Agentic AI with C# Using Semantic Kernel: Designing Intelligent, Autonomous Systems

In Part 1, we explored how Generative AI fits into enterprise architecture and why C# is a strong choice for production-grade Gen AI systems.
In this article, we go one step further—into Agentic AI.

Agentic AI systems do not just respond to prompts. They:

• Reason over goals

• Decide which actions to take

• Call tools or services

• Maintain context and memory

• Execute multi-step workflows autonomously

With Semantic Kernel, C# developers can design controlled, enterprise-safe AI agents without sacrificing architectural discipline.

What Is Agentic AI?

Agentic AI refers to AI systems that can:

• Understand an objective

• Break it into steps

• Decide which tools to use

• Execute actions

• Evaluate results

• Continue until the goal is achieved

Unlike traditional chatbots, agents act as decision-makers rather than just responders.

Example Goals

• “Validate customer KYC and explain failure reasons”

• “Analyze a claim and recommend next actions”

• “Generate a summary and store it in the system”

• “Answer user questions using enterprise data”

Why Agentic AI Matters in Enterprise Systems

Enterprise systems are:

• Rule-driven

• Security-sensitive

• Workflow-oriented

• Auditable

Pure LLM-based responses are not sufficient.

Agentic AI enables:

• Controlled decision-making

• Deterministic tool execution

• Business-rule enforcement

• Observability and logging

This is where C# + Semantic Kernel fits perfectly.

What Is Semantic Kernel?

Semantic Kernel (SK) is Microsoft’s AI orchestration SDK that allows developers to combine:

• Prompts

• Native C# functions

• Memory

• Planning

• Tool invocation

It acts as the AI brain coordinator, not the AI model itself.

Think of it as:

“Dependency Injection + Orchestration + AI Reasoning”

Core Building Blocks of Semantic Kernel

1. Kernel (The AI Runtime)

The Kernel is the central orchestrator:

• Connects to LLMs

• Registers tools

• Manages execution flow

var kernel = Kernel.CreateBuilder()
    .AddAzureOpenAIChatCompletion(
        deploymentName,
        endpoint,
        apiKey)
    .Build();

2. Plugins (Tools the Agent Can Use)

Plugins are real C# methods that the AI can invoke.

public class KycPlugin
{
    [KernelFunction]
    public string ValidateCustomer(string customerId)
    {
        if (string.IsNullOrEmpty(customerId))
            return "KYC Failed: Customer ID missing";

        return "KYC Valid";
    }
}

Registering the plugin:

kernel.ImportPluginFromObject(new KycPlugin(), "Kyc");

Now the AI can decide when to call this function.

3. Prompts (Reasoning Instructions)

Prompts define how the agent thinks, not just what it answers.

var prompt = """
You are a banking assistant.
Decide whether to validate KYC.
If validation fails, explain the reason clearly.
""";

Semantic Kernel allows prompt + function calling together, enabling real agent behavior.

From Chatbot to Agent: Execution Flow

Traditional Chatbot

User → Prompt → LLM → Text Response

Agentic AI with Semantic Kernel

User Goal
   ↓
Reasoning
   ↓
Tool Selection
   ↓
C# Function Execution
   ↓
Evaluation
   ↓
Final Response

This loop is what makes the system agentic.

Example: Simple Agent Execution

var function = kernel.CreateFunctionFromPrompt(prompt);

var result = await kernel.InvokeAsync(function, new()
{
    ["customerId"] = "CUST123"
});

Console.WriteLine(result);

The agent:

1. Reads the goal

2. Decides to call ValidateCustomer

3. Uses the result

4. Produces a controlled response

Why C# Is Ideal for Agentic AI

1. Strong Control Over AI Behavior

• Native methods enforce rules

• AI cannot bypass validations

• Business logic remains deterministic

2. Enterprise Observability

With C#, you can:

• Log every tool call

• Trace decisions

• Audit AI behavior

• Apply exception handling

This is critical for regulated domains.

3. Clean Architecture Compatibility

Agentic AI fits naturally into:

• Application layer (orchestration)

• Domain layer (rules)

• Infrastructure layer (AI + data)

No architectural compromise is required.

Common Enterprise Use Cases

• KYC validation agents

• Insurance claim assessment agents

• Customer support copilots

• Document processing agents

• Policy explanation assistants

• AI-driven workflow automation

C# vs Python for Agentic AI

Area	C#	Python
Tool Governance	Strong	Moderate
Architecture	Clean & layered	Flexible but loose
Security	Enterprise-ready	Needs extra controls
Observability	Excellent	Limited
Production Agents	Ideal	Risky without structure

Python is excellent for research agents.

C# is better for enterprise agents.

Best Practices for Agentic AI in C#

1. Never allow AI to execute uncontrolled actions

2. Keep business rules outside prompts

3. Log every agent decision

4. Limit tool access explicitly

5. Treat prompts as configuration, not logic

6. Validate AI outputs before acting on them

Conclusion

Agentic AI represents the next evolution of enterprise AI systems.

With Semantic Kernel and C#, architects can design AI agents that are:

• Intelligent

• Autonomous

• Secure

• Observable

• Production-ready

Rather than replacing traditional architecture, Agentic AI enhances it, allowing AI to operate within well-defined enterprise boundaries.

For organizations serious about adopting Generative AI at scale, C# + Semantic Kernel is a powerful and practical foundation.