By name: microsoft-foundry description: Expert knowledge for Microsoft Foundry (aka Azure AI Foundry) development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building Foundry agents with Entra auth, VNet isolation, model routing, Azure OpenAI setup, or MCP/OpenAPI tools, and other Microsoft Foundry related development tasks. Not for Microsoft Foundry Classic (use microsoft-foundry-classic), Microsoft Foundry Local (use microsoft-foundry-local), Microsoft Foundry Tools (use microsoft-foundry-tools). compatibility: Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation. metadata: generated_at: "2026-06-07" generator: "docs2skills/1.0.0"
Microsoft Foundry Skill
This skill provides expert guidance for Microsoft Foundry. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
How to Use This Skill
IMPORTANT for Agent: Use the Category Index below to locate relevant sections. For categories with line ranges (e.g.,
L35-L120), useread_filewith the specified lines. For categories with file links (e.g.,[security.md](security.md)), useread_fileon the linked reference file
IMPORTANT for Agent: If
metadata.generated_atis more than 3 months old, suggest the user pull the latest version from the repository. Ifmcp_microsoftdocstools are not available, suggest the user install it: Installation Guide
This skill requires network access to fetch documentation content:
- Preferred: Use
mcp_microsoftdocs:microsoft_docs_fetchwith query stringfrom=learn-agent-skill. Returns Markdown. - Fallback: Use
fetch_webpagewith query stringfrom=learn-agent-skill&accept=text/markdown. Returns Markdown.
Category Index
| Category | Lines | Description |
|---|---|---|
| Troubleshooting | L37-L45 | Diagnosing and fixing Foundry issues: partner/community models, agent service recovery, eval/observability errors, Azure OpenAI webhooks, and known bugs/workarounds. |
| Best Practices | L46-L59 | Best practices for prompts, tools, safety messages, routing, evaluation, and fine-tuning so you can design, operate, and measure high-quality Azure/Foundry AI agents in production |
| Decision Making | L60-L93 | Guidance for choosing Foundry deployment, regions, models, costs, and lifecycle policies, plus migration paths from legacy/OpenAI/GitHub setups and planning DR, PTU, and eval-based model selection |
| Architecture & Design Patterns | L94-L101 | Designing Foundry agent architectures: VNet/subnet sizing, isolated resource layouts, high availability patterns, and model router patterns for routing and scaling AI workloads. |
| Limits & Quotas | L102-L121 | Quotas, limits, and regional support for Foundry agents and models, including sessions, vector/file search, evals, PTU sizing, cost safeguards, and Azure OpenAI deployment/usage caps |
| Security | L122-L159 | Security, governance, and compliance for Foundry: auth (Entra, keyless), RBAC, agent identities, private networking, guardrails, data privacy, Azure Policy, and content safety controls. |
| Configuration | L160-L220 | Configuring and operating Foundry agents and models: endpoints, tools, skills, memory, evaluations, monitoring, Azure OpenAI/Fireworks setup, security/guardrails, networking, and resource integration. |
| Integrations & Coding Patterns | L221-L294 | Patterns and APIs for integrating Foundry agents/models with tools, MCP/OpenAPI services, LangChain/LangGraph, Azure/M365 data, observability (OTel), fine-tuning, audio/Realtime, and Responses/REST. |
| Deployment | L295-L310 | Deploying and running Foundry agents/models: hosted and containerized deployments, portal/CLI flows, evaluations (cloud/CI), red teaming, voice agents, outages, and open‑source/AOAI models. |
Troubleshooting
| Topic | URL |
|---|---|
| Use partner and community models in Foundry | https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-from-partners |
| Recover Foundry Agent Service from resource and data loss | https://learn.microsoft.com/en-us/azure/foundry/how-to/agent-service-operator-disaster-recovery |
| Troubleshoot Foundry evaluation and observability issues | https://learn.microsoft.com/en-us/azure/foundry/observability/how-to/troubleshooting |
| Set up and troubleshoot Azure OpenAI webhooks | https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/webhooks |
| Known issues and workarounds for Microsoft Foundry services | https://learn.microsoft.com/en-us/azure/foundry/reference/foundry-known-issues |
Best Practices
Decision Making
Architecture & Design Patterns
| Topic | URL |
|---|---|
| Design networking and subnet sizing for Foundry agents | https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/agents-networking-deep-dive |
| Plan standard agent setup with isolated resources | https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/standard-agent-setup |
| Design high availability for Microsoft Foundry agents | https://learn.microsoft.com/en-us/azure/foundry/how-to/high-availability-resiliency |
| Apply model router patterns with Foundry agents | https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/model-router-agents |