(
        self,
        workflow_id: str,
        customer_id: str,
        request: str
    ) -> dict:
        """Handle customer request"""
        
        # Get customer context
        customer = lookup_customer.invoke(customer_id)
        customer_context = CustomerContext(
            customer_id=customer_id,
            tier=customer["tier"],
            account_age_days=customer["account_age_days"]
        )
        
        # Build initial state
        initial_state = {
            "customer": customer_context,
            "request": request,
            "conversation_history": [],
            "escalated": False,
            "resolution": None
        }
        
        # Create workflow
        workflow = await self.executor.create_workflow(
            workflow_id=workflow_id,
            initial_state=initial_state
        )
        
        try:
            # Execute processing flow
            result = await self._process_with_retry(workflow)
            return {
                "status": "resolved",
                "workflow_id": workflow_id,
                "result": result
            }
        except HumanApprovalRequired as e:
            # Human approval needed (e.g., refund exceeds threshold)
            return {
                "status": "pending_approval",
                "workflow_id": workflow_id,
                "approval_details": e.details
            }
        except Exception as e:
            return {
                "status": "failed",
                "workflow_id": workflow_id,
                "error": str(e)
            }
    
    async def _process_with_retry(
        self,
        workflow: WorkflowState,
        max_retries: int = 3
    ) -> dict:
        """Processing logic with retry"""
        
        for attempt in range(max_retries):
            try:
                # Build prompt
                prompt = self._build_workflow_prompt(workflow.current_state)
                
                # Call LLM
                response = await self.llm.agenerate([prompt])
                
                # Parse response and execute tool calls
                actions = self._parse_llm_response(response)
                
                for action in actions:
                    if action.type == "tool_call":
                        result = await workflow.execute_tool(
                            action.tool_name,
                            action.parameters
                        )
                        workflow.add_to_history(action, result)
                    elif action.type == "escalate":
                        workflow.update_state({"escalated": True})
                    elif action.type == "resolve":
                        workflow.update_state({
                            "resolution": action.resolution
                        })
                
                # Check if completed
                if workflow.current_state.get("resolution"):
                    return workflow.current_state["resolution"]
                
            except ToolCallError as e:
                if attempt == max_retries - 1:
                    raise
                # Retry logic
                await asyncio.sleep(2 ** attempt)
        
        raise MaxRetriesExceededError("Failed after maximum retries")

# ========== Deployment Configuration ==========

def create_production_deployment() -> AgentGroup:
    """Create production deployment configuration"""
    
    return AgentGroup(
        name="customer-service-prod",
        agent_type="customer-support",
        scaling=ScalingConfig(
            min_replicas=10,
            max_replicas=1000,
            target_concurrent_requests=100,
            scale_up_cooldown=60,
            scale_down_cooldown=300
        ),
        resources=ResourceRequirements(
            requests={"cpu": "1", "memory": "2Gi"},
            limits={"cpu": "4", "memory": "8Gi"}
        ),
        security=SecurityConfig(
            sandbox_enabled=True,
            network_policy="customer-service-netpol",
            service_account="customer-service-sa"
        ),
        persistence=PersistenceConfig(
            enabled=True,
            checkpoint_interval_seconds=30,
            max_checkpoints_per_workflow=100
        )
    )

# ========== Main Program ==========

async def main():
    agent = CustomerServiceAgent()
    deployment = create_production_deployment()
    
    # Deploy Agent Group
    await deployment.deploy()
    
    # Handle customer request
    result = await agent.handle_customer_request(
        workflow_id="wf_cs_20260528_001",
        customer_id="CUST-12345",
        request="I want to request a return, order number ORD-002"
    )
    
    print(f"Processing result: {result}")

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