[Feature] Dev-Test-Prod ALM guidance for MCS+AI

### What is it?
Successful users of this module will need a clear pattern for how to manage the infrastructure and configuration of MCS+AI across dev, test, and production environments.

This issue tracks the definition and documentation of a practical, scalable ALM (Application Lifecycle Management) approach that fits this architecture.

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### User Story
As a user, I want guidance and examples of how to manage MCS+AI infrastructure and app config across dev, test, and prod, so that I can implement a consistent ALM flow that fits the architecture.

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### Definition of Done
- A complete ALM flow is defined, including:
- Infra separation strategies (e.g., workspaces, environments, variables)
- Deployment flow from dev → test → prod
- Documentation is added to explain the pattern and how users can adopt it

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### Subtasks
- [ ] #23 Define ALM flow and environment strategy for MCS+AI
- [ ] #24 Document per-environment configuration setup and flow

No files or tests are named; start by inspecting the repository's Terraform/AZD template and the MCS+AI architecture described by the project. Define the dev-to-test-to-prod infrastructure and configuration strategy, then document adoption guidance and examples covering environment separation and deployment flow.

Written by the indexing model from the issue text.