Building Custom AI Agents with IBM watsonx Orchestrate ADK

IBM watsonx Orchestrate is transforming how enterprises automate complex workflows through AI agents. With the Agent Development Kit (ADK), developers can build, test, and deploy custom agents that integrate seamlessly with business systems. In this guide, we'll explore how to leverage the ADK to create powerful enterprise AI agents.

What is the watsonx Orchestrate ADK?

The IBM watsonx Orchestrate Agent Development Kit (ADK) is a comprehensive toolkit for building AI agents that run on the watsonx Orchestrate platform. It provides:

Python Library: Core development framework for agent logic
Command Line Interface (CLI): Tools for managing agents and deployments
Developer Edition: Local development and testing environment
Evaluation Framework: Testing and validation tools

Getting Started with Agent Development

Setting Up Your Environment

The ADK is distributed as a Python package, making it easy to integrate into existing development workflows:

# Install the watsonx Orchestrate ADK
pip install watsonx-orchestrate-adk

# Initialize a new agent project
wxo init my-custom-agent

Understanding Agent Architecture

A watsonx Orchestrate agent consists of several key components:

Agent Core: The main logic that processes user requests and orchestrates responses
Tools: Functions that extend agent capabilities (API calls, data processing, etc.)
Knowledge Bases: RAG-powered data sources for grounding agent responses
Connections: Integrations with enterprise systems and external APIs
Channels: Deployment targets for user interaction

Building Custom Tools

Tools are the building blocks of agent functionality. They enable agents to perform specific actions:

from wxo_adk import Tool, tool

@tool(
    name="get_customer_info",
    description="Retrieve customer information from CRM"
)
def get_customer_info(customer_id: str) -> dict:
    """
    Fetches customer data from the enterprise CRM system.
    
    Args:
        customer_id: The unique customer identifier
        
    Returns:
        Customer profile data including contact info and history
    """
    # Integration logic with your CRM system
    return fetch_from_crm(customer_id)

Tool Best Practices

Clear Descriptions: Help the LLM understand when to use each tool
Strong Typing: Use type hints for reliable parameter handling
Error Handling: Implement graceful failure modes
Documentation: Include docstrings for maintenance and debugging

Creating Knowledge Bases

Knowledge bases enable agents to access and reason over your business data:

from wxo_adk import KnowledgeBase

# Create a knowledge base from documents
kb = KnowledgeBase(
    name="product_documentation",
    description="Product manuals, FAQs, and support articles",
    sources=[
        "./docs/product-manual.pdf",
        "./docs/faqs/",
        "./docs/support-articles/"
    ],
    embedding_model="text-embedding-3-large"
)

# Associate with your agent
agent.add_knowledge_base(kb)

RAG Architecture Considerations

Chunking Strategy: Optimize document splitting for your content type
Embedding Selection: Choose models appropriate for your domain
Retrieval Tuning: Balance precision and recall for your use case
Freshness: Implement update pipelines for dynamic content

Enterprise Integrations

watsonx Orchestrate supports integration with 80+ enterprise applications:

Salesforce Integration

from wxo_adk.connections import SalesforceConnection

salesforce = SalesforceConnection(
    instance_url="https://yourorg.salesforce.com",
    auth_type="oauth2"
)

@tool(name="create_salesforce_lead")
def create_lead(name: str, email: str, company: str):
    return salesforce.create("Lead", {
        "Name": name,
        "Email": email,
        "Company": company
    })

Microsoft 365 Integration

from wxo_adk.connections import Microsoft365Connection

m365 = Microsoft365Connection(tenant_id="your-tenant-id")

@tool(name="schedule_meeting")
def schedule_meeting(attendees: list, subject: str, datetime: str):
    return m365.calendar.create_event(
        attendees=attendees,
        subject=subject,
        start=datetime
    )

Multi-Agent Orchestration

For complex workflows, multiple agents can work together:

from wxo_adk import Agent, Orchestrator

# Define specialized agents
hr_agent = Agent(name="hr_assistant", tools=[...])
finance_agent = Agent(name="finance_assistant", tools=[...])
it_agent = Agent(name="it_support", tools=[...])

# Create orchestrator for coordination
orchestrator = Orchestrator(
    agents=[hr_agent, finance_agent, it_agent],
    routing_strategy="intent_based"
)

Testing and Evaluation

The ADK includes a comprehensive evaluation framework:

from wxo_adk.evaluation import AgentEvaluator, TestCase

evaluator = AgentEvaluator(agent)

# Define test cases
test_cases = [
    TestCase(
        input="What is our refund policy?",
        expected_tools=["search_knowledge_base"],
        expected_output_contains=["30 days", "full refund"]
    ),
    TestCase(
        input="Create a support ticket for customer 12345",
        expected_tools=["create_support_ticket"],
        expected_output_contains=["ticket created"]
    )
]

# Run evaluation
results = evaluator.run(test_cases)
print(f"Pass rate: {results.pass_rate}%")

Deployment to Orchestrate Catalog

Once your agent is ready, deploy it to the watsonx Orchestrate catalog:

# Validate agent configuration
wxo validate

# Deploy to staging environment
wxo deploy --environment staging

# Promote to production
wxo deploy --environment production --catalog

Governance and Security

Access Controls: Define who can use your agent
Guardrails: Set boundaries for agent behavior
Audit Logging: Track all agent interactions
Compliance: Ensure adherence to enterprise policies

Real-World Example: Uptok Video Commerce Agent

At Kwanso, we built a custom agent for Uptok that is now live in the IBM watsonx Orchestrate catalog. This agent enables:

Video commerce workflow automation
Customer engagement orchestration
Integration with Uptok's video platform
Enterprise CRM connectivity

The agent demonstrates how custom ADK development can extend watsonx Orchestrate capabilities for specific business domains.

Best Practices Summary

Start Simple: Begin with core functionality and iterate
Test Thoroughly: Use the evaluation framework extensively
Document Everything: Maintain clear tool and agent documentation
Monitor Performance: Track agent metrics post-deployment
Plan for Scale: Design with enterprise deployment in mind

Conclusion

The IBM watsonx Orchestrate ADK provides a powerful foundation for building enterprise AI agents. By leveraging its Python-based development model, comprehensive tooling, and enterprise integration capabilities, organizations can create custom agents that automate complex workflows and drive business value.

Ready to build your own watsonx Orchestrate agent? Contact our team to discuss your requirements and see how we can help bring your AI agent vision to life.