Automate 21risk tasks via Rube MCP (Composio). Always search tools first for current schemas.
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Build container-based Foundry Agents with Azure AI Projects SDK (HostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry.
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Review the instructions the agent will receive before you install.
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.
Build container-based hosted agents using HostedAgentDefinition from the Azure AI Projects SDK.
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.
export AZURE_AI_PROJECT_ENDPOINT="https://<resource>.services.ai.azure.com/api/projects/<project>"
Before creating hosted agents:
azure-ai-projects==2.3.0Use 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
)
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import (
HostedAgentDefinition,
ContainerConfiguration,
ProtocolVersionRecord,
AgentEndpointProtocol,
)
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})")
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.
client.agents.delete_version(
agent_name="my-hosted-agent",
agent_version=agent.version
)
| 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 |
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 |
Specify CPU and memory for your container:
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Automate 21risk tasks via Rube MCP (Composio). Always search tools first for current schemas.
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Automate 2chat tasks via Rube MCP (Composio). Always search tools first for current schemas.
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Usar al iniciar o mejorar un proyecto de IA: diagnostica código y restricciones, propone skills existentes y redacta tareas y un plan de implementación en español.
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Usar para crear o mejorar una skill de Andru.ia: define entradas y resultados, redacta SKILL.md con ejemplos y límites, valida la estructura y prepara una PR.
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Usar tras definir el sector de un proyecto: prepara un dossier en español con contexto, normas aplicables, patrones UX y riesgos, distinguiendo fuentes verificadas de hipótesis.
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AI-powered presentation generation via the 2slides API — create slides from text, match a reference image style, summarize documents into decks, add AI voice narration, and export pages/audio. Use f
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