Building Agentic AI with .NET and Microsoft Semantic Kernel

Quick Answer

Building Agentic AI with .NET and Microsoft Semantic Kernel in production typically fails due to poor configuration, missing observability, and unsafe rollout patterns. This guide explains why failures happen, how to fix them, common mistakes, and practical steps that improve scalability without adding unnecessary complexity.

Table of Contents

• Problem Framing: Building Agentic AI with .NET and Microsoft Semantic Kernel
• Real-World Example: Autonomous Robot
• Trade-Offs: Complexity vs Maintainability
• Decision Guide: Choosing the Right Tools and Frameworks
• Performance Considerations
• Scalability Notes
• When This Fails in Production
• Common Mistakes Engineers Make
• Better Approach Based on Experience
• Related Articles
• What breaks in production
• Debugging Agentic AI Systems
• Best Practices for Agentic AI Development

Problem Framing: Building Agentic AI with .NET and Microsoft Semantic Kernel

Agentic AI refers to the development of artificial intelligence systems that can act autonomously, making decisions and taking actions based on their environment and goals. In recent years, there has been a growing interest in building Agentic AI Systems that can interact with humans and other agents in complex ways. However, building Agentic AI Systems is not without its challenges, and it's essential to understand the trade-offs and considerations involved. When I'd choose to build Agentic AI over traditional AI, I'd consider the need for autonomy, adaptability, and complex decision-making.

When I'd avoid building Agentic AI, I'd consider the complexity and maintainability requirements, as well as the potential risks and challenges involved. What I'd avoid is over-engineering the system, which can lead to complexity and maintainability issues.

Real-World Example: Autonomous Robot

Let's consider a real-world example of an autonomous robot that can navigate and interact with its environment. The robot is equipped with sensors, cameras, and actuators that enable it to perceive its surroundings and take actions accordingly. The robot's AI system is designed to learn from its experiences, adapt to new situations, and make decisions based on its goals and objectives.

Trade-Offs: Complexity vs Maintainability

When building Agentic AI Systems, there are several trade-offs to consider. One of the key trade-offs is between complexity and maintainability. As the system becomes more complex, it becomes increasingly difficult to maintain and update. However, if the system is too simple, it may not be able to handle complex situations and adapt to changing circumstances. To mitigate this trade-off, I'd recommend using modular design patterns and focusing on maintainability from the outset.

Decision Guide: Choosing the Right Tools and Frameworks

So, how do you choose the right tools and frameworks for building Agentic AI Systems? Here are some guidelines to consider:

• Define clear goals and objectives for the system.
• Choose the right tools and frameworks for the job, such as the Microsoft Semantic Kernel and the .NET Core platform.
• Ensure that the system is designed with scalability, flexibility, and maintainability in mind.

Performance Considerations

When building Agentic AI Systems, performance considerations are crucial. The system should be designed to handle complex situations and adapt to changing circumstances without compromising its performance. This can be achieved by using efficient data structures, algorithms, and programming paradigms. When I'd choose to use a specific algorithm or data structure, I'd consider the trade-offs between performance, maintainability, and scalability.

Scalability Notes

As the system grows and becomes more complex, scalability becomes a critical consideration. The system should be designed to handle increased loads and data without compromising its performance. This can be achieved by using distributed systems, load balancing, and caching. What I'd avoid is using a single-point-of-failure architecture, which can lead to scalability issues.

When This Fails in Production

When building Agentic AI Systems, it's essential to consider the potential risks and challenges involved. If the system fails in production, it can have severe consequences, including downtime, data loss, and financial losses. To mitigate these risks, it's essential to have a robust testing and validation process in place, as well as a plan for disaster recovery and business continuity.

Common Mistakes Engineers Make

When building Agentic AI Systems, there are several common mistakes that engineers make. These include:

• Over-engineering the system, which can lead to complexity and maintainability issues.
• Not considering the potential risks and challenges involved, which can lead to severe consequences.
• Not designing the system with scalability, flexibility, and maintainability in mind, which can lead to technical debt and performance issues.

Better Approach Based on Experience

Based on our experience building Agentic AI Systems, we recommend the following approach:

• Define clear goals and objectives for the system.
• Choose the right tools and frameworks for the job, such as the Microsoft Semantic Kernel and the .NET Core platform.
• Ensure that the system is designed with scalability, flexibility, and maintainability in mind.
• Have a robust testing and validation process in place.
• Have a plan for disaster recovery and business continuity.

using Microsoft.SemanticKernel;
  using System;

  namespace AgenticAI
  {
      public class Agent
      {
          public void Initialize()
          {
              // Initialize the agent
          }

          public void Update()
          {
              // Update the agent state
          }

          public void Act()
          {
              // Take action based on the agent state
          }
      }

      public class AutonomousRobot : Agent
      {
          public void Navigate()
          {
              // Navigate the robot
          }

          public void Interact()
          {
              // Interact with the environment
          }
      }
  }
  

What breaks in production

Teams often discuss similar failures on GitHub or Stack Overflow, where they share their experiences and solutions to common problems. For instance, a team may encounter issues with the Microsoft Semantic Kernel when deploying their Agentic AI system to production. They may find that the system is not handling errors properly, or that the performance is degrading over time. By sharing their experiences and solutions, teams can learn from each other and improve their systems.

Debugging Agentic AI Systems

Debugging Agentic AI systems can be challenging due to their complexity and autonomy. However, there are several techniques that can be used to debug these systems. One approach is to use logging and monitoring tools to track the system's behavior and identify potential issues. Another approach is to use simulation-based testing, where the system is tested in a simulated environment to identify potential problems. By using these techniques, developers can identify and fix issues in their Agentic AI systems, ensuring that they are reliable and performant.

Best Practices for Agentic AI Development

When developing Agentic AI systems, there are several best practices that should be followed. These include:

• Define clear goals and objectives for the system.
• Choose the right tools and frameworks for the job.
• Ensure that the system is designed with scalability, flexibility, and maintainability in mind.
• Have a robust testing and validation process in place.
• Have a plan for disaster recovery and business continuity.

Related Articles

• Unlock Agentic AI: Free Course with Certificate
• Agentic AI vs AI Agents: Key Differences and Applications
• Fixing Agentic AI in .NET and C#: A Guide to Observability and Rollout