Autonomous Workflow Orchestration with Google ADK

Building intelligent agents that automatically execute multi-step business processes with reasoning, planning, and adaptive decision-making.

Overview

Autonomous workflow orchestration uses ADK's agent core to handle complex business processes. Agents reason about the current state, plan next steps, use available tools, and adapt to changes without human intervention.

Basic Workflow Agent

from google_adk import Agent, Tool, Workflow
from typing import Dict, Any

# Define available tools
@Tool(description="Fetch customer data from database")
def fetch_customer(customer_id: str) -> Dict[str, Any]:
    """Retrieve customer information"""
    return {
        "id": customer_id,
        "name": "John Doe",
        "email": "john@example.com",
        "status": "active"
    }

@Tool(description="Create a new order")
def create_order(customer_id: str, items: list) -> Dict[str, Any]:
    """Create order in the system"""
    return {
        "order_id": "ORD-12345",
        "customer_id": customer_id,
        "items": items,
        "total": sum(item["price"] for item in items),
        "status": "pending"
    }

@Tool(description="Send notification to customer")
def send_notification(customer_id: str, message: str) -> bool:
    """Send email or SMS to customer"""
    print(f"Notification sent to {customer_id}: {message}")
    return True

# Define the workflow
workflow = Workflow(
    name="customer_order_workflow",
    description="Process customer order from inquiry to confirmation"
)

# Create agent with tools
agent = Agent(
    name="OrderProcessor",
    tools=[fetch_customer, create_order, send_notification],
    memory_size=10,
    reasoning_depth=5
)

async def run_order_workflow():
    # Agent autonomously orchestrates the workflow
    result = await agent.execute(
        initial_prompt="Process order for customer C123 with items: laptop ($999), mouse ($29), keyboard ($89)",
        workflow=workflow,
        max_steps=10
    )
    
    return result

Advanced Workflow with Conditional Logic

from google_adk import Agent, Tool, Memory, WorkflowState
import asyncio

class OrderWorkflowAgent(Agent):
    def __init__(self):
        super().__init__()
        self.memory = Memory(capacity=100)
        self.workflow_state = WorkflowState()
    
    @Tool(description="Check customer credit limit")
    def check_credit_limit(self, customer_id: str) -> Dict:
        """Validate customer can place order"""
        return {
            "customer_id": customer_id,
            "credit_limit": 5000,
            "used": 1200,
            "available": 3800
        }
    
    @Tool(description="Apply discount if eligible")
    def calculate_discount(self, customer_id: str, order_total: float) -> Dict:
        """Determine applicable discounts"""
        if order_total > 500:
            return {"discount_percent": 10, "discount_amount": order_total * 0.1}
        return {"discount_percent": 0, "discount_amount": 0}
    
    @Tool(description="Process payment")
    def process_payment(self, customer_id: str, amount: float) -> Dict:
        """Handle payment processing"""
        return {
            "status": "success",
            "transaction_id": "TXN-98765",
            "amount": amount,
            "timestamp": "2025-01-21T10:30:00Z"
        }
    
    @Tool(description="Trigger fulfillment")
    def trigger_fulfillment(self, order_id: str) -> Dict:
        """Start warehouse fulfillment process"""
        return {
            "order_id": order_id,
            "status": "fulfillment_started",
            "estimated_delivery": "3-5 business days"
        }
    
    async def orchestrate_order(self, customer_id: str, items: list):
        """Agent autonomously orchestrates entire order process"""
        
        order_total = sum(item["price"] for item in items)
        credit_check = self.check_credit_limit(customer_id)
        
        if credit_check["available"] < order_total:
            return {"error": "Insufficient credit"}
        
        discount = self.calculate_discount(customer_id, order_total)
        final_total = order_total - discount["discount_amount"]
        
        payment_result = self.process_payment(customer_id, final_total)
        if payment_result["status"] != "success":
            return {"error": "Payment failed"}
        
        order_id = f"ORD-{payment_result['transaction_id'].split('-')[1]}"
        fulfillment = self.trigger_fulfillment(order_id)
        
        return {
            "success": True,
            "order_id": order_id,
            "final_total": final_total,
            "estimated_delivery": fulfillment["estimated_delivery"]
        }

Best Practices

• Define clear tool schemas and descriptions for agent reasoning
• Use memory effectively to track workflow state and decisions
• Implement comprehensive error handling and recovery strategies
• Enable agent to re-plan when steps fail or conditions change
• Set reasonable step limits to prevent infinite loops
• Monitor and log agent decisions for debugging and improvement
• Test workflows with various edge cases and failure scenarios

Best Practices

• Use ViewModel to survive configuration changes
• Implement Repository pattern for data abstraction
• Use Coroutines with proper scope for async operations
• Leverage Jetpack Compose for faster UI development
• Implement proper error handling and loading states
• Use ProGuard/R8 for code shrinking and optimization
• Test with both unit tests and instrumentation tests

Common Libraries Stack

dependencies {
    // Jetpack
    implementation("androidx.lifecycle:lifecycle-viewmodel-compose:2.6.1")
    implementation("androidx.compose.ui:ui:1.5.0")
    implementation("androidx.room:room-runtime:2.5.2")
    kapt("androidx.room:room-compiler:2.5.2")

    // Networking
    implementation("com.squareup.retrofit2:retrofit:2.9.0")
    implementation("com.squareup.retrofit2:converter-gson:2.9.0")

    // Kotlin
    implementation("org.jetbrains.kotlinx:kotlinx-coroutines-android:1.7.1")
}