mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Eduard van Valkenburg
125 lines
4.5 KiB
Python
125 lines
4.5 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
|
|
|
|
import asyncio
|
|
import os
|
|
from textwrap import dedent
|
|
|
|
from agent_framework import Agent, Skill, SkillsProvider
|
|
from agent_framework.azure import AzureOpenAIResponsesClient
|
|
from azure.identity import AzureCliCredential
|
|
from dotenv import load_dotenv
|
|
|
|
"""
|
|
Skill Script Approval — Require human approval before executing skill scripts
|
|
|
|
This sample demonstrates how to use ``require_script_approval=True`` on
|
|
:class:`SkillsProvider` so that every call to ``run_skill_script`` is
|
|
gated by a human-in-the-loop approval step.
|
|
|
|
How it works:
|
|
1. A code-defined skill with a script is registered via SkillsProvider.
|
|
2. ``require_script_approval=True`` causes the agent to pause and return
|
|
approval requests in ``result.user_input_requests`` instead of executing
|
|
scripts immediately.
|
|
3. The application inspects each request and calls
|
|
``request.to_function_approval_response(approved=True|False)`` to approve
|
|
or reject.
|
|
4. The approval response is sent back via ``agent.run(approval_response, session=session)``
|
|
and the agent continues — executing the script if approved, or receiving
|
|
an error if rejected.
|
|
|
|
Prerequisites:
|
|
- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
|
|
- AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME (defaults to "gpt-4o-mini").
|
|
"""
|
|
|
|
# Load environment variables from .env file
|
|
load_dotenv()
|
|
|
|
# Define a code skill with a script that performs a sensitive operation
|
|
deployment_skill = Skill(
|
|
name="deployment",
|
|
description="Tools for deploying application versions to production",
|
|
content=dedent("""\
|
|
Use this skill when the user asks to deploy an application.
|
|
|
|
1. Run the deploy script with the version and environment parameters.
|
|
"""),
|
|
)
|
|
|
|
|
|
@deployment_skill.script
|
|
def deploy(version: str, environment: str = "staging") -> str:
|
|
"""Deploy the application to the specified environment."""
|
|
return f"Deployed version {version} to {environment}"
|
|
|
|
|
|
async def main() -> None:
|
|
"""Run the skill script approval demo."""
|
|
endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
|
|
deployment = os.environ.get("AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME", "gpt-4o-mini")
|
|
|
|
client = AzureOpenAIResponsesClient(
|
|
project_endpoint=endpoint,
|
|
deployment_name=deployment,
|
|
credential=AzureCliCredential(),
|
|
)
|
|
|
|
# Create the skills provider with script approval enabled
|
|
skills_provider = SkillsProvider(
|
|
skills=[deployment_skill],
|
|
require_script_approval=True,
|
|
)
|
|
|
|
async with Agent(
|
|
client=client,
|
|
instructions="You are a deployment assistant. Use the deployment skill to deploy applications.",
|
|
context_providers=[skills_provider],
|
|
) as agent:
|
|
session = agent.create_session()
|
|
|
|
print("Starting agent with skill script approval enabled...")
|
|
print("-" * 60)
|
|
|
|
# Step 1: Send the user request — the agent will try to call the script
|
|
query = "Deploy the latest application version 2.5.0 to the production environment"
|
|
print(f"User: {query}")
|
|
result = await agent.run(query, session=session)
|
|
|
|
# Step 2: Handle approval requests (with sessions, context is
|
|
# maintained automatically — just send the approval response)
|
|
while result.user_input_requests:
|
|
for request in result.user_input_requests:
|
|
print("\nApproval needed:")
|
|
print(f" Function: {request.function_call.name}") # type: ignore[union-attr]
|
|
print(f" Arguments: {request.function_call.arguments}") # type: ignore[union-attr]
|
|
|
|
# In a real application, prompt the user here
|
|
approved = True # Change to False to see rejection
|
|
print(f" Decision: {'Approved' if approved else 'Rejected'}")
|
|
|
|
# Send the approval response — session preserves conversation history
|
|
approval_response = request.to_function_approval_response(approved=approved)
|
|
result = await agent.run(approval_response, session=session)
|
|
|
|
print(f"\nAgent: {result}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|
|
|
|
"""
|
|
Sample output:
|
|
|
|
Starting agent with skill script approval enabled...
|
|
------------------------------------------------------------
|
|
User: Deploy version 2.5.0 to production
|
|
|
|
Approval needed:
|
|
Function: run_skill_script
|
|
Arguments: {"skill_name": "deployment", "script_name": "deploy", ...}
|
|
Decision: Approved
|
|
|
|
Agent: Successfully deployed version 2.5.0 to production.
|
|
"""
|