Initialize OpenAI Agents for Enterprise Deployment

Using OpenAI Agents SDK, define a 'Deployment Strategist' agent (GPT-5 Pro), a 'Compliance & Doc Agent' (Claude 4 Sonnet), and a 'Performance Engineer' agent (GPT-5 Pro). Each agent should have access to a simulated tool for interacting with deployment resources. For example, the Strategist might have a `provision_infrastructure` tool, the Compliance agent a `generate_compliance_report` tool, and the Performance Engineer an `optimize_inference` tool (simulating TensorRT-LLM). ```python
from openai import OpenAI client = OpenAI(api_key='YOUR_OPENAI_API_KEY') # Define tools (simplified for prompt)
def provision_infrastructure(env_type: str, cloud_provider: str): print(f'Simulating provisioning {env_type} on {cloud_provider}') return {'status': 'success', 'details': f'Infrastructure for {env_type} provisioned.'} tools_config = [ { 'type': 'function', 'function': { 'name': 'provision_infrastructure', 'description': 'Simulates provisioning cloud infrastructure.', 'parameters': { 'type': 'object', 'properties': { 'env_type': {'type': 'string'}, 'cloud_provider': {'type': 'string'} }, 'required': ['env_type', 'cloud_provider'] } } }
] strategist_assistant = client.beta.assistants.create( name='Deployment Strategist', instructions='You are an expert in cloud infrastructure deployment and architecture. Your role is to plan and execute the setup of enterprise AI environments.', model='gpt-5-pro', tools=tools_config
) compliance_assistant = client.beta.assistants.create( name='Compliance & Doc Agent', instructions='You specialize in generating compliance reports and documentation for enterprise AI systems.', model='claude-4-sonnet', # Example: Add a tool for generating compliance docs tools=[] ) performance_assistant = client.beta.assistants.create( name='Performance Engineer', instructions='You optimize AI model inference for speed and efficiency.', model='gpt-5-pro', # Example: Add a tool for optimizing inference with TensorRT-LLM tools=[]
) # Example of creating a thread and sending a message (actual orchestration would be more complex):
# thread = client.beta.threads.create()
# message = client.beta.threads.messages.create(
# thread_id=thread.id,
# role='user',
# content='Plan a production deployment for an AI chatbot on AWS EKS.'
# )
# run = client.beta.threads.runs.create(thread_id=thread.id, assistant_id=strategist_assistant.id)
# print(run)
```