foundry-agent-sync

oleh github

Buat dan sinkronkan agen AI berbasis prompt langsung di dalam Azure AI Foundry melalui REST API, dari manifes JSON lokal. Tidak seperti keterampilan perancah yang hanya…

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

Lebih banyak skill dari github

console-rendering
github
Instruksi untuk menggunakan sistem rendering konsol berbasis tag struct di Go
official
acquire-codebase-knowledge
github
Gunakan keterampilan ini ketika pengguna secara eksplisit meminta untuk memetakan, mendokumentasikan, atau mempelajari basis kode yang sudah ada. Aktifkan untuk perintah seperti "petakan basis kode ini", "dokumentasikan…
official
acreadiness-assess
github
Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc…
official
acreadiness-generate-instructions
github
Menghasilkan file instruksi agen AI yang disesuaikan melalui perintah instruksi AgentRC. Menghasilkan .github/copilot-instructions.md (default, direkomendasikan untuk Copilot di VS…
official
acreadiness-policy
github
Bantu pengguna memilih, menulis, atau menerapkan kebijakan AgentRC. Kebijakan menyesuaikan penilaian kesiapan dengan menonaktifkan pemeriksaan yang tidak relevan, mengganti dampak/tingkat, mengatur…
official
add-educational-comments
github
Tambahkan komentar edukatif ke file kode untuk mengubahnya menjadi sumber belajar yang efektif. Menyesuaikan kedalaman penjelasan dan nada dengan tiga tingkat pengetahuan yang dapat dikonfigurasi: pemula, menengah, dan mahir. Secara otomatis meminta file jika tidak ada yang disediakan, dengan pencocokan daftar bernomor untuk pemilihan cepat. Memperluas file hingga 125% hanya menggunakan komentar edukatif (batas keras: 400 baris baru; 300 untuk file di atas 1.000 baris). Mempertahankan encoding file, gaya indentasi, kebenaran sintaks, dan...
official
adobe-illustrator-scripting
github
Menulis, men-debug, dan mengoptimalkan skrip otomatisasi Adobe Illustrator menggunakan ExtendScript (JavaScript/JSX). Gunakan saat membuat atau memodifikasi skrip yang memanipulasi…
official
agent-governance
github
Kebijakan deklaratif, klasifikasi intensi, dan jejak audit untuk mengontrol akses dan perilaku alat agen AI. Kebijakan tata kelola yang dapat dikomposisikan mendefinisikan alat yang diizinkan/diblokir, filter konten, batas kecepatan, dan persyaratan persetujuan — disimpan sebagai konfigurasi, bukan kode. Klasifikasi intensi semantik mendeteksi perintah berbahaya (eksfiltrasi data, eskalasi hak istimewa, injeksi perintah) sebelum eksekusi alat menggunakan sinyal berbasis pola. Dekorator tata kelola tingkat alat memberlakukan kebijakan pada fungsi...
official