agents-v2-py – Agents & Automation skill for AI agents

Compatibility and maintenance

Compatibility alias of hosted-agents-v2-py; use that ID for new references when no existing contract requires this one. The instruction-only procedure remains local; no executable support files are bundled so existing installations

continue to work offline. This compatibility guide documents the hosted-agent procedure, not an additional capability; older copies may use retired preview names.

Preserve the callable ID when an existing manifest or client configuration uses it.

Modified in AAS on 2026-09-05; original metadata and license notices are retained.

Azure AI Hosted Agents (Python)

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

Installation

pip install 'azure-ai-projects==2.3.0' azure-identity

These are SDK 2.3.0 instruction fragments aligned with the current Microsoft hosted-agent guide. Validate the installed schema and service support before provisioning; documentation inspection is not a cloud integration test.

Environment Variables

export 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 the project identity the registry role supported by its permission mode; current hosted-agent guidance uses Container Registry Repository Reader

3. Capability Host - Confirm current project/region hosted-agent availability and the application's network policy; do not enable public hosting automatically

4. SDK Version - Ensure azure-ai-projects==2.3.0

Authentication

Use the approved Azure credential flow for the intended tenant/subscription; this sketch uses DefaultAzureCredential:

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

import os

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

Core Workflow

1. Imports

import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import (
    HostedAgentDefinition,
    ContainerConfiguration,
    ProtocolVersionRecord,
    AgentEndpointProtocol,
)

2. Create Hosted Agent

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

agent = client.agents.create_version(
    agent_name="my-hosted-agent",
    definition=HostedAgentDefinition(
        protocol_versions=[
            ProtocolVersionRecord(protocol=AgentEndpointProtocol.RESPONSES, version="2.0.0")
        ],
        cpu="1",
        memory="2Gi",
        container_configuration=ContainerConfiguration(image=os.environ["REVIEWED_CONTAINER_IMAGE"]),
        environment_variables={
            "AZURE_AI_PROJECT_ENDPOINT": os.environ["AZURE_AI_PROJECT_ENDPOINT"],
            "MODEL_NAME": os.environ["MODEL_DEPLOYMENT_NAME"]
        }
    )
)

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

3. List Agent Versions

versions = client.agents.list_versions(agent_name="my-hosted-agent")
for version in versions:
    print(f"Version: {version.version}")  # Inspect supported status fields via get_version

Inspect the exact returned model for supported fields; a created version is not proof a running container is healthy. Close synchronous client/credential resources through their supported context managers or close() methods.

4. Delete Agent Version

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

HostedAgentDefinition Parameters

| Parameter | Type | Required | Description |

|-----------|------|----------|-------------|

| protocol_versions | list[ProtocolVersionRecord] | Yes | Protocol versions the agent supports |

| container_configuration | ContainerConfiguration | Yes for an image deployment | Reviewed registry image reference |

| cpu | str | No | CPU allocation (e.g., "1", "2") |

| memory | str | No | Memory allocation (e.g., "2Gi", "4Gi") |

| environment_variables | dict[str, str] | No | Environment variables for the container |

Protocol Versions

The protocol_versions parameter specifies which protocols your agent supports:

from azure.ai.projects.models import ProtocolVersionRecord, AgentEndpointProtocol

# RESPONSES protocol - standard agent responses
protocol_versions=[
    ProtocolVersionRecord(protocol=AgentEndpointProtocol.RESPONSES, version="2.0.0")
]

Available Protocols:

| Protocol | Description |

|----------|-------------|

| AgentEndpointProtocol.RESPONSES | Standard response protocol for agent interactions |

Resource Allocation

Specify CPU and memory for your container:

definition=HostedAgentDefinition(
    protocol_versions=[...],
    container_configuration=ContainerConfiguration(image=os.environ["REVIEWED_CONTAINER_IMAGE"]),
    cpu="2",      # 2 CPU cores
    memory="4Gi"  # 4 GiB memory
)

Illustrative resource sizes; verify regional/SKU limits before provisioning:

| Resource | Min | Max | Default |

|----------|-----|-----|---------|

| CPU | 0.5 | 4 | 1 |

| Memory | 1Gi | 8Gi | 2Gi |

Tools Configuration

Configure tool support inside the selected container runtime/framework. The following tool dictionaries describe application-specific intent; do not assume a tools field exists on HostedAgentDefinition or that defining it provisions a tool or grants permission:

Code Interpreter

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

MCP Tools

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

Multiple Tools

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:

environment_variables={
    "AZURE_AI_PROJECT_ENDPOINT": os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    "MODEL_NAME": os.environ["MODEL_DEPLOYMENT_NAME"],
    "LOG_LEVEL": "INFO",
    "CUSTOM_CONFIG": "value"
}

Best Practice: Keep non-secret deployment names and a reviewed immutable image reference in configuration; secrets need the platform's actual protected injection mechanism. Arbitrary environment dictionaries do not establish Key Vault integration.

Complete Example

import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import (
    HostedAgentDefinition,
    ContainerConfiguration,
    ProtocolVersionRecord,
    AgentEndpointProtocol,
)

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=HostedAgentDefinition(
            protocol_versions=[
                ProtocolVersionRecord(
                    protocol=AgentEndpointProtocol.RESPONSES,
                    version="2.0.0"
                )
            ],
            container_configuration=ContainerConfiguration(image=os.environ["REVIEWED_CONTAINER_IMAGE"]),
            cpu="2",
            memory="4Gi",
            environment_variables={
                "AZURE_AI_PROJECT_ENDPOINT": os.environ["AZURE_AI_PROJECT_ENDPOINT"],
                "MODEL_NAME": os.environ["MODEL_DEPLOYMENT_NAME"],
                "MAX_RETRIES": "3"
            }
        )
    )
    
    print(f"Created hosted agent: {agent.name}")
    print(f"Version: {agent.version}")
    # Poll get_version status with a deadline before treating deployment as active.
    
    return agent

if __name__ == "__main__":
    create_hosted_agent()

Async Pattern

import os
from azure.identity.aio import DefaultAzureCredential
from azure.ai.projects.aio import AIProjectClient
from azure.ai.projects.models import (
    HostedAgentDefinition,
    ContainerConfiguration,
    ProtocolVersionRecord,
    AgentEndpointProtocol,
)

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-agen

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