Skip to main content

Debugging Semantic Kernel in C#

I'm working on a (small) project where I'm using Microsoft Semantic Kernel. Although it makes it really easy to build your own copilots and AI agents, it is not that easy to understand what is going on (or what is going wrong).

Just in case you have no clue what Semantic Kernel is:

Semantic Kernel is an open-source SDK that lets you easily build agents that can call your existing code. It is highly extensible and  you can use Semantic Kernel with models from OpenAI, Azure OpenAI, Hugging Face, and more. It can be used in C#, Python and Java code.

After adding the Microsoft.Semantic.Kernel.Core and Microsoft.Semantic.Kernel.Connectors.OpenAI nuget packages, the simplest implementation to write is the following:

That’s easy and there is not much magic going on as you are directly controlling the prompt that is shared with the LLM.

However when you start to use the more complex features like the planner, you don't know what exactly is shared with the LLM. In that case it can be useful to enable debugging. Therefore update the kernel configuration code to add logging:

If we now run our Semantic Kernel enabled application, we can see the prompt that is send to the LLM in the output window:


Happy AI coding!

Labels:

Popular posts from this blog

Podman– Command execution failed with exit code 125

After updating WSL on one of the developer machines, Podman failed to work. When we took a look through Podman Desktop, we noticed that Podman had stopped running and returned the following error message: Error: Command execution failed with exit code 125 Here are the steps we tried to fix the issue: We started by running podman info to get some extra details on what could be wrong: >podman info OS: windows/amd64 provider: wsl version: 5.3.1 Cannot connect to Podman. Please verify your connection to the Linux system using `podman system connection list`, or try `podman machine init` and `podman machine start` to manage a new Linux VM Error: unable to connect to Podman socket: failed to connect: dial tcp 127.0.0.1:2655: connectex: No connection could be made because the target machine actively refused it. That makes sense as the podman VM was not running. Let’s check the VM: >podman machine list NAME         ...
Read more

Cache stampede: when our cache turned against us

While investigating some performance issues, we ran into an ASP.NET Core API that cached a fairly expensive aggregation query for 60 seconds. Under normal load, that was fine: one request rebuilds the cache, everyone else reads from it. Under peak load, dozens of requests would arrive in that same expiry window, all see a cache miss, and all fire the same expensive query in parallel. The database didn't like that. That was the moment when our caching layer stopped helping and started hurting. A burst of requests comes in at the same time, all miss the cache, and all go hammer the database or the downstream API at once. That's a cache stampede . The cache was supposed to protect our backend, and for a few hundred milliseconds it did the opposite. Why this happens IMemoryCache.GetOrCreate (and its async sibling) looks like it protects you, but it doesn't add any locking on its own. Look at the naive version: public async Task<Report> GetReportAsync(string key) ...
Read more

A complex system designed from scratch never works

A few years ago, I worked as an architect on a big mainframe rewrite. I still count it as one of my failures. Not because the technology was wrong, but because I couldn't convince the management team to simplify the approach. Years later, the organization is still struggling to get the new system up and running. I left the project at the time, because I couldn't put my name behind an approach that would take very long and cost a lot of money without a working system to show for it along the way. Gall’s Law That memory keeps coming back to me, because it's a textbook case of Gall's Law playing out in real life. Gall's Law , from John Gall's Systemantics , states it plainly: A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works, and it cannot be patched to make it work. You have to start over with a simple system that works. What does that mean in practice,...
Read more