foundry-agent-sync

작성자: github

Azure AI Foundry 내에서 REST API를 통해 로컬 JSON 매니페스트로부터 프롬프트 기반 AI 에이전트를 직접 생성하고 동기화합니다. 스캐폴딩 스킬과 달리 단순히...

npx skills add https://github.com/github/awesome-copilot --skill foundry-agent-sync

Foundry Agent Sync

Overview

Create and synchronize prompt-based AI agents directly within Azure AI Foundry via the Agent Service REST API. This skill registers agents in the Foundry service itself — making them immediately available for invocation, evaluation, and management through the Foundry portal or API. Each agent is created or updated idempotently via a named POST call, using definitions from a local JSON manifest file.

Key distinction: This skill creates agents inside AI Foundry (server-side). It does not scaffold local agent code or container images — for that, use the microsoft-foundry skill's create sub-skill.

Prerequisites

The user must have:

  1. An Azure AI Foundry project with a deployed model (e.g. gpt-5-4)
  2. Azure CLI (az) authenticated with access to the Foundry project
  3. The Azure AI User role (or higher) on the Foundry project resource

Collect these values before proceeding:

ValueHow to get it
Foundry project endpointAzure Portal → AI Foundry project → Overview → Endpoint, or az resource show
Subscription IDaz account show --query id -o tsv
Model deployment nameThe model name deployed in the Foundry project (e.g. gpt-5-4)

Manifest Format

The manifest is a JSON array where each entry defines one agent. Look for it at common paths: infra/foundry-agents.json, foundry-agents.json, or .foundry/agents.json. If none exists, scaffold one.

[
  {
    "useCaseId": "alert-triage",
    "description": "Short description of what this agent does.",
    "baseInstruction": "You are an assistant that... <system prompt for the agent>"
  }
]

Field Reference

FieldRequiredDescription
useCaseIdYesKebab-case identifier; used to build the agent name ({prefix}-{useCaseId})
descriptionYesHuman-readable description stored as agent metadata
baseInstructionYesSystem prompt / base instructions for the agent

Sync Script

PowerShell (interactive / CI)

Create or locate the sync script. The canonical path is infra/scripts/sync-foundry-agents.ps1 but adapt to the repo layout.

param(
  [Parameter(Mandatory)]
  [string]$SubscriptionId,

  [Parameter(Mandatory)]
  [string]$ProjectEndpoint,

  [string]$ManifestPath = (Join-Path $PSScriptRoot '..\foundry-agents.json'),
  [string]$ModelName = 'gpt-5-4',
  [string]$AgentNamePrefix = 'myproject',
  [string]$ApiVersion = '2025-11-15-preview'
)

$ErrorActionPreference = 'Stop'

# Optional: append a common instruction suffix to every agent
$commonSuffix = ''

az account set --subscription $SubscriptionId | Out-Null
$accessToken = az account get-access-token --resource https://ai.azure.com/ --query accessToken -o tsv
if (-not $accessToken) { throw 'Failed to acquire Foundry access token.' }

$definitions = Get-Content -Raw -Path $ManifestPath | ConvertFrom-Json
$headers = @{ Authorization = "Bearer $accessToken" }
$results = @()

foreach ($def in $definitions) {
  $agentName = "$AgentNamePrefix-$($def.useCaseId)"
  $instructions = if ($commonSuffix) { "$($def.baseInstruction)`n`n$commonSuffix" } else { $def.baseInstruction }
  $body = @{
    definition  = @{ kind = 'prompt'; model = $ModelName; instructions = $instructions }
    description = $def.description
    metadata    = @{ useCaseId = $def.useCaseId; managedBy = 'foundry-agent-sync' }
  } | ConvertTo-Json -Depth 8

  $uri = "$($ProjectEndpoint.TrimEnd('/'))/agents/$agentName`?api-version=$ApiVersion"
  $resp = Invoke-RestMethod -Method Post -Uri $uri -Headers $headers -ContentType 'application/json' -Body $body
  $version = $resp.version ?? $resp.latest_version ?? $resp.id ?? 'unknown'
  Write-Host "Synced $agentName ($version)"
  $results += [pscustomobject]@{ name = $agentName; version = $version }
}

$results | Format-Table -AutoSize

Bash (Bicep deployment script / CI)

For automated deployment via Microsoft.Resources/deploymentScripts, use a bash script that:

  1. Authenticates with a managed identity: az login --identity --username "$CLIENT_ID"
  2. Acquires a Foundry token: az account get-access-token --resource https://ai.azure.com/
  3. Iterates definitions from the FOUNDRY_AGENT_DEFINITIONS environment variable (JSON string)
  4. POSTs each agent to {endpoint}/agents/{name}?api-version=2025-11-15-preview

Bicep Integration (optional)

To run the sync automatically during infrastructure deployment:

  1. Load the manifest at compile time:

    var agentDefinitions = loadJsonContent('foundry-agents.json')
    
  2. Create a User-Assigned Managed Identity with the Azure AI User role on the Foundry project.

  3. Create a Microsoft.Resources/deploymentScripts resource (kind AzureCLI) that:

    • Uses the managed identity
    • Loads the bash sync script via loadTextContent
    • Passes the project endpoint, definitions, and model as environment variables

Gate behind a deployFoundryAgents parameter so teams can opt in/out.

Workflow

Step 1 — Locate or scaffold the manifest

Search the repo for foundry-agents.json. If it doesn't exist, ask the user what agents they need and create the manifest.

Step 2 — Locate or scaffold the sync script

Search for sync-foundry-agents.ps1 or foundry-agent-sync.sh. If missing, create the PowerShell script using the template above, adapting:

  • $AgentNamePrefix to match the project name
  • $ModelName to the user's deployed model
  • $ManifestPath to the actual manifest location

Step 3 — Collect parameters

Ask the user for:

  • Foundry project endpoint
  • Subscription ID
  • Model deployment name (default: gpt-5-4)
  • Agent name prefix (default: repo name in kebab-case)

Step 4 — Run the sync

Execute the PowerShell script with the collected parameters:

.\infra\scripts\sync-foundry-agents.ps1 `
  -SubscriptionId '<sub-id>' `
  -ProjectEndpoint '<endpoint>' `
  -ModelName '<model>' `
  -AgentNamePrefix '<prefix>'

Step 5 — Verify

Confirm synced agents by listing them:

$token = az account get-access-token --resource https://ai.azure.com/ --query accessToken -o tsv
$endpoint = '<project-endpoint>'
Invoke-RestMethod -Uri "$endpoint/agents?api-version=2025-11-15-preview" `
  -Headers @{ Authorization = "Bearer $token" }

REST API Reference

OperationMethodURL
Create/update agentPOST{projectEndpoint}/agents/{agentName}?api-version=2025-11-15-preview
List agentsGET{projectEndpoint}/agents?api-version=2025-11-15-preview
Get agentGET{projectEndpoint}/agents/{agentName}?api-version=2025-11-15-preview
Delete agentDELETE{projectEndpoint}/agents/{agentName}?api-version=2025-11-15-preview

Create/Update Payload

{
  "definition": {
    "kind": "prompt",
    "model": "<deployed-model-name>",
    "instructions": "<system prompt>"
  },
  "description": "<agent description>",
  "metadata": {
    "useCaseId": "<use-case-id>",
    "managedBy": "foundry-agent-sync"
  }
}

Troubleshooting

SymptomCauseFix
401 UnauthorizedToken expired or wrong audienceRe-run az account get-access-token --resource https://ai.azure.com/
403 ForbiddenMissing Azure AI User roleAssign the role on the Foundry project scope
404 Not FoundWrong project endpointVerify endpoint includes /api/projects/{projectName}
Model not foundModel not deployed in projectDeploy the model in AI Foundry portal first
Empty definitionsManifest path wrongCheck -ManifestPath points to the JSON file

github의 다른 스킬

console-rendering
github
Go에서 struct 태그 기반 콘솔 렌더링 시스템 사용 지침
official
acquire-codebase-knowledge
github
사용자가 기존 코드베이스에 대한 매핑, 문서화, 또는 온보딩을 명시적으로 요청할 때 이 스킬을 사용하세요. "이 코드베이스를 매핑해줘", "문서화해줘"와 같은 프롬프트에서 트리거됩니다.
official
acreadiness-assess
github
현재 리포
official
acreadiness-generate-instructions
github
AgentRC 명령어를 통해 맞춤형 AI 에이전트 지침 파일을 생성합니다. .github/copilot-instructions.md 파일을 생성합니다(기본값, VS Code의 Copilot에 권장됨).
official
acreadiness-policy
github
사용자가 AgentRC 정책을 선택, 작성 또는 적용할 수 있도록 지원합니다. 정책은 관련 없는 검사를 비활성화하고, 영향/수준을 재정의하며, 설정을 통해 준비 상태 점수를 사용자 지정합니다.
official
add-educational-comments
github
코드 파일에 교육용 주석을 추가하여 효과적인 학습 자료로 변환합니다. 설명의 깊이와 어조를 세 가지 설정 가능한 지식 수준(초급, 중급, 고급)에 맞게 조정합니다. 파일이 제공되지 않으면 자동으로 요청하며, 빠른 선택을 위해 번호 목록 매칭을 제공합니다. 교육용 주석만을 사용하여 파일을 최대 125%까지 확장합니다(엄격한 제한: 새 줄 400개, 1,000줄 초과 파일의 경우 300개). 파일 인코딩, 들여쓰기 스타일, 구문 정확성 등을 유지합니다.
official
adobe-illustrator-scripting
github
Adobe Illustrator 자동화 스크립트를 ExtendScript(JavaScript/JSX)로 작성, 디버깅 및 최적화합니다. 스크립트를 생성하거나 수정하여 조작할 때 사용합니다.
official
agent-governance
github
선언적 정책, 의도 분류, AI 에이전트 도구 접근 및 행동 제어를 위한 감사 추적. 구성 가능한 거버넌스 정책은 허용/차단된 도구, 콘텐츠 필터, 속도 제한, 승인 요구 사항을 정의하며, 코드가 아닌 구성으로 저장됨. 의미론적 의도 분류는 패턴 기반 신호를 사용하여 도구 실행 전에 위험한 프롬프트(데이터 유출, 권한 상승, 프롬프트 인젝션)를 탐지함. 도구 수준 거버넌스 데코레이터는 함수에서 정책을 적용함...
official