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hosted-agents-v2-py skill

/skills/hosted-agents-v2-py

This skill helps you deploy and manage container-based hosted agents on Azure AI Projects using ImageBasedHostedAgentDefinition for customized workflows.

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---
name: hosted-agents-v2-py
description: |
  Build hosted agents using Azure AI Projects SDK with ImageBasedHostedAgentDefinition.
  Use when creating container-based agents that run custom code in Azure AI Foundry.
  Triggers: "ImageBasedHostedAgentDefinition", "hosted agent", "container agent", 
  "create_version", "ProtocolVersionRecord", "AgentProtocol.RESPONSES".
package: azure-ai-projects
---

# Azure AI Hosted Agents (Python)

Build container-based hosted agents using `ImageBasedHostedAgentDefinition` from the Azure AI Projects SDK.

## Installation

```bash
pip install azure-ai-projects>=2.0.0b3 azure-identity
```

**Minimum SDK Version:** `2.0.0b3` or later required for hosted agent support.

## Environment Variables

```bash
AZURE_AI_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
```

## Prerequisites

Before creating hosted agents:

1. **Container Image** - Build and push to Azure Container Registry (ACR)
2. **ACR Pull Permissions** - Grant your project's managed identity `AcrPull` role on the ACR
3. **Capability Host** - Account-level capability host with `enablePublicHostingEnvironment=true`
4. **SDK Version** - Ensure `azure-ai-projects>=2.0.0b3`

## Authentication

Always use `DefaultAzureCredential`:

```python
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient

credential = DefaultAzureCredential()
client = AIProjectClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=credential
)
```

## Core Workflow

### 1. Imports

```python
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import (
    ImageBasedHostedAgentDefinition,
    ProtocolVersionRecord,
    AgentProtocol,
)
```

### 2. Create Hosted Agent

```python
client = AIProjectClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential()
)

agent = client.agents.create_version(
    agent_name="my-hosted-agent",
    definition=ImageBasedHostedAgentDefinition(
        container_protocol_versions=[
            ProtocolVersionRecord(protocol=AgentProtocol.RESPONSES, version="v1")
        ],
        cpu="1",
        memory="2Gi",
        image="myregistry.azurecr.io/my-agent:latest",
        tools=[{"type": "code_interpreter"}],
        environment_variables={
            "AZURE_AI_PROJECT_ENDPOINT": os.environ["AZURE_AI_PROJECT_ENDPOINT"],
            "MODEL_NAME": "gpt-4o-mini"
        }
    )
)

print(f"Created agent: {agent.name} (version: {agent.version})")
```

### 3. List Agent Versions

```python
versions = client.agents.list_versions(agent_name="my-hosted-agent")
for version in versions:
    print(f"Version: {version.version}, State: {version.state}")
```

### 4. Delete Agent Version

```python
client.agents.delete_version(
    agent_name="my-hosted-agent",
    version=agent.version
)
```

## ImageBasedHostedAgentDefinition Parameters

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `container_protocol_versions` | `list[ProtocolVersionRecord]` | Yes | Protocol versions the agent supports |
| `image` | `str` | Yes | Full container image path (registry/image:tag) |
| `cpu` | `str` | No | CPU allocation (e.g., "1", "2") |
| `memory` | `str` | No | Memory allocation (e.g., "2Gi", "4Gi") |
| `tools` | `list[dict]` | No | Tools available to the agent |
| `environment_variables` | `dict[str, str]` | No | Environment variables for the container |

## Protocol Versions

The `container_protocol_versions` parameter specifies which protocols your agent supports:

```python
from azure.ai.projects.models import ProtocolVersionRecord, AgentProtocol

# RESPONSES protocol - standard agent responses
container_protocol_versions=[
    ProtocolVersionRecord(protocol=AgentProtocol.RESPONSES, version="v1")
]
```

**Available Protocols:**
| Protocol | Description |
|----------|-------------|
| `AgentProtocol.RESPONSES` | Standard response protocol for agent interactions |

## Resource Allocation

Specify CPU and memory for your container:

```python
definition=ImageBasedHostedAgentDefinition(
    container_protocol_versions=[...],
    image="myregistry.azurecr.io/my-agent:latest",
    cpu="2",      # 2 CPU cores
    memory="4Gi"  # 4 GiB memory
)
```

**Resource Limits:**
| Resource | Min | Max | Default |
|----------|-----|-----|---------|
| CPU | 0.5 | 4 | 1 |
| Memory | 1Gi | 8Gi | 2Gi |

## Tools Configuration

Add tools to your hosted agent:

### Code Interpreter

```python
tools=[{"type": "code_interpreter"}]
```

### MCP Tools

```python
tools=[
    {"type": "code_interpreter"},
    {
        "type": "mcp",
        "server_label": "my-mcp-server",
        "server_url": "https://my-mcp-server.example.com"
    }
]
```

### Multiple Tools

```python
tools=[
    {"type": "code_interpreter"},
    {"type": "file_search"},
    {
        "type": "mcp",
        "server_label": "custom-tool",
        "server_url": "https://custom-tool.example.com"
    }
]
```

## Environment Variables

Pass configuration to your container:

```python
environment_variables={
    "AZURE_AI_PROJECT_ENDPOINT": os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    "MODEL_NAME": "gpt-4o-mini",
    "LOG_LEVEL": "INFO",
    "CUSTOM_CONFIG": "value"
}
```

**Best Practice:** Never hardcode secrets. Use environment variables or Azure Key Vault.

## Complete Example

```python
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import (
    ImageBasedHostedAgentDefinition,
    ProtocolVersionRecord,
    AgentProtocol,
)

def create_hosted_agent():
    """Create a hosted agent with custom container image."""
    
    client = AIProjectClient(
        endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
        credential=DefaultAzureCredential()
    )
    
    agent = client.agents.create_version(
        agent_name="data-processor-agent",
        definition=ImageBasedHostedAgentDefinition(
            container_protocol_versions=[
                ProtocolVersionRecord(
                    protocol=AgentProtocol.RESPONSES,
                    version="v1"
                )
            ],
            image="myregistry.azurecr.io/data-processor:v1.0",
            cpu="2",
            memory="4Gi",
            tools=[
                {"type": "code_interpreter"},
                {"type": "file_search"}
            ],
            environment_variables={
                "AZURE_AI_PROJECT_ENDPOINT": os.environ["AZURE_AI_PROJECT_ENDPOINT"],
                "MODEL_NAME": "gpt-4o-mini",
                "MAX_RETRIES": "3"
            }
        )
    )
    
    print(f"Created hosted agent: {agent.name}")
    print(f"Version: {agent.version}")
    print(f"State: {agent.state}")
    
    return agent

if __name__ == "__main__":
    create_hosted_agent()
```

## Async Pattern

```python
import os
from azure.identity.aio import DefaultAzureCredential
from azure.ai.projects.aio import AIProjectClient
from azure.ai.projects.models import (
    ImageBasedHostedAgentDefinition,
    ProtocolVersionRecord,
    AgentProtocol,
)

async def create_hosted_agent_async():
    """Create a hosted agent asynchronously."""
    
    async with DefaultAzureCredential() as credential:
        async with AIProjectClient(
            endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
            credential=credential
        ) as client:
            agent = await client.agents.create_version(
                agent_name="async-agent",
                definition=ImageBasedHostedAgentDefinition(
                    container_protocol_versions=[
                        ProtocolVersionRecord(
                            protocol=AgentProtocol.RESPONSES,
                            version="v1"
                        )
                    ],
                    image="myregistry.azurecr.io/async-agent:latest",
                    cpu="1",
                    memory="2Gi"
                )
            )
            return agent
```

## Common Errors

| Error | Cause | Solution |
|-------|-------|----------|
| `ImagePullBackOff` | ACR pull permission denied | Grant `AcrPull` role to project's managed identity |
| `InvalidContainerImage` | Image not found | Verify image path and tag exist in ACR |
| `CapabilityHostNotFound` | No capability host configured | Create account-level capability host |
| `ProtocolVersionNotSupported` | Invalid protocol version | Use `AgentProtocol.RESPONSES` with version `"v1"` |

## Best Practices

1. **Version Your Images** - Use specific tags, not `latest` in production
2. **Minimal Resources** - Start with minimum CPU/memory, scale up as needed
3. **Environment Variables** - Use for all configuration, never hardcode
4. **Error Handling** - Wrap agent creation in try/except blocks
5. **Cleanup** - Delete unused agent versions to free resources

## Reference Links

- [Azure AI Projects SDK](https://pypi.org/project/azure-ai-projects/)
- [Hosted Agents Documentation](https://learn.microsoft.com/azure/ai-services/agents/how-to/hosted-agents)
- [Azure Container Registry](https://learn.microsoft.com/azure/container-registry/)

Overview

This skill shows how to build container-based hosted agents in Azure AI Projects using ImageBasedHostedAgentDefinition. It covers authentication, prerequisites, creating agent versions, resource sizing, tools, and common errors to streamline deploying custom code in Azure AI Foundry. Practical examples for sync and async Python usage are included.

How this skill works

The skill uses the Azure AI Projects SDK to register a container image as a hosted agent version via client.agents.create_version with ImageBasedHostedAgentDefinition. You specify container_protocol_versions (e.g., AgentProtocol.RESPONSES v1), image path, CPU/memory, tools, and environment variables. The client then manages versions, listing, and deletion through the API.

When to use it

  • Deploy an autonomous agent that runs custom code inside a container in Azure Foundry.
  • Run containerized workloads that need direct access to code interpreter or MCP tools.
  • Manage multiple agent versions with controlled resource allocations and environment variables.
  • Automate creation and cleanup of hosted agent versions in CI/CD pipelines.
  • Test asynchronous agent deployment patterns using the SDK’s async client.

Best practices

  • Use DefaultAzureCredential for authentication and avoid embedding secrets in code.
  • Version your container images (avoid latest) to ensure reproducible deployments.
  • Start with minimal CPU/memory, monitor performance, and scale resources as needed.
  • Grant your project’s managed identity AcrPull role on the ACR to prevent ImagePullBackOff.
  • Set configuration via environment variables or Azure Key Vault instead of hardcoding.

Example use cases

  • A data-processing agent packaged as a container that uses code_interpreter to transform datasets.
  • A document search agent combining file_search and code_interpreter tools for analysis workflows.
  • CI pipeline step that creates a temporary hosted agent version to run integration tests.
  • A microservice agent behind MCP integration for custom toolchains and external APIs.

FAQ

What SDK version do I need?

Use azure-ai-projects 2.0.0b3 or later for hosted agent support.

Why do I see ImagePullBackOff?

Usually the managed identity lacks AcrPull on the ACR or the image path/tag is invalid; grant AcrPull and verify the image exists.