FO

foundry-agent-sync

Directly deploy and synchronize prompt-based agents to Azure AI Foundry.

Install

mkdir -p .claude/skills/foundry-agent-sync && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/16995" && unzip -o skill.zip -d .claude/skills/foundry-agent-sync && rm skill.zip

Installs to .claude/skills/foundry-agent-sync

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Create and synchronize prompt-based AI agents directly within Azure AI Foundry via REST API, from a local JSON manifest. Unlike scaffolding skills that only generate local code, this skill registers agents in the Foundry service itself — making them immediately available for invocation. Use when the user asks to create agents in Foundry, sync, deploy, register, or push agents to Foundry, update agent instructions, or scaffold the manifest and sync script for a new repository. Triggers: 'create agent in foundry', 'sync foundry agents', 'deploy agents to foundry', 'register agents in foundry', 'push agents', 'create foundry agent manifest', 'scaffold agent sync'.
669 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Intermediate

Key capabilities

  • Create prompt-based AI agents in Azure AI Foundry
  • Synchronize existing agents in Foundry
  • Update agent instructions in Foundry
  • Scaffold a JSON manifest for agent definitions
  • Deploy agents to the Foundry service

How it works

The skill registers or updates agents in Azure AI Foundry by making named POST calls to the Agent Service REST API, using definitions from a local JSON manifest file.

Inputs & outputs

You give it
A local JSON manifest file defining agents, Foundry project endpoint, Subscription ID, Model deployment name, Agent name prefix
You get back
Agents created or updated in Azure AI Foundry, confirmation of synced agents

When to use foundry-agent-sync

  • Sync agents to Foundry
  • Register AI agents
  • Deploy agents to cloud

About this skill

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

When not to use it

  • When scaffolding local agent code
  • When scaffolding container images
  • When the user does not have an Azure AI Foundry project

Prerequisites

An Azure AI Foundry project with a deployed modelAzure CLI (`az`) authenticated with access to the Foundry projectThe Azure AI User role (or higher) on the Foundry project resource

Limitations

  • It does not scaffold local agent code
  • It does not scaffold container images
  • Requires specific Azure roles and deployed models

How it compares

This skill directly registers agents in the cloud service, making them immediately available, unlike scaffolding skills that only generate local code.

Compared to similar skills

foundry-agent-sync side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
foundry-agent-sync (this skill)02moNo flagsIntermediate
azure-functions105moReviewIntermediate
azure-deployment-preflight77moReviewAdvanced
microsoft-skill-creator65moReviewAdvanced

Try saying

Example prompts that trigger this skill in your AI assistant.

You might also like

azure-functions

aj-geddes

Create serverless functions on Azure with triggers, bindings, authentication, and monitoring. Use for event-driven computing without managing infrastructure.

10104

azure-deployment-preflight

github

Performs comprehensive preflight validation of Bicep deployments to Azure, including template syntax validation, what-if analysis, and permission checks. Use this skill before any deployment to Azure to preview changes, identify potential issues, and ensure the deployment will succeed. Activate when users mention deploying to Azure, validating Bicep files, checking deployment permissions, previewing infrastructure changes, running what-if, or preparing for azd provision.

746

microsoft-skill-creator

MicrosoftDocs

Create agent skills for Microsoft technologies using Learn MCP tools. Use when users want to create a skill that teaches agents about any Microsoft technology, library, framework, or service (Azure, .NET, M365, VS Code, Bicep, etc.). Investigates topics deeply, then generates a hybrid skill storing essential knowledge locally while enabling dynamic deeper investigation.

626

agent-framework-azure-ai-py

microsoft

Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.

14

azure-resource-manager-mysql-dotnet

microsoft

Azure MySQL Flexible Server SDK for .NET. Database management for MySQL Flexible Server deployments. Use for creating servers, databases, firewall rules, configurations, backups, and high availability. Triggers: "MySQL", "MySqlFlexibleServer", "MySQL Flexible Server", "Azure Database for MySQL", "MySQL database management", "MySQL firewall", "MySQL backup".

14

skypilot-multi-cloud-orchestration

davila7

Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.

10

Search skills

Search the agent skills registry