Hi community,

I am finalizing a proposal for a startup developing Python-based AI Agent services that is transitioning from MVP to a full-scale Production environment. We are recommending Single-tenant ScienceCloud as the core infrastructure for their enterprise-grade deployment.

To ensure the platform meets the rigorous standards required for a live Python production environment, I need to verify the following:

1. Standardized Python Environment Management: The client's services are built entirely on Python, requiring a stable and repeatable environment.

• In a Single-tenant setup, can we utilize Service Requests (SR) to establish a validated Python environment with specific library versions (e.g., LangChain, OpenAI SDK)?
• Since manual pip install is restricted for security, is it a standard procedure to have the ScienceCloud operations team configure and maintain these custom Python environments to ensure production stability?

2. Reliable Real-time API Integration (Python Outbound): Their Python-based agents must perform real-time data fetching via external REST APIs (e.g., PubMed).

• Is whitelisting outbound domains a standard production-grade procedure for Python communication in Single-tenant environments?
• Can we guarantee the uptime and low latency necessary for Python-driven real-time services when communicating through the Managed network?

3. Enterprise-scale Python AI Success Stories:

• Are there any proven success stories of Production-scale, Python-based AI Agent services currently running on Single-tenant ScienceCloud?
• We want to confirm that this "Managed Cloud" architecture is a proven fit for Python-intensive AI deployments that demand high security and operational standardization.

We aim to provide a secure and scalable foundation for a mission-critical Python AI service. Any insights on Production-level support would be greatly appreciated.