Using AI Agents in .NET – Build Autonomous Workflows with C# and ASP.NET Core

AI applications are evolving beyond simple chatbots and text generation systems. Modern AI systems can now perform tasks autonomously using AI agents.

AI agents are capable of:

Understanding goals
Planning workflows
Using APIs
Accessing enterprise systems
Executing tasks automatically

For .NET developers, AI agents open new possibilities for building intelligent enterprise applications and workflow automation systems.

In this article, we will explore how to build AI agent workflows using C#, ASP.NET Core, and modern AI APIs.

What Is an AI Agent?

An AI agent is an intelligent software system that can perform tasks autonomously using:

Large Language Models
APIs
Memory systems
Workflow orchestration
External tools

Unlike traditional chatbots, AI agents can make decisions and execute multi-step workflows.

How AI Agents Work

A typical AI agent workflow looks like this:

Step	Action
1	Receive user request
2	Analyze goal
3	Plan workflow
4	Use APIs or tools
5	Process results
6	Return final response

This allows AI agents to automate complex business operations.

Common AI Agent Use Cases

AI agents are increasingly used in:

Customer support automation
Workflow management
DevOps automation
AI copilots
Enterprise productivity systems
SaaS platforms

Modern businesses are adopting AI agents rapidly.

Why Build AI Agents in .NET?

.NET provides an excellent ecosystem for enterprise AI development because of:

ASP.NET Core
Web API support
Cloud integration
Scalability
Security features
Enterprise tooling

C# developers can build scalable AI orchestration systems efficiently.

Technologies Used in AI Agent Development

ASP.NET Core

Used for building APIs and backend services.

OpenAI APIs

Used for reasoning, conversation, and AI decision-making.

Semantic Kernel

Microsoft’s AI orchestration framework for:

Prompt workflows
Function calling
AI planning
AI memory

Vector Databases

Used for:

AI memory
Semantic retrieval
Long-term context

Creating a Simple AI Agent in ASP.NET Core

Create a new Web API project:

dotnet new webapi -n AIAgentDemo

Navigate to the project folder:

cd AIAgentDemo

Installing Semantic Kernel

Install required packages:

dotnet add package Microsoft.SemanticKernel

dotnet add package Microsoft.SemanticKernel.Connectors.OpenAI

Configuring OpenAI Settings

Add settings inside appsettings.json.

{
  "OpenAI": {
    "ApiKey": "YOUR_API_KEY",
    "Model": "gpt-4o-mini"
  }
}

Creating the AI Agent Service

Create a service class for AI orchestration.

using Microsoft.SemanticKernel;

public class AgentService
{
    private readonly Kernel _kernel;

    public AgentService(IConfiguration configuration)
    {
        var builder = Kernel.CreateBuilder();

        builder.AddOpenAIChatCompletion(
            modelId: configuration["OpenAI:Model"],
            apiKey: configuration["OpenAI:ApiKey"]);

        _kernel = builder.Build();
    }

    public async Task<string> ExecuteAsync(string task)
    {
        var prompt = $@"
        You are an AI agent.
        Complete this task:
        {task}";

        var result =
            await _kernel.InvokePromptAsync(prompt);

        return result.ToString();
    }
}

This service acts as a simple AI agent capable of task execution.

Registering the Service

builder.Services.AddSingleton<AgentService>();

Creating the API Controller

Create an API endpoint for the AI agent.

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

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

    [HttpPost]
    public async Task<IActionResult> Run(string task)
    {
        var result = await _agentService
            .ExecuteAsync(task);

        return Ok(result);
    }
}

This endpoint allows users to send tasks to the AI agent.

Real-World AI Agent Enhancements

Enterprise AI agents usually include additional capabilities.

Function Calling

AI agents can trigger:

APIs
Database operations
External services
Workflow engines

Memory Systems

AI agents often store memory using vector databases for contextual awareness.

Multi-Step Planning

Advanced agents can break large tasks into smaller workflows automatically.

Multi-Agent Systems

Modern architectures may include multiple AI agents working together collaboratively.

Benefits of AI Agents in Enterprise Applications

Workflow Automation

AI agents reduce repetitive manual work.

Improved Productivity

Businesses can automate complex operational tasks.

Intelligent Decision Support

AI agents help analyze information and assist users dynamically.

Scalable Enterprise Automation

AI systems can scale across cloud-native applications efficiently.

Challenges of AI Agents

Despite their advantages, AI agents also introduce challenges.

Security Risks

AI agents often access enterprise systems and sensitive data.

AI Hallucinations

Incorrect AI-generated decisions may create operational risks.

Infrastructure Costs

Large-scale AI agents require cloud AI infrastructure and API resources.

Workflow Reliability

Complex autonomous systems require monitoring and validation.

The Future of AI Agents in .NET

AI agents are expected to become major components of enterprise software systems.

Future AI agent systems may include:

Autonomous SaaS workflows
AI-powered enterprise copilots
Multi-agent collaboration
Intelligent DevOps systems
AI-native cloud applications

AI orchestration is becoming an important skill for modern .NET developers.

Conclusion

AI agents are transforming enterprise software by enabling autonomous workflows and intelligent automation.

Using ASP.NET Core, OpenAI APIs, and Semantic Kernel, .NET developers can build scalable AI agent systems for modern enterprise applications.

As AI adoption continues growing, understanding AI agents and orchestration patterns will become increasingly important for C# developers building next-generation software systems.