Many AI applications start with a simple chat interface.

The user sends a message, the application sends it to an AI model, and the model returns a response.

User
  |
  v
Application
  |
  v
AI Model
  |
  v
Response

This works well for basic question-and-answer scenarios. However, modern AI applications often need to do more than generate text.

An AI agent may need to ask follow-up questions, call tools, inspect results, make decisions, wait for user input, and continue the task based on the response.

That changes the application from a one-way chatbot into an interactive agent workflow.

In C#, this can be implemented by treating the agent as an ongoing process with state, tools, decisions, and interaction points instead of simply calling a model for every message.

Chatbot vs Interactive Agent

A traditional chatbot generally follows a request-response pattern:

User Question
      |
      v
AI Model
      |
      v
AI Response

An interactive agent can follow a more dynamic flow:

User Request
      |
      v
Agent
      |
      +---- Ask Question ----> User
      |                         |
      |<------ Answer ----------+
      |
      +---- Call Tool
      |
      +---- Analyze Result
      |
      +---- Ask Another Question
      |
      v
Final Action

The agent is no longer just generating a response.

It is participating in a process.

For example, instead of asking:

User:
Book a hotel in Delhi.

and immediately generating text, an interactive agent might ask:

Agent:
What dates are you traveling?

User:
October 10 to October 12.

Agent:
How many guests?

User:
Two.

Agent:
I found several matching options. Which price range should I use?

The application needs to maintain context throughout this interaction.

What Makes an Agent Interactive?

An interactive agent generally has several capabilities:

A useful mental model is:

                 +----------------+
                 |     Agent      |
                 +-------+--------+
                         |
          +--------------+--------------+
          |              |              |
          v              v              v
       User Input      Tools         Memory
          |              |              |
          +--------------+--------------+
                         |
                         v
                     Decision
                         |
              +----------+----------+
              |                     |
              v                     v
        Ask User Again          Continue

This structure allows the agent to react to the situation rather than always producing a final answer immediately.

Managing Conversation State in C#

The first requirement is maintaining state between interactions.

A simple state model might look like this:

public sealed class AgentSession
{
    public string SessionId { get; init; } = string.Empty;

    public string UserId { get; init; } = string.Empty;

    public List<ChatMessage> Messages { get; } = [];

    public string? CurrentTask { get; set; }

    public string? PendingQuestion { get; set; }
}

The important part is that the state should be associated with a specific session or workflow.

For example:

Session ID: SESSION-1001
User ID: USER-42
Current Task: Generate monthly report
Pending Question: Which department?

When the user responds, the application can retrieve the session and continue from the current state.

Asking Follow-Up Questions

A useful agent should not guess when important information is missing.

Consider a report-generation request:

User:
Create a sales report.

The agent may need additional information:

Agent:
Which period should I use?

The application can represent this as a pending interaction:

public sealed class PendingInteraction
{
    public string InteractionId { get; init; } = string.Empty;

    public string SessionId { get; init; } = string.Empty;

    public string Question { get; init; } = string.Empty;

    public string ExpectedInput { get; init; } = string.Empty;
}

The UI can display the question and send the user's answer back to the application.

The agent then continues instead of starting a completely new task.

Separating the Agent From the User Interface

A common architectural mistake is putting agent logic directly inside the web or mobile UI.

Instead, keep the interaction layer separate:

Web / Mobile UI
       |
       v
Agent API
       |
       v
Agent Orchestrator
       |
       +---- Memory
       |
       +---- Tools
       |
       +---- Workflow State
       |
       v
AI Model

For example, an ASP.NET Core endpoint could receive the user's message:

[ApiController]
[Route("api/agent")]
public class AgentController : ControllerBase
{
    private readonly IAgentService _agentService;

    public AgentController(IAgentService agentService)
    {
        _agentService = agentService;
    }

    [HttpPost("message")]
    public async Task<IActionResult> SendMessage(
        AgentMessageRequest request,
        CancellationToken cancellationToken)
    {
        var response = await _agentService.ProcessAsync(
            request,
            cancellationToken);

        return Ok(response);
    }
}

The controller should not contain the agent's reasoning or workflow logic.

That belongs in a dedicated service or orchestration layer.

Returning an Interaction Instead of a Final Answer

An interactive agent does not always need to return a completed response.

A response model can explicitly represent the next action:

public enum AgentResponseType
{
    Message,
    Question,
    ToolCall,
    Completed,
    Failed
}

Then:

public sealed class AgentResponse
{
    public AgentResponseType Type { get; init; }

    public string? Message { get; init; }

    public string? InteractionId { get; init; }
}

Now the client can determine what to do.

For example:

Type: Question

Message:
Which database should I use?

InteractionId:
INT-204

The UI can display the question and wait for the user's response.

This is more reliable than trying to infer application state from plain text.

Tool Calling Makes Agents More Useful

Interactive agents become more powerful when they can use tools.

Suppose a user asks:

How many orders were created today?

The agent can decide that it needs database information.

Conceptually:

User
 |
 v
Agent
 |
 +--> Need order data
 |
 v
Database Tool
 |
 v
Order Count
 |
 v
Agent
 |
 v
User

A tool can be represented through an interface:

public interface IOrderTool
{
    Task<int> GetTodayOrderCountAsync(
        CancellationToken cancellationToken);
}

The agent orchestration layer can invoke the tool when the task requires it.

This keeps external operations separate from the model itself.

Handling Multiple Interaction Turns

A multi-turn interaction might look like this:

Turn 1
User: Create a deployment report.

Turn 2
Agent: Which environment?

Turn 3
User: Production.

Turn 4
Agent: What date range?

Turn 5
User: Last seven days.

Turn 6
Agent: I found 14 deployments. Should I include failed deployments?

Turn 7
User: Yes.

Turn 8
Agent: Report generated.

The application needs to know:

That is why interactive agents need state management.

Handling Interruptions

Users do not always follow the expected conversation.

For example:

Agent:
Which environment should I use?

User:
Actually, forget that. Show me the latest production deployments.

The agent should be able to recognize that the user's intent changed.

The workflow should therefore avoid assuming that every next message is simply an answer to the previous question.

A useful approach is to distinguish:

Current task
Pending interaction
New user intent
Completed work

The orchestration layer can then decide whether to continue the current task or start another one.

Human Approval as an Interaction Point

Some actions should not be executed immediately.

For example:

Agent:
I prepared the production deployment.

Agent:
Do you want me to continue?

User:
Approve.

This is another type of interaction state.

public enum WorkflowStatus
{
    Running,
    WaitingForInput,
    WaitingForApproval,
    Completed,
    Failed
}

The workflow can move from:

Running
   |
   v
WaitingForApproval
   |
   v
Approved
   |
   v
Running
   |
   v
Completed

This approach works particularly well for long-running agent workflows.

Streaming Responses

Interactive applications can also stream model output rather than waiting for the entire response.

Conceptually:

User
 |
 v
Agent
 |
 +--> Token
 +--> Token
 +--> Token
 +--> Token
 |
 v
Completed

In ASP.NET Core applications, streaming can make the interface feel more responsive.

However, streaming text should not be confused with workflow completion.

A response might be streamed successfully while a later tool operation still fails.

For this reason, applications should separately track:

Response generation
Tool execution
Workflow completion

Error Handling in Interactive Agents

Failures should be represented as part of the agent's state.

For example:

public sealed class AgentExecutionResult
{
    public bool Success { get; init; }

    public string? Message { get; init; }

    public string? ErrorCode { get; init; }

    public bool CanRetry { get; init; }
}

This allows the application to distinguish:

Temporary failure
Permanent failure
User input required
Approval required
Task completed

Instead of returning a generic:

Something went wrong.

the application can provide a meaningful state to the client.

Interactive Agent Architecture

A practical architecture can look like this:

                    +----------------+
                    |   Web / App    |
                    +-------+--------+
                            |
                            v
                    +---------------+
                    |   Agent API   |
                    +-------+-------+
                            |
                            v
                 +----------------------+
                 | Agent Orchestrator  |
                 +----------+-----------+
                            |
          +-----------------+-----------------+
          |                 |                 |
          v                 v                 v
       Memory             Tools            Workflow
          |                 |                 |
          +-----------------+-----------------+
                            |
                            v
                       AI Model

Each component has a clear responsibility.

The UI handles interaction.

The API handles communication.

The orchestrator manages the agent workflow.

Memory provides relevant context.

Tools perform external operations.

Workflow state tracks progress.

The model provides reasoning and language generation.

Common Mistakes

Treating Every Message as a New Conversation

Without persistent state, the agent loses context between requests.

Letting the Model Control Application State Directly

The model can suggest an action, but application code should validate important state transitions.

Asking Questions When Data Already Exists

Before asking the user, check available session state, memory, or application data.

Executing Tools Without Validation

Tool parameters should be validated before external operations are performed.

Mixing UI Logic With Agent Logic

Keep the orchestration layer separate from controllers and UI components.

Ignoring User Intent Changes

A user may abandon the current task and start another one. The application should support this naturally.

Best Practices for Production

Use Explicit Workflow States

Prefer:

Running
WaitingForInput
WaitingForApproval
Completed
Failed

over relying on free-form text.

Persist Important State

If the agent can run for a long time, do not depend only on application memory.

Validate Tool Inputs

Treat agent-generated tool parameters as untrusted input and validate them before execution.

Make External Operations Idempotent

A recovered workflow should not accidentally perform the same side effect multiple times.

Keep User Interaction Clear

Tell the user what information is needed and why when appropriate.

Set Reasonable Timeouts

External tool calls should not remain active indefinitely.

Log Workflow Transitions

Useful logs can include:

Session ID
Workflow ID
Current state
Tool name
Interaction ID
Execution result
Error code

Avoid placing sensitive information in logs unnecessarily.

Advantages and Disadvantages

Advantages

Disadvantages

Troubleshooting Interactive Agents

If an interactive agent behaves incorrectly, check the following:

  1. Verify that the correct session is being loaded.

  2. Check whether conversation state is persisted.

  3. Confirm that the current workflow state is correct.

  4. Verify that pending interaction IDs are valid.

  5. Check whether the user response is being associated with the correct task.

  6. Review tool execution logs.

  7. Confirm that failed tool calls are handled correctly.

  8. Check whether a previous workflow was accidentally restarted.

  9. Verify authorization before executing sensitive tools.

  10. Check whether the agent is receiving only the relevant context.

These checks can help determine whether the problem is caused by the model, application state, workflow logic, or an external tool.

Summary of the Article

Interactive AI agents are more capable than simple request-response chatbots because they can maintain state, ask questions, call tools, wait for user input, and continue multi-step tasks.

In C#, a reliable implementation should separate the user interface, agent orchestration, memory, tools, and workflow state. Explicit states such as WaitingForInput, WaitingForApproval, Completed, and Failed make the application's behavior easier to control and troubleshoot.

The most important design principle is to keep the model responsible for language and reasoning while application code remains responsible for state transitions, validation, authorization, and external side effects.

When these responsibilities are separated properly, a C# application can move beyond one-way chatbot interactions and support agents that participate in real workflows.