azure-openai

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

1. 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)
2. Use managed identity when possible — Azure Function with managed identity to AOAI is the most secure pattern
3. AI Builder for simple, Foundry for complex — AI Builder prompts are faster to build; direct AOAI is more flexible
4. Monitor token consumption — set up alerts on Azure OpenAI usage to prevent unexpected costs
5. 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 sources
• power-automate — Flow integration with AI connectors
• code-apps — React UI patterns for AI features
• licensing — AI Builder credits, Copilot Credits, AOAI consumption costs
• integration-patterns — Azure Functions for server-side AI