Handle Requests and Responses in Workflows

In .NET, human-in-the-loop workflows use RequestPort and external request handling to pause execution and gather user input. This pattern enables interactive workflows where the system can request information from external sources during execution.

Prerequisites

• .NET 8.0 SDK or later.
• Azure OpenAI service endpoint and deployment configured.
• Azure CLI installed and authenticated (for Azure credential authentication).
• Basic understanding of C# and async programming.
• A new console application.

Install NuGet packages

First, install the required packages for your .NET project:

dotnet add package Microsoft.Agents.AI.Workflows --prerelease

Key Components

RequestPort and External Requests

A RequestPort acts as a bridge between the workflow and external input sources. When the workflow needs input, it generates a RequestInfoEvent that your application handles:

// Create a RequestPort for handling human input requests
RequestPort numberRequestPort = RequestPort.Create<NumberSignal, int>("GuessNumber");

Signal Types

Define signal types to communicate different request types:

/// <summary>
/// Signals used for communication between guesses and the JudgeExecutor.
/// </summary>
internal enum NumberSignal
{
    Init,     // Initial guess request
    Above,    // Previous guess was too high
    Below,    // Previous guess was too low
}

Workflow Executor

Create executors that process user input and provide feedback:

/// <summary>
/// Executor that judges the guess and provides feedback.
/// </summary>
internal sealed class JudgeExecutor : Executor<int>("Judge")
{
    private readonly int _targetNumber;
    private int _tries;

    public JudgeExecutor(int targetNumber) : this()
    {
        _targetNumber = targetNumber;
    }

    public override async ValueTask HandleAsync(int message, IWorkflowContext context, CancellationToken cancellationToken)
    {
        _tries++;
        if (message == _targetNumber)
        {
            await context.YieldOutputAsync($"{_targetNumber} found in {_tries} tries!", cancellationToken)
                         .ConfigureAwait(false);
        }
        else if (message < _targetNumber)
        {
            await context.SendMessageAsync(NumberSignal.Below, cancellationToken).ConfigureAwait(false);
        }
        else
        {
            await context.SendMessageAsync(NumberSignal.Above, cancellationToken).ConfigureAwait(false);
        }
    }
}

Building the Workflow

Connect the RequestPort and executor in a feedback loop:

internal static class WorkflowHelper
{
    internal static ValueTask<Workflow<NumberSignal>> GetWorkflowAsync()
    {
        // Create the executors
        RequestPort numberRequestPort = RequestPort.Create<NumberSignal, int>("GuessNumber");
        JudgeExecutor judgeExecutor = new(42);

        // Build the workflow by connecting executors in a loop
        return new WorkflowBuilder(numberRequestPort)
            .AddEdge(numberRequestPort, judgeExecutor)
            .AddEdge(judgeExecutor, numberRequestPort)
            .WithOutputFrom(judgeExecutor)
            .BuildAsync<NumberSignal>();
    }
}

Executing the Interactive Workflow

Handle external requests during workflow execution:

private static async Task Main()
{
    // Create the workflow
    var workflow = await WorkflowHelper.GetWorkflowAsync().ConfigureAwait(false);

    // Execute the workflow
    await using StreamingRun handle = await InProcessExecution.StreamAsync(workflow, NumberSignal.Init).ConfigureAwait(false);
    await foreach (WorkflowEvent evt in handle.WatchStreamAsync().ConfigureAwait(false))
    {
        switch (evt)
        {
            case RequestInfoEvent requestInputEvt:
                // Handle human input request from the workflow
                ExternalResponse response = HandleExternalRequest(requestInputEvt.Request);
                await handle.SendResponseAsync(response).ConfigureAwait(false);
                break;

            case WorkflowOutputEvent outputEvt:
                // The workflow has yielded output
                Console.WriteLine($"Workflow completed with result: {outputEvt.Data}");
                return;
        }
    }
}

Request Handling

Process different types of input requests:

private static ExternalResponse HandleExternalRequest(ExternalRequest request)
{
    switch (request.DataAs<NumberSignal?>())
    {
        case NumberSignal.Init:
            int initialGuess = ReadIntegerFromConsole("Please provide your initial guess: ");
            return request.CreateResponse(initialGuess);
        case NumberSignal.Above:
            int lowerGuess = ReadIntegerFromConsole("You previously guessed too large. Please provide a new guess: ");
            return request.CreateResponse(lowerGuess);
        case NumberSignal.Below:
            int higherGuess = ReadIntegerFromConsole("You previously guessed too small. Please provide a new guess: ");
            return request.CreateResponse(higherGuess);
        default:
            throw new ArgumentException("Unexpected request type.");
    }
}

private static int ReadIntegerFromConsole(string prompt)
{
    while (true)
    {
        Console.Write(prompt);
        string? input = Console.ReadLine();
        if (int.TryParse(input, out int value))
        {
            return value;
        }
        Console.WriteLine("Invalid input. Please enter a valid integer.");
    }
}

Implementation Concepts

RequestInfoEvent Flow

1. Workflow Execution: The workflow processes until it needs external input
2. Request Generation: RequestPort generates a RequestInfoEvent with the request details
3. External Handling: Your application catches the event and gathers user input
4. Response Submission: Send an ExternalResponse back to continue the workflow
5. Workflow Resumption: The workflow continues processing with the provided input

Workflow Lifecycle

• Streaming Execution: Use StreamAsync to monitor events in real-time
• Event Handling: Process RequestInfoEvent for input requests and WorkflowOutputEvent for completion
• Response Coordination: Match responses to requests using the workflow's response handling mechanism

Implementation Flow

1. Workflow Initialization: The workflow starts by sending a NumberSignal.Init to the RequestPort.

2. Request Generation: The RequestPort generates a RequestInfoEvent requesting an initial guess from the user.

3. Workflow Pause: The workflow pauses and waits for external input while the application handles the request.

4. Human Response: The external application collects user input and sends an ExternalResponse back to the workflow.

5. Processing and Feedback: The JudgeExecutor processes the guess and either completes the workflow or sends a new signal (Above/Below) to request another guess.

6. Loop Continuation: The process repeats until the correct number is guessed.

Framework Benefits

• Type Safety: Strong typing ensures request-response contracts are maintained
• Event-Driven: Rich event system provides visibility into workflow execution
• Pausable Execution: Workflows can pause indefinitely while waiting for external input
• State Management: Workflow state is preserved across pause-resume cycles
• Flexible Integration: RequestPorts can integrate with any external input source (UI, API, console, etc.)

Complete Sample

For the complete working implementation, see the Human-in-the-Loop Basic sample.

This pattern enables building sophisticated interactive applications where users can provide input at key decision points within automated workflows.

What You'll Build

You'll create an interactive number guessing game workflow that demonstrates request-response patterns:

• An AI agent that makes intelligent guesses
• Executors that can directly send requests using the request_info API
• A turn manager that coordinates between the agent and human interactions using @response_handler
• Interactive console input/output for real-time feedback

Prerequisites

• Python 3.10 or later
• Azure OpenAI deployment configured
• Azure CLI authentication configured (az login)
• Basic understanding of Python async programming

Key Concepts

Requests-and-Responses Capabilities

Executors have built-in requests-and-responses capabilities that enable human-in-the-loop interactions:

• Call ctx.request_info(request_data=request_data, response_type=response_type) to send requests
• Use the @response_handler decorator to handle responses
• Define custom request/response types without inheritance requirements

Request-Response Flow

Executors can send requests directly using ctx.request_info() and handle responses using the @response_handler decorator:

1. Executor calls ctx.request_info(request_data=request_data, response_type=response_type)
2. Workflow emits a RequestInfoEvent with the request data
3. External system (human, API, etc.) processes the request
4. Response is sent back via send_responses_streaming()
5. Workflow resumes and delivers the response to the executor's @response_handler method

Setting Up the Environment

First, install the required packages:

pip install agent-framework-core
pip install azure-identity
pip install pydantic

Define Request and Response Models

Start by defining the data structures for request-response communication:

import asyncio
from dataclasses import dataclass
from pydantic import BaseModel

from agent_framework import (
    AgentExecutor,
    AgentExecutorRequest,
    AgentExecutorResponse,
    ChatMessage,
    Executor,
    RequestInfoEvent,
    Role,
    WorkflowBuilder,
    WorkflowContext,
    WorkflowOutputEvent,
    WorkflowRunState,
    WorkflowStatusEvent,
    handler,
    response_handler,
)
from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import AzureCliCredential

@dataclass
class HumanFeedbackRequest:
    """Request message for human feedback in the guessing game."""
    prompt: str = ""
    guess: int | None = None

class GuessOutput(BaseModel):
    """Structured output from the AI agent with response_format enforcement."""
    guess: int

The HumanFeedbackRequest is a simple dataclass for structured request payloads:

• Strong typing for request payloads
• Forward-compatible validation
• Clear correlation semantics with responses
• Contextual fields (like the previous guess) for rich UI prompts

Create the Turn Manager

The turn manager coordinates the flow between the AI agent and human:

class TurnManager(Executor):
    """Coordinates turns between the AI agent and human player.

    Responsibilities:
    - Start the game by requesting the agent's first guess
    - Process agent responses and request human feedback
    - Handle human feedback and continue the game or finish
    """

    def __init__(self, id: str | None = None):
        super().__init__(id=id or "turn_manager")

    @handler
    async def start(self, _: str, ctx: WorkflowContext[AgentExecutorRequest]) -> None:
        """Start the game by asking the agent for an initial guess."""
        user = ChatMessage(Role.USER, text="Start by making your first guess.")
        await ctx.send_message(AgentExecutorRequest(messages=[user], should_respond=True))

    @handler
    async def on_agent_response(
        self,
        result: AgentExecutorResponse,
        ctx: WorkflowContext,
    ) -> None:
        """Handle the agent's guess and request human guidance."""
        # Parse structured model output (defensive default if agent didn't reply)
        text = result.agent_run_response.text or ""
        last_guess = GuessOutput.model_validate_json(text).guess if text else None

        # Craft a clear human prompt that defines higher/lower relative to agent's guess
        prompt = (
            f"The agent guessed: {last_guess if last_guess is not None else text}. "
            "Type one of: higher (your number is higher than this guess), "
            "lower (your number is lower than this guess), correct, or exit."
        )
        # Send a request using the request_info API
        await ctx.request_info(
            request_data=HumanFeedbackRequest(prompt=prompt, guess=last_guess),
            response_type=str
        )

    @response_handler
    async def on_human_feedback(
        self,
        original_request: HumanFeedbackRequest,
        feedback: str,
        ctx: WorkflowContext[AgentExecutorRequest, str],
    ) -> None:
        """Continue the game or finish based on human feedback."""
        reply = feedback.strip().lower()
        # Use the correlated request's guess to avoid extra state reads
        last_guess = original_request.guess

        if reply == "correct":
            await ctx.yield_output(f"Guessed correctly: {last_guess}")
            return

        # Provide feedback to the agent for the next guess
        user_msg = ChatMessage(
            Role.USER,
            text=f'Feedback: {reply}. Return ONLY a JSON object matching the schema {{"guess": <int 1..10>}}.',
        )
        await ctx.send_message(AgentExecutorRequest(messages=[user_msg], should_respond=True))

Build the Workflow

Create the main workflow that connects all components:

async def main() -> None:
    # Create the chat agent with structured output enforcement
    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
    agent = chat_client.create_agent(
        instructions=(
            "You guess a number between 1 and 10. "
            "If the user says 'higher' or 'lower', adjust your next guess. "
            'You MUST return ONLY a JSON object exactly matching this schema: {"guess": <integer 1..10>}. '
            "No explanations or additional text."
        ),
        response_format=GuessOutput,
    )

    # Create workflow components
    turn_manager = TurnManager(id="turn_manager")
    agent_exec = AgentExecutor(agent=agent, id="agent")

    # Build the workflow graph
    workflow = (
        WorkflowBuilder()
        .set_start_executor(turn_manager)
        .add_edge(turn_manager, agent_exec)  # Ask agent to make/adjust a guess
        .add_edge(agent_exec, turn_manager)  # Agent's response goes back to coordinator
        .build()
    )

    # Execute the interactive workflow
    await run_interactive_workflow(workflow)

async def run_interactive_workflow(workflow):
    """Run the workflow with human-in-the-loop interaction."""
    pending_responses: dict[str, str] | None = None
    completed = False
    workflow_output: str | None = None

    print("🎯 Number Guessing Game")
    print("Think of a number between 1 and 10, and I'll try to guess it!")
    print("-" * 50)

    while not completed:
        # First iteration uses run_stream("start")
        # Subsequent iterations use send_responses_streaming with pending responses
        stream = (
            workflow.send_responses_streaming(pending_responses)
            if pending_responses
            else workflow.run_stream("start")
        )

        # Collect events for this turn
        events = [event async for event in stream]
        pending_responses = None

        # Process events to collect requests and detect completion
        requests: list[tuple[str, str]] = []  # (request_id, prompt)
        for event in events:
            if isinstance(event, RequestInfoEvent) and isinstance(event.data, HumanFeedbackRequest):
                # RequestInfoEvent for our HumanFeedbackRequest
                requests.append((event.request_id, event.data.prompt))
            elif isinstance(event, WorkflowOutputEvent):
                # Capture workflow output when yielded
                workflow_output = str(event.data)
                completed = True

        # Check workflow status
        pending_status = any(
            isinstance(e, WorkflowStatusEvent) and e.state == WorkflowRunState.IN_PROGRESS_PENDING_REQUESTS
            for e in events
        )
        idle_with_requests = any(
            isinstance(e, WorkflowStatusEvent) and e.state == WorkflowRunState.IDLE_WITH_PENDING_REQUESTS
            for e in events
        )

        if pending_status:
            print("🔄 State: IN_PROGRESS_PENDING_REQUESTS (requests outstanding)")
        if idle_with_requests:
            print("⏸️  State: IDLE_WITH_PENDING_REQUESTS (awaiting human input)")

        # Handle human requests if any
        if requests and not completed:
            responses: dict[str, str] = {}
            for req_id, prompt in requests:
                print(f"\n🤖 {prompt}")
                answer = input("👤 Enter higher/lower/correct/exit: ").lower()

                if answer == "exit":
                    print("👋 Exiting...")
                    return
                responses[req_id] = answer
            pending_responses = responses

    # Show final result
    print(f"\n🎉 {workflow_output}")

Running the Example

For the complete working implementation, see the Human-in-the-Loop Guessing Game sample.

How It Works

1. Workflow Initialization: The workflow starts with the TurnManager requesting an initial guess from the AI agent.

2. Agent Response: The AI agent makes a guess and returns structured JSON, which flows back to the TurnManager.

3. Human Request: The TurnManager processes the agent's guess and calls ctx.request_info() with a HumanFeedbackRequest.

4. Workflow Pause: The workflow emits a RequestInfoEvent and continues until no further actions can be taken, then waits for human input.

5. Human Response: The external application collects human input and sends responses back using send_responses_streaming().

6. Resume and Continue: The workflow resumes, the TurnManager's @response_handler method processes the human feedback, and either ends the game or sends another request to the agent.

Key Benefits

• Structured Communication: Type-safe request and response models prevent runtime errors
• Correlation: Request IDs ensure responses are matched to the correct requests
• Pausable Execution: Workflows can pause indefinitely while waiting for external input
• State Preservation: Workflow state is maintained across pause-resume cycles
• Event-Driven: Rich event system provides visibility into workflow status and transitions

This pattern enables building sophisticated interactive applications where AI agents and humans collaborate seamlessly within structured workflows.