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Agent Skill

Azure Aigateway

azure-aigateway

Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, configure AI backend, token metrics, AI cost control, convert API to MCP, import OpenAPI to gateway.

MicrosoftAI/MLPythonAgent-skills

408K installs

microsoft/azure-skills

by Microsoft

Score

8.5

/ 10

Installs

408K

Repo Stars

1.2K

Last Updated

0d ago

Fresh

Quality Ratio

99%

Description

Verified

Language

Python

First Published

Feb 2026

Summary

The Azure Aigateway agent skill enables developers to configure Azure API Management as a robust AI Gateway, centralizing governance, security, and optimization for AI models, MCP tools, and agents. This agent skill is designed for architects and developers building AI-powered applications on Azure who require fine-grained control over AI consumption, cost, and safety. It is one of the most installed skills in the entire registry, reflecting its utility for managing AI integrations at scale. It provides concrete guidance for implementing critical policies such as `azure-openai-token-limit` for cost control and `llm-content-safety` for mitigating harmful content. The skill also facilitates adding various AI model backends, including Azure OpenAI, and establishing semantic caching for significant cost savings and improved performance. This agent skill is particularly relevant for environments deeply integrated with the Azure ecosystem, leveraging Azure API Management for AI workload orchestration.

Skill Definition

Configure Azure API Management (APIM) as an AI Gateway for governing AI models, MCP tools, and agents.

To deploy APIM, use the azure-prepare skill. See APIM deployment guide.

When to Use This Skill

CategoryTriggers
Model Governance"semantic caching", "token limits", "load balance AI", "track token usage"
Tool Governance"rate limit MCP", "protect my tools", "configure my tool", "convert API to MCP"
Agent Governance"content safety", "jailbreak detection", "filter harmful content"
Configuration"add Azure OpenAI backend", "configure my model", "add AI Foundry model"
Testing"test AI gateway", "call OpenAI through gateway"

Quick Reference

PolicyPurposeDetails
azure-openai-token-limitCost controlModel Policies
azure-openai-semantic-cache-lookup/store60-80% cost savingsModel Policies
azure-openai-emit-token-metricObservabilityModel Policies
llm-content-safetySafety & complianceAgent Policies
rate-limit-by-keyMCP/tool protectionTool Policies

Get Gateway Details

# Get gateway URL
az apim show --name <apim-name> --resource-group <rg> --query "gatewayUrl" -o tsv

# List backends (AI models)
az apim backend list --service-name <apim-name> --resource-group <rg> \
  --query " [].{id:name, url:url}" -o table

# Get subscription key
az apim subscription keys list \
  --service-name <apim-name> --resource-group <rg> --subscription-id <sub-id>

Test AI Endpoint

GATEWAY_URL=$(az apim show --name <apim-name> --resource-group <rg> --query "gatewayUrl" -o tsv)

curl -X POST "${GATEWAY_URL}/openai/deployments/<deployment>/chat/completions?api-version=2024-02-01" \
  -H "Content-Type: application/json" \
  -H "Ocp-Apim-Subscription-Key: <key>" \
  -d '{"messages": [{"role": "user", "content": "Hello"}], "max_tokens": 100}'

Common Tasks

Add AI Backend

See references/patterns.md for full steps.

# Discover AI resources
az cognitiveservices account list --query " [?kind=='OpenAI']" -o table

# Create backend
az apim backend create --service-name <apim> --resource-group <rg> \
  --backend-id openai-backend --protocol http --url "https://<aoai>.openai.azure.com/openai"

# Grant access (managed identity)
az role assignment create --assignee <apim-principal-id> \
  --role "Cognitive Services User" --scope <aoai-resource-id>

Apply AI Governance Policy

Recommended policy order in <inbound>:

  1. Authentication - Managed identity to backend
  2. Semantic Cache Lookup - Check cache before calling AI
  3. Token Limits - Cost control
  4. Content Safety - Filter harmful content
  5. Backend Selection - Load balancing
  6. Metrics - Token usage tracking

See references/policies.md for complete example.


Troubleshooting

IssueSolution
Token limit 429Increase tokens-per-minute or add load balancing
No cache hitsLower score-threshold to 0.7
Content false positivesIncrease category thresholds (5-6)
Backend auth 401Grant APIM "Cognitive Services User" role

See references/troubleshooting.md for details.


References

SDK Quick References

How to Use

Use in O-mega

Claude Code

npx skills add microsoft/azure-skills azure-aigateway
Azure Aigateway | Agent Skills | o-mega