Building .NET Applications with Claude AI Agents for Automation
Tapesh Mehta | Published on: Feb 06, 2026 | Est. reading time: 14 minutes
AI-powered automation is no longer a futuristic concept reserved for Python developers and data scientists. With Anthropic’s official C# SDK and the growing ecosystem of .NET libraries for Claude, C# developers can now build intelligent agents that automate complex business workflows directly within their existing .NET applications. Whether you need to process documents, generate reports, orchestrate multi-step tasks, or build conversational assistants, Claude’s tool use capabilities and the official .NET SDK make it surprisingly straightforward.
In this guide, we will walk through practical, production-ready examples of building .NET applications that leverage Claude AI agents for automation. From setting up the SDK to implementing tool-calling agents and integrating with the Model Context Protocol (MCP), you will learn everything you need to start building Claude agents in C# today. If you have been following best practices for integrating AI into .NET applications, this article takes things to the next level with agentic workflows.
Table of Contents
- What Are Claude AI Agents and Why .NET?
- Setting Up the Anthropic C# SDK
- Building a Tool-Calling Agent in C#
- Integrating with Microsoft.Extensions.AI
- Building a Document Processing Agent
- Leveraging MCP for Extended Agent Capabilities
- Dependency Injection and ASP.NET Core Integration
- Best Practices for Production Claude Agents
- Real-World Automation Scenarios
- Conclusion
What Are Claude AI Agents and Why .NET?
A Claude AI agent is an application that uses Claude’s reasoning capabilities combined with external tools to autonomously accomplish tasks. Unlike a simple chatbot that only generates text responses, an agent can decide which tools to call, process the results, and iterate until a task is complete. Think of it as giving Claude a set of capabilities and letting it figure out the best way to solve a problem.
The .NET ecosystem is an excellent fit for building Claude AI agents for automation. Enterprise applications are overwhelmingly built on .NET, and the ability to embed intelligent automation directly into existing C# codebases means you do not need to maintain a separate Python service or deal with cross-language communication overhead. Anthropic has released an official C# SDK (currently in beta) that provides first-class support for the Messages API, streaming, tool use, and the IChatClient interface from Microsoft.Extensions.AI. This means Claude agents integrate seamlessly with the broader .NET AI ecosystem, including Semantic Kernel and the Microsoft Agent Framework.
If you have been exploring building intelligent apps with .NET 8, adding Claude agents to your toolkit opens up a whole new dimension of automation possibilities.
Setting Up the Anthropic C# SDK
Getting started with Claude AI agents in .NET requires the official Anthropic C# SDK. The SDK targets .NET 8 or later and provides strongly-typed access to the full Claude API. Here is how to set up your project.
Installation and Configuration
First, add the official Anthropic NuGet package to your project:
dotnet add package AnthropicNext, configure your API key. The SDK automatically reads from the ANTHROPIC_API_KEY environment variable, but you can also pass it explicitly:
using Anthropic;
using Anthropic.Models.Messages;
// Option 1: Uses ANTHROPIC_API_KEY environment variable automatically
AnthropicClient client = new();
// Option 2: Explicit API key
AnthropicClient client = new(new AnthropicOptions
{
ApiKey = "your-api-key-here"
});For production applications, you should store your API key in Azure Key Vault or a similar secrets manager. If you are working with Azure, check out our guide on implementing Azure Key Vault in C# for secure configuration.
Making Your First API Call
Here is a simple example that sends a message to Claude and prints the response:
using Anthropic;
using Anthropic.Models.Messages;
AnthropicClient client = new();
MessageCreateParams parameters = new()
{
MaxTokens = 1024,
Messages =
[
new()
{
Role = Role.User,
Content = "Summarize the key benefits of microservices architecture in 3 bullet points.",
},
],
Model = Model.ClaudeSonnet4_5_20250929,
};
var message = await client.Messages.Create(parameters);
Console.WriteLine(message);The SDK also supports streaming responses, which is essential for building responsive applications. Streaming allows your UI to display Claude’s response as it is being generated rather than waiting for the complete answer:
MessageCreateParams parameters = new()
{
MaxTokens = 1024,
Messages =
[
new()
{
Role = Role.User,
Content = "Explain dependency injection in ASP.NET Core.",
},
],
Model = Model.ClaudeSonnet4_5_20250929,
};
await foreach (var chunk in client.Messages.CreateStreaming(parameters))
{
Console.Write(chunk);
}Building a Tool-Calling Agent in C#
The real power of Claude AI agents for automation comes from tool use. Tools allow Claude to interact with external systems, databases, APIs, and more. You define the tools available to Claude, and the model decides when and how to use them based on the user’s request. This is the foundation of agentic behavior.
Defining Custom Tools
Tools are defined using JSON Schema-style descriptions that tell Claude what each tool does and what parameters it expects. Here is an example of defining a weather lookup tool and a database query tool for an agent:
using System.Text.Json;
public static class AgentTools
{
public static object GetWeatherTool() => new
{
name = "get_weather",
description = "Get the current weather for a specified city.",
input_schema = new
{
type = "object",
properties = new
{
city = new
{
type = "string",
description = "The city name, e.g. 'London' or 'New York'"
},
units = new
{
type = "string",
@enum = new[] { "celsius", "fahrenheit" },
description = "Temperature units"
}
},
required = new[] { "city" }
}
};
public static object GetOrderStatusTool() => new
{
name = "get_order_status",
description = "Look up the status of a customer order by order ID.",
input_schema = new
{
type = "object",
properties = new
{
order_id = new
{
type = "string",
description = "The unique order identifier"
}
},
required = new[] { "order_id" }
}
};
}Implementing the Agent Loop
An agent loop is the core pattern where you send a message to Claude, check if it wants to use a tool, execute that tool, send the results back, and repeat until Claude provides a final text response. This is how autonomous behavior emerges, as Claude can chain multiple tool calls together to solve complex problems.
public class ClaudeAgent
{
private readonly HttpClient _httpClient;
private readonly string _apiKey;
private readonly string _model = "claude-sonnet-4-5-20250929";
public ClaudeAgent(string apiKey)
{
_apiKey = apiKey;
_httpClient = new HttpClient
{
BaseAddress = new Uri("https://api.anthropic.com/")
};
_httpClient.DefaultRequestHeaders.Add("x-api-key", _apiKey);
_httpClient.DefaultRequestHeaders.Add("anthropic-version", "2023-06-01");
}
public async Task<string> RunAgentAsync(string userMessage)
{
var messages = new List<object>
{
new { role = "user", content = userMessage }
};
var tools = new[]
{
AgentTools.GetWeatherTool(),
AgentTools.GetOrderStatusTool()
};
while (true)
{
var requestBody = new
{
model = _model,
max_tokens = 4096,
messages,
tools
};
var response = await SendRequestAsync(requestBody);
var content = response.GetProperty("content");
var stopReason = response.GetProperty("stop_reason").GetString();
if (stopReason == "tool_use")
{
// Claude wants to use a tool
messages.Add(new { role = "assistant", content = content });
var toolResults = new List<object>();
foreach (var block in content.EnumerateArray())
{
if (block.GetProperty("type").GetString() == "tool_use")
{
var toolName = block.GetProperty("name").GetString();
var toolInput = block.GetProperty("input");
var toolId = block.GetProperty("id").GetString();
var result = await ExecuteToolAsync(toolName, toolInput);
toolResults.Add(new
{
type = "tool_result",
tool_use_id = toolId,
content = result
});
}
}
messages.Add(new { role = "user", content = toolResults });
}
else
{
// Claude has a final text response
foreach (var block in content.EnumerateArray())
{
if (block.GetProperty("type").GetString() == "text")
{
return block.GetProperty("text").GetString();
}
}
}
}
}
private async Task<string> ExecuteToolAsync(
string toolName, JsonElement input)
{
return toolName switch
{
"get_weather" => await GetWeatherAsync(
input.GetProperty("city").GetString()),
"get_order_status" => await GetOrderStatusAsync(
input.GetProperty("order_id").GetString()),
_ => "Unknown tool"
};
}
private Task<string> GetWeatherAsync(string city)
{
// Replace with real weather API call
return Task.FromResult(
$"{{\"city\": \"{city}\", " +
$"\"temperature\": 22, \"condition\": \"Sunny\"}}");
}
private Task<string> GetOrderStatusAsync(string orderId)
{
// Replace with real database lookup
return Task.FromResult(
$"{{\"order_id\": \"{orderId}\", " +
$"\"status\": \"Shipped\", \"eta\": \"2026-02-10\"}}");
}
private async Task<JsonElement> SendRequestAsync(object body)
{
var json = JsonSerializer.Serialize(body);
var content = new StringContent(
json, System.Text.Encoding.UTF8, "application/json");
var response = await _httpClient.PostAsync(
"v1/messages", content);
var responseJson = await response.Content.ReadAsStringAsync();
return JsonDocument.Parse(responseJson).RootElement;
}
}This agent loop pattern is the backbone of all Claude AI agents for automation. Claude examines the user’s request, decides which tools are relevant, calls them in sequence, and synthesizes the results into a coherent response. For more advanced patterns, you can combine this with event-driven architectures using .NET and Kafka to build agents that react to real-time events.
Integrating with Microsoft.Extensions.AI
One of the most exciting aspects of the official Anthropic C# SDK is its implementation of the IChatClient interface from Microsoft.Extensions.AI (MEAI). This means you can use Claude as a drop-in replacement for any AI provider in your .NET application, making it easy to switch between models or even use multiple providers in the same application.
using Anthropic;
using Microsoft.Extensions.AI;
// The Anthropic SDK implements IChatClient
AnthropicClient anthropicClient = new();
IChatClient chatClient = anthropicClient; // Works directly
var response = await chatClient.GetResponseAsync(
"What are the key differences between " +
"microservices and monolithic architectures?");
Console.WriteLine(response.Text);This abstraction is particularly powerful when building enterprise applications where you might want to use Claude Opus for complex reasoning tasks and a lighter model for simple classification or routing. The IChatClient interface also enables integration with the Microsoft Agent Framework, which lets you compose Claude agents with other agent types in orchestrated workflows.
Building a Document Processing Agent
Let us build a practical example: a document processing agent that reads uploaded files, extracts key information, and stores it in a structured format. This is a common automation scenario in enterprise environments where teams deal with invoices, contracts, or reports.
public class DocumentProcessingAgent
{
private readonly AnthropicClient _client;
public DocumentProcessingAgent()
{
_client = new AnthropicClient();
}
public async Task<InvoiceData> ProcessInvoiceAsync(
byte[] pdfBytes)
{
string base64Pdf = Convert.ToBase64String(pdfBytes);
MessageCreateParams parameters = new()
{
MaxTokens = 2048,
Messages =
[
new()
{
Role = Role.User,
Content = new object[]
{
new
{
type = "document",
source = new
{
type = "base64",
media_type = "application/pdf",
data = base64Pdf
}
},
new
{
type = "text",
text = "Extract the following from " +
"this invoice and return as JSON: " +
"vendor_name, invoice_number, " +
"invoice_date, total_amount, " +
"line_items (description, quantity, " +
"unit_price, total). Return ONLY " +
"valid JSON, no markdown."
}
}
}
],
Model = Model.ClaudeSonnet4_5_20250929,
};
var message = await _client.Messages.Create(parameters);
var jsonResponse = message.ToString();
return JsonSerializer.Deserialize<InvoiceData>(
jsonResponse,
new JsonSerializerOptions
{
PropertyNameCaseInsensitive = true
});
}
}
public record InvoiceData(
string VendorName,
string InvoiceNumber,
string InvoiceDate,
decimal TotalAmount,
List<LineItem> LineItems);
public record LineItem(
string Description,
int Quantity,
decimal UnitPrice,
decimal Total);Claude’s vision capabilities let it read PDFs and images natively, making document processing agents straightforward to build. You can extend this pattern to handle contracts, receipts, medical forms, or any structured document extraction scenario. For teams building ASP.NET Core Web APIs, wrapping this agent in an API endpoint makes it available to your entire application ecosystem.
Leveraging MCP for Extended Agent Capabilities
The Model Context Protocol (MCP) is an open standard created by Anthropic that enables standardized integrations between AI agents and external services. Microsoft and Anthropic have collaborated to create an official C# SDK for MCP, available as the ModelContextProtocol NuGet package. MCP handles authentication and API calls automatically, which means your Claude agents can connect to tools like Slack, GitHub, Google Drive, databases, and more without writing custom integration code.
An MCP server exposes a set of tools that any MCP-compatible client can discover and use. Here is how you can build a simple MCP server in .NET that exposes database query capabilities to Claude agents:
using ModelContextProtocol;
using Microsoft.Extensions.Hosting;
var builder = Host.CreateApplicationBuilder(args);
builder.Services.AddMcpServer()
.WithStdioTransport()
.WithTools<DatabaseTools>();
await builder.Build().RunAsync();
public class DatabaseTools
{
[McpTool("query_customers",
"Search for customers by name or email")]
public async Task<string> QueryCustomers(
[McpParameter("search_term",
"Name or email to search for")] string searchTerm)
{
// Your database query logic here
using var connection = new SqlConnection(
"your-connection-string");
await connection.OpenAsync();
var command = new SqlCommand(
"SELECT TOP 10 Id, Name, Email " +
"FROM Customers " +
"WHERE Name LIKE @term " +
"OR Email LIKE @term",
connection);
command.Parameters.AddWithValue(
"@term", $"%{searchTerm}%");
var results = new List<object>();
using var reader = await command.ExecuteReaderAsync();
while (await reader.ReadAsync())
{
results.Add(new
{
Id = reader.GetInt32(0),
Name = reader.GetString(1),
Email = reader.GetString(2)
});
}
return JsonSerializer.Serialize(results);
}
}MCP servers decouple your tool implementations from your agent logic, making it easy to reuse the same tools across different agents and applications. The growing MCP ecosystem means you can quickly add new capabilities to your Claude AI agents for automation as pre-built integrations become available.
Dependency Injection and ASP.NET Core Integration
For production .NET applications, you will want to register the Anthropic client in your dependency injection container and add resilience with retry policies. Here is how to set up Claude in an ASP.NET Core application with proper DI and resilience:
using Anthropic;
using Microsoft.Extensions.Http.Resilience;
var builder = WebApplication.CreateBuilder(args);
// Register AnthropicClient with resilience
builder.Services.AddHttpClient<AnthropicClient>()
.AddStandardResilienceHandler();
// Register your agent as a service
builder.Services.AddScoped<IDocumentAgent, DocumentProcessingAgent>();
builder.Services.AddScoped<ICustomerSupportAgent, CustomerSupportAgent>();
var app = builder.Build();
app.MapPost("/api/process-invoice", async (
IFormFile file,
IDocumentAgent agent) =>
{
using var stream = new MemoryStream();
await file.CopyToAsync(stream);
var result = await agent.ProcessInvoiceAsync(
stream.ToArray());
return Results.Ok(result);
});
app.MapPost("/api/support", async (
SupportRequest request,
ICustomerSupportAgent agent) =>
{
var response = await agent.HandleRequestAsync(
request.Message);
return Results.Ok(new { reply = response });
});
app.Run();The AddStandardResilienceHandler from Microsoft.Extensions.Http.Resilience adds automatic retries, circuit breakers, and timeouts, which is essential when calling external APIs like Claude in production. This approach follows the same patterns you would use for any high-performance ASP.NET Core application.
Best Practices for Production Claude Agents
Building Claude AI agents for automation that are reliable and cost-effective in production requires attention to several key areas. Here are the practices we have found most important when deploying .NET applications with Claude agents.
Token Management and Cost Optimization
Claude API pricing is based on input and output tokens. To keep costs predictable, monitor your token usage closely and use the effort parameter to control reasoning depth. For routine tasks, Claude Sonnet or Haiku can handle the work at a fraction of the cost of Opus. Only route complex reasoning tasks to Opus when necessary.
// Use Sonnet for simple tasks
var simpleParams = new MessageCreateParams()
{
Model = Model.ClaudeSonnet4_5_20250929,
MaxTokens = 512,
Messages = [ /* ... */ ]
};
// Use Opus only for complex multi-step reasoning
var complexParams = new MessageCreateParams()
{
Model = Model.ClaudeOpus4_5_20250929,
MaxTokens = 4096,
Messages = [ /* ... */ ]
};Error Handling and Resilience
Always wrap your agent calls in proper error handling. The SDK throws specific exception types that you can catch and handle accordingly:
try
{
var result = await agent.RunAgentAsync(userMessage);
return Results.Ok(result);
}
catch (AnthropicIOException ex)
{
// Network or I/O error - retry or fallback
logger.LogError(ex, "Network error calling Claude API");
return Results.StatusCode(503);
}
catch (AnthropicInvalidDataException ex)
{
// Unexpected response format
logger.LogError(ex, "Invalid response from Claude API");
return Results.StatusCode(500);
}
catch (AnthropicException ex)
{
// General API error
logger.LogError(ex, "Claude API error: {Message}", ex.Message);
return Results.StatusCode(500);
}Security and Prompt Injection Prevention
When building agents that process user input, always validate and sanitize inputs before passing them to Claude. Never expose raw tool results to end users without filtering. Use system prompts to constrain Claude’s behavior and prevent it from performing actions outside its intended scope. For a deeper dive into securing your .NET applications, review our article on best practices for securing .NET applications.
Real-World Automation Scenarios
Claude AI agents for automation shine in scenarios that require a combination of understanding natural language, reasoning about data, and taking structured actions. Here are some practical use cases where .NET developers are deploying Claude agents today.
Customer support automation is one of the most common applications. A Claude agent connected to your CRM, order management system, and knowledge base can handle customer inquiries end-to-end, escalating only when human judgment is genuinely needed. Code review and quality assurance agents can analyze pull requests, check for common patterns, and suggest improvements aligned with your team’s coding standards. Financial reporting agents can pull data from multiple sources, perform calculations, generate charts, and compile polished reports. DevOps automation agents can diagnose production issues, suggest fixes, and even apply patches with appropriate approval workflows.
The key insight is that Claude agents are not replacing developers. They are handling the repetitive, time-consuming parts of workflows so that developers can focus on what requires human creativity and judgment. If your team is already using generative AI in software development, adding Claude agents for task automation is the natural next step.
Conclusion
Building .NET applications with Claude AI agents for automation is now accessible to every C# developer thanks to the official Anthropic SDK, the Model Context Protocol, and deep integration with the Microsoft AI ecosystem. The patterns we covered, from basic API calls to tool-calling agent loops, document processing, and MCP integration, provide a solid foundation for building intelligent automation into your existing .NET applications.
Start small by identifying a repetitive workflow in your application that involves reasoning over data and taking structured actions. Build a prototype agent with one or two tools, validate that it handles edge cases correctly, and then expand from there. The agentic approach is not about replacing human developers. It is about amplifying what your team can accomplish. And with .NET as your platform, you have the enterprise-grade tooling and ecosystem to deploy these agents with confidence.
Ready to build intelligent .NET applications with AI-powered automation? WireFuture’s ASP.NET development team specializes in building production-grade .NET solutions that leverage cutting-edge AI capabilities. Contact us to discuss how Claude AI agents can transform your business workflows.
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Tapesh Mehta is a seasoned tech worker who has been making apps for the web, mobile devices, and desktop for over 15+ years. Tapesh knows a lot of different computer languages and frameworks. For robust web solutions, he is an expert in Asp.Net, PHP, and Python. He is also very good at making hybrid mobile apps, which use Ionic, Xamarin, and Flutter to make cross-platform user experiences that work well together. In addition, Tapesh has a lot of experience making complex desktop apps with WPF, which shows how flexible and creative he is when it comes to making software. His work is marked by a constant desire to learn and change.
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