Building Your First Agentic AI Application in .NET 10 Step by Step

Introduction

Artificial Intelligence is evolving beyond simple chatbots and question-answering systems. Modern AI applications can now make decisions, plan tasks, use tools, and interact with external systems autonomously. These systems are commonly known as AI Agents.

An Agentic AI application goes beyond generating text. It can analyze a goal, determine the required steps, execute actions, and adapt based on results. This capability is transforming how developers build intelligent applications.

For .NET developers, building Agentic AI applications has become easier thanks to the growing ecosystem of AI frameworks, APIs, and libraries. In this article, you'll learn the fundamentals of Agentic AI and build your first Agentic AI application using .NET 10.

What Is Agentic AI?

Agentic AI refers to AI systems that can:

Understand a user's goal
Create a plan to achieve that goal
Execute actions using available tools
Evaluate results
Continue working until the objective is completed

Unlike traditional AI applications that only respond to prompts, Agentic AI applications actively perform tasks.

For example:

A traditional chatbot might answer:

"The weather in London is 22°C."

An AI Agent could:

Check the weather.
Analyze travel conditions.
Suggest clothing recommendations.
Create a travel checklist.

The agent performs multiple actions instead of providing a single response.

Core Components of an Agentic AI Application

Most Agentic AI systems contain the following components:

Goal

The objective provided by the user.

Example:

Find the latest .NET news and summarize it.

Planning

The agent determines the required steps.

Example:

1. Search for .NET news
2. Collect articles
3. Generate summary

Tools

External functions the AI can use.

Examples:

Web Search
Database Query
Email Service
Calendar Service
File System

Memory

Stores previous interactions and context.

Examples:

Conversation history
User preferences
Previous tasks

Execution

The agent performs actions and returns results.

Why Build Agentic AI with .NET?

.NET provides several advantages for AI development:

Strong type safety
High performance
Enterprise-ready architecture
Excellent API development support
Cloud-native deployment options
Integration with Azure AI services

Developers can combine ASP.NET Core, AI SDKs, and cloud services to create scalable AI Agent solutions.

Setting Up the Project

Create a new console application.

dotnet new console -n AgenticAIDemo
cd AgenticAIDemo

Install the required package.

dotnet add package OpenAI

Your project structure might look like this:

AgenticAIDemo
│
├── Program.cs
├── Services
│   └── WeatherService.cs
└── Models

Creating a Simple Tool

Agents become powerful when they can use tools.

Let's create a weather tool.

public class WeatherService
{
    public string GetWeather(string city)
    {
        return $"Current weather in {city} is 25°C and sunny.";
    }
}

This is a simple example, but in a production application, you would call a real weather API.

Creating the Agent

Now let's create a simple agent that uses the weather tool.

var weatherService = new WeatherService();

Console.WriteLine("Enter a city:");

var city = Console.ReadLine();

var result = weatherService.GetWeather(city!);

Console.WriteLine(result);

Output:

Enter a city:
London

Current weather in London is 25°C and sunny.

At this stage, the application acts as a basic tool-enabled agent.

Adding Decision-Making Capabilities

The real power of Agentic AI comes from decision-making.

Consider the following scenario:

User Prompt:

Should I go for a walk in London today?

The agent can:

Get weather information.
Analyze conditions.
Generate recommendations.

Example logic:

public string RecommendActivity(string weather)
{
    if(weather.Contains("sunny"))
    {
        return "Weather looks great. A walk is recommended.";
    }

    return "You may want to stay indoors today.";
}

The agent is no longer simply retrieving information. It is making decisions based on the data.

Understanding the Agent Workflow

A typical Agentic AI workflow looks like this:

User Goal
     ↓
AI Planning
     ↓
Tool Selection
     ↓
Tool Execution
     ↓
Result Evaluation
     ↓
Final Response

For example:

User:
Find the latest AI news and summarize it.

Agent Workflow:

Step 1: Search AI news
Step 2: Collect articles
Step 3: Extract key points
Step 4: Generate summary
Step 5: Return results

This ability to chain actions together is what makes Agentic AI different from traditional applications.

Practical Use Cases

Agentic AI applications are being used in many industries.

Customer Support

Agents can:

Answer questions
Create support tickets
Escalate issues

Software Development

Agents can:

Generate code
Review pull requests
Create documentation
Explain errors

Business Automation

Agents can:

Generate reports
Analyze sales data
Schedule meetings
Send notifications

Personal Productivity

Agents can:

Manage tasks
Summarize emails
Create reminders
Organize documents

Best Practices

When building Agentic AI applications in .NET, consider the following practices:

Keep Tools Small and Focused

Each tool should perform a single responsibility.

Good example:

GetWeather()
SearchNews()
SendEmail()

Avoid large tools that perform multiple unrelated tasks.

Validate Inputs

Always validate user inputs before executing actions.

if(string.IsNullOrWhiteSpace(city))
{
    throw new ArgumentException("City name is required.");
}

Log Agent Activities

Track actions performed by the agent.

Useful information includes:

Tool usage
API calls
Errors
Execution time

Secure External Integrations

Protect API keys and credentials.

Use:

Azure Key Vault
Environment Variables
Managed Identities

Add Memory Carefully

Store only relevant context to avoid unnecessary token usage and increased costs.

Conclusion

Agentic AI is changing how intelligent applications are built. Instead of simply responding to prompts, AI Agents can plan, reason, use tools, and complete tasks autonomously.

With .NET 10, developers can leverage a modern and powerful platform to build scalable Agentic AI solutions. By combining planning, tools, memory, and execution capabilities, you can create applications that solve real-world problems and automate complex workflows.

The simple example in this article demonstrates the foundation of an Agentic AI system. As you continue your journey, you can extend these concepts by integrating real AI models, external APIs, vector databases, and multi-agent workflows to build more advanced and production-ready solutions.