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Nikhil Wagh
Nikhil Wagh

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Building Autonomous AI Agents in .NET: Let GPT Handle Your Business Workflows

#dotnet #ai #openai #aiagent

Introduction

Most developers are using ChatGPT as a helper — answering questions, generating snippets, maybe drafting an email. But what if GPT could do more?

What if it could:

  • Send invoices based on CRM activity
  • Process support tickets
  • Generate reports and publish them
  • Trigger actions in your database or send alerts

In this article, we’ll explore how to build Autonomous AI Agents in .NET using OpenAI’s Assistants API, Azure Functions, and Tool Integration, following 2025’s most powerful AI trend: agentic workflows.

What is an AI Agent?

An AI agent is an LLM (like GPT-4-turbo) paired with:

  • Memory: it remembers previous interactions
  • Tools: it can call APIs, run functions, or query data
  • Goals: it follows instructions, takes actions, and loops until a task is done

Think of it as GPT with arms and a to-do list.

Use Case: Smart Support Assistant for .NET Apps

Let’s say you run a SaaS product built on .NET 10. You want to:

  • Read new support tickets
  • Analyze them with GPT to find urgency
  • Assign them to the right agent
  • Send an alert on high-priority issues

Instead of coding each step, let GPT decide what to do — by giving it tools.

Tech Stack

  • OpenAI Assistants API or Azure OpenAI + Functions
  • .NET 10 Web API
  • SQL Server or Azure Table Storage
  • Twilio/SendGrid/Slack for notifications
  • Optional: LangChain.NET or Semantic Kernel

Step 1: Define the Tools

public class TicketTool : ITool { public string Name => "fetch_support_tickets"; public async Task<string> ExecuteAsync(string input) { var recent = await dbContext.Tickets .Where(t => t.Status == "Open") .OrderByDescending(t => t.CreatedAt) .Take(10) .ToListAsync(); return JsonConvert.SerializeObject(recent); } }
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public class AlertTool : ITool { public string Name => "send_alert"; public Task<string> ExecuteAsync(string input) { var alert = JsonConvert.DeserializeObject<AlertMessage>(input); return SlackNotifier.SendAsync(alert.Text); } }
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Step 2: Configure GPT Agent

var agent = new GptAssistant("gpt-4", apiKey); agent.AddMemory("This agent triages support tickets and alerts on urgent ones."); agent.RegisterTool(new TicketTool()); agent.RegisterTool(new AlertTool()); await agent.ExecuteAsync("Check latest support tickets and alert if urgency is high.");
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The model fetches tickets, analyzes them for urgency using sentiment/context, and calls send_alert if needed — all without you hardcoding the logic.

Benefits of Autonomous Agents

  • Fewer if-else conditions — logic is inferred from intent
  • More scalable — agents can adapt as tools change
  • Lower code maintenance — updates happen at prompt/model level
  • More human-like — agents can explain actions, suggest improvements

Common .NET Use Cases

  • Finance: AI that reads transactions, flags anomalies, sends reports
  • Support: AI that tags/assigns tickets
  • HR: AI that pre-screens resumes, ranks applicants
  • CRM: AI that drafts follow-up emails or fills in missing lead data

Things to Watch

  • Prompt injection (sanitize input from users)
  • Audit logs for all tool usage
  • Rate limits and cost (track API usage)
  • Regulatory compliance (AI shouldn’t touch sensitive data blindly)

Conclusion

The age of AI copilots is evolving into the age of AI teammates — agents that do more than suggest. They act.

With .NET, GPT, and the new Assistants API, you can build production-grade, task-completing, autonomous agents — faster than ever before.

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