---
title: "OpenAI Agents SDK Analysis - Major Changes (v0.0.15 → v0.0.19 | Code Examples)"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# OpenAI Agents SDK Analysis - Major Changes (v0.0.15 → v0.0.19 | Code Examples)

This folder demonstrates the **agentic infrastructure capabilities** introduced in OpenAI Agents SDK v0.0.19, showcasing the evolution from basic agent framework to production-ready infrastructure layer.

## 🎯 Overview

The examples in this folder prove that the OpenAI Agents SDK has evolved into an **agentic infrastructure platform** capable of supporting enterprise-scale deployments, including the **DACA (Dapr Agentic Cloud Ascent)** design pattern for 10+ million concurrent agents.

## 📁 Examples

### 1. **Custom Agent Runner** (`01_custom_runner.py`)

**What it demonstrates**: Complete replacement of the default agent execution runtime with custom preprocessing and postprocessing logic.

```python
class CustomAgentRunner(AgentRunner):
    async def run(self, starting_agent, input, **kwargs):
        # Custom preprocessing: routing, load balancing, monitoring
        print(f"CustomAgentRunner.run() - Infrastructure Layer")
        
        # Core execution with custom logic  
        result = await super().run(starting_agent, input, **kwargs)
        
        # Custom postprocessing: analytics, state persistence, logging
        return result

set_default_agent_runner(CustomAgentRunner())
```

**Infrastructure Use Cases**:
- **Load Balancing**: Route requests across multiple agent instances
- **Monitoring & Analytics**: Track agent performance and usage patterns
- **Custom Routing**: Direct requests based on business logic
- **DACA Integration**: Interface with Dapr Actors and Workflows
- **State Persistence**: Save agent state to distributed stores
- **Security**: Add authentication and authorization layers

**Production Benefits**:
- ✅ Complete control over agent execution pipeline
- ✅ Enterprise-grade monitoring and observability
- ✅ Seamless integration with cloud-native infrastructure
- ✅ Support for millions of concurrent agents

---

### 2. **Conditional Tools** (`02_tool_dynamic_permission.py`)

**What it demonstrates**: Dynamic enabling/disabling of tools based on user context, subscription tiers, and business logic.

```python
def premium_feature_enabled(context: RunContextWrapper, agent: Agent) -> bool:
    return context.context.subscription_tier in ["premium", "enterprise"]

@function_tool(is_enabled=premium_feature_enabled)
def get_weather(city: str) -> str:
    return "Weather data for premium users"
```

**Infrastructure Use Cases**:
- **Subscription Tiers**: Enable premium features for paying customers
- **A/B Testing**: Gradually roll out new capabilities
- **Feature Flags**: Control feature availability in real-time
- **Permission Systems**: Role-based access control
- **Resource Management**: Limit expensive operations
- **Compliance**: Restrict tools based on regulatory requirements

**Production Benefits**:
- ✅ Real-time feature control without code changes
- ✅ Revenue optimization through tiered access
- ✅ Risk mitigation through controlled rollouts
- ✅ Compliance and governance enforcement

---

### 3. **Conditional Handoffs** (`03_handoff_dynamic_permission.py`)

**What it demonstrates**: Context-aware agent handoffs with permission-based routing and expert delegation.

```python
agent.handoffs = [handoff(
    expert_agent, 
    is_enabled=lambda ctx, agent: ctx.context.has_permission
)]
```

**Infrastructure Use Cases**:
- **Expert Routing**: Route complex queries to specialized agents
- **Permission Gating**: Control access to sensitive workflows
- **Workflow Orchestration**: Dynamic agent collaboration
- **Resource Optimization**: Efficient agent utilization
- **Quality Control**: Ensure appropriate expertise for tasks
- **Compliance**: Restrict sensitive operations

**Production Benefits**:
- ✅ Intelligent workload distribution
- ✅ Enhanced security through access controls
- ✅ Improved quality through expert routing
- ✅ Efficient resource utilization

## 🏗️ Infrastructure Transformation

### Before (v0.0.15): Basic Agent Framework
```python
# Simple, static agent execution
agent = Agent(instructions="Help the user")
result = await Runner.run(agent, "Hello")
```

### After (v0.0.19): Agentic Infrastructure Platform
```python
# Enterprise-ready, customizable, context-aware execution
class ProductionAgentRunner(AgentRunner):
    async def run(self, starting_agent, input, **kwargs):
        # Load balancing, monitoring, security, analytics
        return await super().run(starting_agent, input, **kwargs)

set_default_agent_runner(ProductionAgentRunner())

@function_tool(is_enabled=lambda ctx, agent: ctx.context.subscription_tier == "enterprise")
def advanced_analysis(data: str) -> str:
    return "Enterprise-level insights"

agent = Agent(
    instructions="Provide contextual assistance",
    tools=[advanced_analysis],
    handoffs=[handoff(expert_agent, is_enabled=permission_check)]
)
```

## 🎯 DACA Framework Integration

These examples demonstrate perfect alignment with the **DACA (Dapr Agentic Cloud Ascent)** design pattern:

### **Custom Runners → Dapr Integration**
```python
class DaprAgentRunner(AgentRunner):
    async def run(self, starting_agent, input, **kwargs):
        # Interface with Dapr Actors for state management
        # Use Dapr Workflows for orchestration
        # Leverage Dapr messaging for A2A communication
        return await super().run(starting_agent, input, **kwargs)
```

### **Conditional Tools → Feature Management**
```python
# Supports DACA's subscription tier model
@function_tool(is_enabled=lambda ctx, agent: daca_feature_enabled(ctx, "weather_api"))
def weather_tool(city: str) -> str:
    return get_weather_from_api(city)
```

### **Conditional Handoffs → Agent Orchestration**
```python
# Enables A2A (Agent-to-Agent) Protocol workflows
agent.handoffs = [handoff(
    specialized_agent,
    is_enabled=lambda ctx, agent: a2a_routing_logic(ctx, agent)
)]
```

## 🚀 Production Readiness

These examples prove the SDK is ready for:

- ✅ **10+ Million Concurrent Agents**: Custom runners support massive scale
- ✅ **Enterprise Security**: Permission-based access controls
- ✅ **Revenue Optimization**: Subscription tier management
- ✅ **Cloud-Native Deployment**: Kubernetes and container ready
- ✅ **Observability**: Built-in monitoring and analytics hooks
- ✅ **Compliance**: Granular control over agent capabilities


## 🎯 Key Takeaways

1. **Infrastructure Evolution**: SDK has evolved from basic framework to enterprise platform
2. **DACA Alignment**: Perfect foundation for scalable agentic systems
3. **Production Ready**: Supports millions of concurrent agents with enterprise features
4. **Extensible**: Custom runners enable unlimited customization possibilities
5. **Business Logic Integration**: Conditional tools/handoffs support real-world requirements

---

**These examples demonstrate that the OpenAI Agents SDK v0.0.19 provides the infrastructure foundation needed for DACA.** 🌍🤖
