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 Azure OpenAI, model router patterns, MCP tools, private networking, or eval workflows, 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: "2026-05-31" 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-L60 | 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 | L61-L97 | Guidance for choosing models, deployments, costs, migrations, and integration patterns in Foundry, including lifecycle, retirement, DR, and upgrading from legacy or other platforms. |
| Architecture & Design Patterns | L98-L105 | 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 | L106-L125 | Quotas, rate limits, regions, and cost controls for Foundry agents and models, including sessions, function calls, evals, vector/file search, batch jobs, prompt caching, and Azure OpenAI limits. |
| Security | L126-L161 | Securing and governing Foundry: auth (Entra, keyless, MCP, Agent2Agent), RBAC and policies, private networking/isolation, guardrails and content safety, data privacy, and compliance integrations. |
| Configuration | L162-L214 | Configuring Foundry agents, models, and Azure OpenAI: endpoints, tools, skills, memory, security/guardrails, monitoring, tracing, BYO resources, and evaluation workflows. |
| Integrations & Coding Patterns | L215-L273 | Patterns and code for integrating Foundry agents and models with tools, APIs, LangChain/LangGraph, MCP, Azure/M365 services, fine-tuning, tracing, audio/Realtime, and Responses/REST APIs. |
| Deployment | L274-L287 | Deploying and operating Foundry agents/models: hosting from code or containers, portal/CLI deployments, migrations, outages, evaluations, red teaming, and custom/fine-tuned model imports. |
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 |