| name | azure-openai |
| description | Azure OpenAI and AI integration with Power Platform. Use when: Azure OpenAI, AOAI, AI Foundry, GPT, LLM, AI integration, BYOM, bring your own model, AI Builder prompts, structured output, AI architecture decisions, Copilot Studio custom models. |
Skill: Azure OpenAI Integration
When to Use
Trigger when integrating Azure OpenAI (or AI Foundry models) with Power Platform — from Power Automate flows, Code Apps, Copilot Studio agents, or when making architecture decisions about which AI approach to use.
Integration Patterns
| Pattern | Description | Best For |
|---|
| AI Builder prompts | Low-code GPT prompts in flows/apps | Simple text generation, classification, extraction |
| Azure AI Foundry connector | Power Automate connector to AOAI | Flow-based AI with premium connector |
| HTTP connector to AOAI | Direct REST calls to AOAI endpoint | Full control, streaming, advanced parameters |
| Custom API + Azure Function | Server-side AI calls exposed via Custom API | Code Apps, secure key management, streaming |
| Copilot Studio BYOM | Custom model for agent responses/prompts | Domain-specific agent behavior |
| Copilot Studio generative actions | AI selects actions dynamically | Context-dependent automation |
⚠️ REQUIRED: Load Sub-Files Before Implementation
SKILL.md is a summary only — it is NOT sufficient for implementation.
The detailed content (complete payloads, XML templates, working examples, edge-case handling) lives in sub-files in the same directory as this SKILL.md. Before writing any code, you MUST use read_file on the sub-files relevant to your task:
- Power Automate + AI — AI Foundry connector in flows, HTTP connector to AOAI endpoints, prompt patterns, retry/fallback, structured output parsing, token limit management
- Code Apps + AI — AOAI from Code Apps via Custom API or Azure Function (never client-side keys), streaming responses, chat UI patterns, React hooks for AI calls
- Copilot Studio BYOM — Bring Your Own Model for prompts and response generation, AI Foundry model configuration, testing, monitoring token usage
- AI Architecture Decisions — decision tree: Copilot Studio agent vs Azure AI Foundry agent vs Power Automate Agent Flow vs AI Builder vs direct AOAI, licensing implications, data residency
Key Principles
- Never expose API keys client-side — Code Apps and Canvas Apps must call AOAI via a server-side intermediary (Custom API, Azure Function, or Power Automate flow)
- Use managed identity when possible — Azure Function with managed identity to AOAI is the most secure pattern
- AI Builder for simple, Foundry for complex — AI Builder prompts are faster to build; direct AOAI is more flexible
- Monitor token consumption — set up alerts on Azure OpenAI usage to prevent unexpected costs
- Structured output — use JSON mode or function calling for parseable responses, not free-form text
Anti-Patterns
- Exposing AOAI API keys in client-side code (Canvas/Code Apps)
- Using GPT for deterministic tasks that don't need AI (regex, simple lookups)
- No retry logic on AI calls (AOAI returns 429 under load)
- No token limit management (prompt + response exceeding model context window)
- Free-form text output when structured data is needed (use JSON mode)
- Using AI Builder for everything (limited control, limited models)
- No cost monitoring on AOAI deployment (token costs can spike unexpectedly)
- Calling AOAI from every flow run without caching (expensive for repetitive queries)
- Not testing with representative data (AI behavior varies with real-world input)
Related Skills
copilot-studio — Agent building, generative actions, knowledge sourcespower-automate — Flow integration with AI connectorscode-apps — React UI patterns for AI featureslicensing — AI Builder credits, Copilot Credits, AOAI consumption costsintegration-patterns — Azure Functions for server-side AI
