The promise of AI-driven automation is compelling: agents that can monitor systems, make decisions, and execute actions with minimal human intervention. But for enterprises with mature .NET applications built over decades, the idea of "adding AI" often feels like it requires a costly, risky ground-up rewrite. This is a misconception. Modern AI agent frameworks, particularly Microsoft's Semantic Kernel and the Model Context Protocol (MCP), are specifically designed to integrate with existing systems through well-defined interfaces.
This guide outlines a pragmatic, low-risk strategy for adding intelligent automation to legacy .NET applications. The goal is to enhance your systems incrementally, not replace them wholesale.
1. 🔍 The Core Principle: Agents Do Not Replace Systems, They Orchestrate Them
The first mental shift is understanding what an AI agent actually is. It is not a new application. It is an orchestration layer that sits on top of your existing applications and services. Your legacy .NET systems remain the "source of truth" and the execution engines. The AI agent is the brain that decides what to call and when, based on high-level goals or user requests.
Think of it as a highly intelligent, adaptive workflow engine. Instead of hard-coding a sequence of steps (if X, then call API Y, then update database Z), you describe the goal ("Process this customer refund"), and the agent dynamically figures out the steps by selecting from a library of available tools (your existing APIs).
2. 🛠️ Step One: Expose Your Legacy Logic as Skills (APIs)
For an AI agent to interact with your .NET systems, those systems must be accessible via well-defined interfaces. This typically means REST APIs, but could also include message queues, gRPC, or even direct database access (though APIs are strongly preferred for safety and maintainability).
The Pragmatic Approach: The Façade Pattern
If your legacy code is a monolith (e.g., a large ASP.NET Web Forms app or a WCF service), you do not need to refactor the entire application. Instead, use the Façade design pattern.
- Create a Thin API Layer: Build a new, lightweight ASP.NET Core Web API project. This project's sole job is to expose specific business functions from your legacy code.
- Encapsulate the Complexity: Each API endpoint is a simple, purpose-built wrapper around a complex legacy function. For example, you might create a
POST /orders/refundendpoint that internally calls a deeply nested method in your old order processing DLL. - Minimal Changes to Legacy Code: The only modification to your legacy system is to make the core business logic accessible to this new API layer. Often, this is just a matter of setting the right access modifiers (e.g., making a class
publicinstead ofinternal).
This API layer becomes your agent's "skill library." Each endpoint is a tool the agent can use.
3. 🧠 Step Two: Build the Agent with Semantic Kernel
With your legacy systems exposed as APIs, you can now build the AI agent. Using Microsoft's Semantic Kernel (a .NET library), you define "Skills" and "Planners."
Defining Skills in Semantic Kernel
A "Skill" is a C# class that contains one or more methods. Each method is a function the agent can call. For interacting with your legacy APIs, you write simple HttpClient-based methods. For example:
- A
ProcessRefundskill that calls yourPOST /orders/refundAPI. - A
GetCustomerDetailsskill that calls yourGET /customers/{id}API. - A
SendEmailNotificationskill that integrates with your notification service.
Semantic Kernel automatically generates a description of each skill based on your method signatures and XML comments. This description is what the AI will "read" to understand what each tool does.
The Planner: Dynamic Workflow Generation
The power of Semantic Kernel is the Planner. Instead of you telling the AI exactly which skills to use and in what order, the Planner does it for you. Given a high-level goal (e.g., "Summarize the latest email from my boss and send a digest to the team"), the Planner analyzes the available skills and dynamically creates a step-by-step plan to achieve the goal.
For example, if the goal is to process a refund for a customer, the Planner might create a sequence like: GetCustomerDetails → ValidateRefundEligibility → ProcessRefund → SendEmailNotification. This dynamic planning is what makes the agent truly autonomous.
4. 🔐 Step Three: Ensure Security, Governance, and Control
Allowing an AI to autonomously call your production APIs is powerful but risky. A robust implementation must include safeguards:
A. Skill-Level Permissions
Not every agent should have access to every skill. Use role-based access control (RBAC) to define which agents or users can invoke sensitive operations. For instance, an agent helping with customer support should have read-only access to customer data but require human approval for refunds.
B. Plan Validation
Before the Planner executes a plan, run it through a validation step. Does the plan make sense? Does it involve a dangerous combination of actions (e.g., deleting a customer record followed by a refund)? This is your "sanity check" layer.
C. Comprehensive Logging and Auditing
Log every plan, every skill invocation, and every result. This is not just for debugging; it is for compliance. If the AI makes a mistake or a decision is questioned, you need a complete audit trail showing exactly what the agent did and why.
5. 📈 The Business Case: Incremental Value, Minimal Risk
This approach is powerful because it is incremental. You do not need to replace your entire system or bet the company on a risky AI project. Instead:
- Start Small: Pick one high-value, repetitive task (e.g., processing routine support requests). Build an agent for just that workflow.
- Prove Value: Measure the time savings, error reduction, and customer satisfaction improvement.
- Expand Gradually: As you gain confidence, expose more of your legacy systems and give the agent more skills. Over time, you build a comprehensive intelligent automation layer.
By treating your legacy .NET applications as valuable, stable platforms and layering intelligent orchestration on top, you unlock the benefits of AI without the enormous cost and risk of a rewrite. At Smaltsoft, this is the approach we champion. We help enterprises build agent frameworks that integrate seamlessly with decades of .NET investment, turning legacy systems into intelligent, adaptive platforms.