What Are AI Agents?

Introduction

You have probably used AI tools like ChatGPT or Claude to answer a question, write an email, or explain a piece of code. You type something. The AI replies. Done.

But what if the AI could do a task for you — without you typing every single step?

That is exactly what AI agents do. And in 2026, they are changing how developers build software, how teams automate workflows, and how businesses operate at scale.

In this article, you will learn what AI agents actually are, how they think and plan, and see a practical example of an agent in action that you can try yourself.

First, What Is a Regular AI Model Doing?

Before we talk about agents, it helps to understand what a regular AI model like ChatGPT or Claude is doing when you interact with it.

Think of it like this: a regular AI model is like a very smart person sitting in a room. You slide a note under the door with your question. They write an answer and slide it back. That is it. One question, one answer, no memory of last time, no ability to go outside the room and check anything.

This works great for many tasks — writing, explaining, brainstorming. But for anything that requires multiple steps, real-world actions, or decisions along the way, this model hits a wall.

So, What Exactly Is an AI Agent?

An AI agent is an AI system that can:

Receive a goal or task (not just a single question)
Break that goal into smaller steps on its own
Use tools like web search, APIs, databases, or code execution
Check if the output was correct and retry if needed
Keep going until the task is complete — without you guiding every step

In short, you give the agent a destination. It figures out the route, drives there, handles roadblocks, and arrives on its own.

Think of it like the difference between asking your junior developer to "look into this bug" versus asking Google. With a junior developer, you give a goal. They think, try, debug, and come back with a fix. That is agent behavior.

The Four Things Every AI Agent Can Do

Every AI agent, no matter how complex, works on four core abilities:

1. Perceive

The agent takes in information. This could be a user's request, an API response, a file, a webpage, or a database result.

2. Think

The agent reasons about what it received. It decides what to do next — which tool to use, what to ask, or whether the task is complete.

3. Act

The agent takes action. It might call an API, run a query, write a file, send an email, or search the web.

4. Observe

The agent looks at the result of its action and decides:

Was that successful?
Do I need to try something else?
Am I done?

This loop — Perceive → Think → Act → Observe — repeats until the goal is achieved. Developers often call this the ReAct loop (Reason + Act), and it is the engine behind most AI agents today.

A Real .NET Developer Example: The Bug-Fixing Agent

Let us make this concrete with a scenario every C# developer will recognize.

Imagine you have a C# method in your ASP.NET Web API that calculates the average score from a list of student results. It keeps throwing a DivideByZeroException in production when the list comes back empty from the database.

Instead of asking an AI, "What is wrong with this code?", you hand the task to an AI agent.

Here is what the agent does automatically, step by step:

Step 1 — Perceive

The agent receives your goal:

Fix the bug in this C# method and make sure it handles empty lists without throwing a DivideByZeroException.

Step 2 — Think

The agent reads your code and reasons:

scores.Count is zero when the list is empty. Dividing by zero will crash. I need a guard clause before the division.

Step 3 — Act

The agent rewrites the method with the fix, updates the unit test, and validates that it compiles correctly.

Step 4 — Observe

The agent runs the test suite. All tests pass, including the empty-list edge case.

Goal complete.

Original Buggy Method

// Original buggy method — crashes on empty list

public class ScoreService
{
    public double CalculateAverage(List<int> scores)
    {
        int total = scores.Sum();

        return total / scores.Count; // DivideByZeroException if list is empty!
    }
}

// Calling this with an empty list from the DB will throw:
// System.DivideByZeroException: Attempted to divide by zero.

Agent-Fixed C# Code

// Agent-fixed method with guard clause

public class ScoreService
{
    public double CalculateAverage(List<int> scores)
    {
        if (scores == null || scores.Count == 0)
            return 0; // Handles empty or null list gracefully

        return (double)scores.Sum() / scores.Count;
    }
}

// Now safe for all inputs:

var service = new ScoreService();

Console.WriteLine(service.CalculateAverage(new List<int>())); 
// Output: 0

Console.WriteLine(service.CalculateAverage(new List<int> { 85, 90, 78 })); 
// Output: 84.33

The agent did not just answer a question. It identified the root cause, applied the fix with proper C# null-safety, validated the result, and delivered production-ready code — all without you writing a follow-up prompt.

What Tools Can an AI Agent Actually Use?

The real power of an agent comes from the tools it has access to. Think of tools as superpowers you give the agent.

Common tools include:

Web Search — to look up current information or documentation
Code Execution — to actually run C#, SQL, or PowerShell scripts
File Read/Write — to read a CSV, update a config file, or write a report
API Calls — to hit REST endpoints, fetch data, or trigger actions in other systems
Database Queries — to pull or insert records in SQL or NoSQL databases
Email / Calendar — to send notifications or schedule meetings

As a developer, you decide which tools your agent gets. More tools means more capability — but also more responsibility to make sure the agent uses them safely.

How to Build an AI Agent in C# / .NET

As a .NET developer, you have solid options to start building agents without leaving the ecosystem you already know.

Available Frameworks and Services

Microsoft Semantic Kernel — the official Microsoft framework for building AI agents in C#. Supports tool calling (called Plugins), memory, and multi-step planning. Integrates natively with Azure OpenAI and OpenAI.
AutoGen for .NET (by Microsoft) — great for multi-agent scenarios where several agents collaborate on a task, available with .NET bindings.
Anthropic .NET SDK — the official SDK lets you call Claude directly from C#, including full tool use support for building custom agents.
Azure AI Agent Service — a managed Azure service that lets you deploy agents with built-in tool support (code interpreter, file search, custom APIs) without managing infrastructure.

Practical Example Using the Anthropic .NET SDK

Here is a practical example using the Anthropic .NET SDK to build a simple agent that can look up an order status from a fake database — a common real-world scenario in ASP.NET applications.

Install the SDK

dotnet add package Anthropic.SDK

Agent Code in C#

using Anthropic.SDK;
using Anthropic.SDK.Messaging;
using System.Text.Json;

var client = new AnthropicClient(); // Reads ANTHROPIC_API_KEY from env

// Define the tool the agent can use

var tools = new List<Tool>
{
    new Tool
    {
        Name = "get_order_status",
        Description = "Looks up the status of a customer order by order ID",
        InputSchema = new InputSchema
        {
            Type = "object",
            Properties = new Dictionary<string, Property>
            {
                ["order_id"] = new Property
                {
                    Type = "string",
                    Description = "The order ID to look up e.g. ORD-1042"
                }
            },
            Required = new List<string> { "order_id" }
        }
    }
};

var messages = new List<Message>
{
    new Message(RoleType.User, "What is the status of order ORD-1042?")
};

var response = await client.Messages.GetClaudeMessageAsync(
    new MessageParameters
    {
        Model = AnthropicModels.Claude35Sonnet,
        MaxTokens = 1024,
        Tools = tools,
        Messages = messages
    });

// Agent decides to call get_order_status("ORD-1042")
// Your code handles the tool call, returns the result
// Agent then gives a final natural language answer to the user

Console.WriteLine(response.Content[0].Text);

This is exactly the pattern you would use inside an ASP.NET Web API controller or a .NET background service. The agent receives a natural language request, decides which tool to call, your code executes the real business logic (database lookup, API call, etc.), and the agent composes a clean response for the end user.

Where Are AI Agents Being Used Right Now?

AI agents are already in production across many industries.

Customer Support Agents

Handle refund requests, check order status via API, and resolve tickets end-to-end without human involvement.

Code Review Agents

Scan pull requests in Azure DevOps or GitHub, identify bugs in C# code, suggest fixes, and post comments automatically.

Data Pipeline Agents

Monitor SQL Server or Azure SQL databases for anomalies, generate Excel/PDF reports, and email summaries to stakeholders using .NET background services.

DevOps Agents

Watch Azure Monitor logs, detect errors in ASP.NET applications, restart failed App Services, and alert the on-call engineer only when human input is needed.

Research Agents

Given a topic, they search the web, read multiple sources, and produce a structured summary document.

If your job involves repetitive multi-step tasks — reading data, making a decision, taking action, and checking a result — there is a good chance an AI agent can handle it.

One Important Thing to Keep in Mind

AI agents are powerful, but they are not magic. A few things to keep in mind as a developer:

Agents can make mistakes — always build in validation steps, especially for agents that write to databases or send emails.
Keep humans in the loop for high-stakes decisions — an agent can draft a response, but a human should approve it before it goes to a client.
Start small — build an agent that does one thing well before expanding its tool access. Complexity multiplies errors.
Log everything — since agents work autonomously, you need clear logs of what decisions they made and why, for debugging and auditing.

Conclusion

AI agents represent a major shift from traditional AI interactions. Instead of answering a single question and stopping, agents can take a goal, break it into steps, use tools, evaluate results, and continue working until the objective is achieved.

For developers, especially those working in the .NET ecosystem, agent frameworks and services are making it easier than ever to build systems that can automate real-world workflows. Whether it is fixing code, querying databases, reviewing pull requests, or monitoring infrastructure, AI agents are becoming practical tools that extend what software can accomplish autonomously.

Key Takeaways

AI agents work toward goals, not just individual prompts.
They follow a continuous loop of Perceive → Think → Act → Observe.
Agents can use external tools such as APIs, databases, code execution environments, and web search.
The difference between an AI model and an AI agent is autonomy and multi-step reasoning.
.NET developers can build agents using frameworks such as Semantic Kernel, AutoGen, Anthropic's .NET SDK, and Azure AI Agent Service.
AI agents are already being used for support, development, operations, research, and workflow automation.
Proper validation, logging, and human oversight remain essential when deploying agents in production.

Summary

AI agents extend the capabilities of traditional AI models by combining reasoning, planning, tool usage, and autonomous execution. Rather than responding to a single prompt, they can pursue a goal through multiple iterations of perception, reasoning, action, and observation. With growing support across the .NET ecosystem, developers can now build agents that automate complex workflows, interact with real systems, and deliver meaningful business value while still maintaining appropriate safeguards and oversight.