Adjust name from AI Studio to: Microsoft Foundry

[lukemurraynz]

This pull request updates references to "Azure AI Foundry" across the documentation and infrastructure files, standardizing the terminology to "Microsoft Foundry" for consistency. These changes help clarify the architecture and services used in the AI Landing Zone, making the documentation and codebase easier to understand and maintain.

Documentation terminology updates:

• Updated references from "Azure AI Foundry" to "Microsoft Foundry" in the main README.md use cases section to reflect the new naming convention.
• Changed the architecture description in bicep/README.md to use "Microsoft Foundry" instead of "Azure AI Foundry".
• Updated the cost guide section header in docs/AI-Landing-Zones-Cost-Guide.md to reference "Microsoft Foundry / AI Services".

Infrastructure and component documentation updates:

• Modified the description in bicep/infra/components/bing-search/main.bicep to reference "Microsoft Foundry" for the Bing Grounding account and Cognitive Services connection.
• Updated the component documentation in bicep/infra/components/bing-search/readme.md to use "Microsoft Foundry" in place of "Azure AI Foundry".

[Copilot]

Pull request overview

This pull request updates terminology across documentation and infrastructure files to standardize the naming from "Azure AI Foundry" to "Microsoft Foundry". The changes improve consistency in how the AI services platform is referenced throughout the AI Landing Zone repository.

Key changes:

• Updated user-facing documentation to use "Microsoft Foundry" terminology
• Modified infrastructure component descriptions and documentation
• Updated cost guide references to align with new naming convention

Reviewed changes

Copilot reviewed 7 out of 7 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
README.md	Updated use case section to reference "Microsoft Foundry" for chat and agent scenarios
bicep/README.md	Changed architecture description to reference "Microsoft Foundry" as the core platform
bicep/infra/components/bing-search/main.bicep	Updated module description metadata to reference "Microsoft Foundry"
bicep/infra/components/bing-search/readme.md	Updated component documentation to reference "Microsoft Foundry"
docs/AI-Landing-Zones-Cost-Guide.md	Updated cost section header to reference "Microsoft Foundry / AI Services"
docs/AI-Landing-Zones-Design-Checklist.md	Updated multiple design recommendation entries to reference "Microsoft Foundry" across compute, cost, governance, identity, monitoring, reliability, and networking sections
terraform/readme.md	Updated deployment guide sections, next steps, and additional resources to reference "Microsoft Foundry"

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[Copilot]

Inconsistent terminology: While the main description has been updated to "Microsoft Foundry", the sentence still references "AI Foundry Agent service". For consistency with the PR's naming standardization, this should be changed to "Microsoft Foundry Agent service".

Suggested change

This architecture delivers a full **AI Landing Zone** built around **Microsoft Foundry**. The **AI Foundry Agent service**, together with **AI Search, Cosmos DB, Storage, and Key Vault**, operates securely and seamlessly. A dedicated **Azure Container Apps** environment enables custom **GenAI applications**, and supporting services cover configuration, data, and observability. Thanks to its modular design, you can deploy everything or only the components you need.

This architecture delivers a full **AI Landing Zone** built around **Microsoft Foundry**. The **Microsoft Foundry Agent service**, together with **AI Search, Cosmos DB, Storage, and Key Vault**, operates securely and seamlessly. A dedicated **Azure Container Apps** environment enables custom **GenAI applications**, and supporting services cover configuration, data, and observability. Thanks to its modular design, you can deploy everything or only the components you need.

[Copilot]

Inconsistent terminology: While the beginning and end of this row have been updated to "Microsoft Foundry", the middle section still contains "AI Foundry Resource" (should be "Microsoft Foundry Resource") and "AI Foundry resoruces" twice (should be "Microsoft Foundry resoruces"). This inconsistency undermines the PR's goal of standardizing the terminology.

Note: There's also a spelling error "resoruces" (should be "resources"), but this appears to be a pre-existing issue not introduced in this PR.

Suggested change

| R-R4 | Resource Organization | Consider scaling through multi-account and multi-project deployment.| Azure offers tools like Microsoft Foundry [Resource and projects](https://learn.microsoft.com/en-us/azure/ai-studio/concepts/ai-resources) to enforce governance and security. Use an AI Foundry Resource per billing boundary to allocate costs across different teams. For more information, see [Manage AI deployments](https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/ai/manage#manage-ai-deployment). Use distinct AI Foundry resoruces to organize and manage AI artifacts like datasets, models, and experiments. AI Foundry resoruces centralize resource management and simplify access control. For example, use [projects](https://learn.microsoft.com/en-us/azure/ai-studio/concepts/ai-resources#organize-work-in-projects-for-customization) within Microsoft Foundry to manage resources and permissions efficiently, facilitating collaboration while maintaining security boundaries. ||

| R-R4 | Resource Organization | Consider scaling through multi-account and multi-project deployment.| Azure offers tools like Microsoft Foundry [Resource and projects](https://learn.microsoft.com/en-us/azure/ai-studio/concepts/ai-resources) to enforce governance and security. Use a Microsoft Foundry Resource per billing boundary to allocate costs across different teams. For more information, see [Manage AI deployments](https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/ai/manage#manage-ai-deployment). Use distinct Microsoft Foundry resources to organize and manage AI artifacts like datasets, models, and experiments. Microsoft Foundry resources centralize resource management and simplify access control. For example, use [projects](https://learn.microsoft.com/en-us/azure/ai-studio/concepts/ai-resources#organize-work-in-projects-for-customization) within Microsoft Foundry to manage resources and permissions efficiently, facilitating collaboration while maintaining security boundaries. ||