agent-framework/dotnet/samples/03-workflows/Agents/GroupChatToolApproval

Group Chat with Tool Approval Sample

This sample demonstrates how to use GroupChatBuilder with tools that require human approval before execution. A group of specialized agents collaborate on a task, and sensitive tool calls trigger human-in-the-loop approval.

What This Sample Demonstrates

• Using a custom GroupChatManager with agents that have approval-required tools
• Handling FunctionApprovalRequestContent in group chat scenarios
• Multi-round group chat with tool approval interruption and resumption
• Integrating tool call approvals with multi-agent workflows where different agents have different levels of tool access

How It Works

1. A GroupChatBuilder workflow is created with multiple specialized agents
2. A custom DeploymentGroupChatManager determines which agent speaks next based on conversation state
3. Agents collaborate on a software deployment task:
   • QA Engineer: Runs automated tests
   • DevOps Engineer: Checks staging status, creates rollback plan, and deploys to production
4. When the deployment agent tries to deploy to production, it triggers an approval request
5. The sample simulates human approval and the workflow completes

Key Components

Approval-Required Tools

The DeployToProduction function is wrapped with ApprovalRequiredAIFunction to require human approval:

new ApprovalRequiredAIFunction(AIFunctionFactory.Create(DeployToProduction))

Custom Group Chat Manager

The DeploymentGroupChatManager implements custom speaker selection logic:

• First iteration: QA Engineer runs tests
• Subsequent iterations: DevOps Engineer handles deployment tasks

Approval Handling

The sample demonstrates continuous event-driven execution with inline approval handling:

• The workflow runs in a single event loop.
• When an approval-required tool is invoked, the loop surfaces an approval request, processes the (simulated) human response, and then continues execution without starting a separate phase.

Prerequisites

• Azure OpenAI or OpenAI configured with the required environment variables
• AZURE_OPENAI_ENDPOINT environment variable set
• AZURE_OPENAI_DEPLOYMENT_NAME environment variable (defaults to "gpt-4o-mini")

Running the Sample

dotnet run

Expected Output

The sample will show:

1. QA Engineer running tests
2. DevOps Engineer checking staging and creating rollback plan
3. An approval request for production deployment
4. Simulated approval response
5. DevOps Engineer completing the deployment
6. Workflow completion message

Related Samples

• Agent Function Tools with Approvals - Basic function approval pattern
• Agent Workflow Patterns - Group chat without approvals
• Human-in-the-Loop Basic - Workflow-level human interaction