In an earlier post I wrote about using Semantic Kernel to create an Agentic AI solution, all using C#. Of course, similar flows can be created with Python. To try this, I’ve created a sample solution to update a resume so it’s more likely to pass the ATS requirements used by various companies nowadays.

My sample is heavilly inspired by Gian Paolo Santopaolo his CV-Pilot repository, which I was not able to use due to the CrewAI tooling phoning home and my DNS (PiHole) blocking those requests. Even after disabling the tracking features, the library/ies still tracked ‘something’, which caused the logic to break so I decided to create something myself using Semantic Kernel.

What did I create?

A tool/flow to update a resume so it can pass the ATS (Applicant Tracking System), based on the job description. These systems ofen check for specific wording and skills. For a human, it takes quite a bit of time to (re)write a resume to pass the ATS requirements. It’s the perfect job for a LLM to perform, as it’s built to create text based on other text input.

You can try doing this in a single prompt, but there’s a high probability this won’t perform the way you like. In my sample, I’ve created a ‘Project Manager’-, ‘Job Market Analyst’- and ‘Strategist’-agent. Each with their own specific job and goals.

We are finally at a state in the GenAI-space where we can create agentic AI solutions with ease.
I’m most familiar with Semantic Kernel, when working with LLMs, and this library works great for creating these solutions.

In a nutshell, what you need to do is create a group chat, add your agents to it, and then let them work together to solve a problem.
Do keep in mind, at the time of this writing, version 1.58.0 of the Semantic Kernel library is used. Development is going fast in this space, so behavior might change in future versions.
For my proof of concept, I’ve created a simple solution capable of creating a Fibonacci sequence, validate if a number is part of that sequence or answer random questions you would also ask to a regular LLM-powered chatbot. If you’re interested, the full sourcecode can be found on GitHub in my console-agent repository.

Create the agents

Agents are created with the ChatCompletionAgent class. You should provide the instructions & the kernel to use.
The instructions is just a regular prompt we all know and love when working with an LLM and specifies what the agent should do. I won’t repeat it over here as it’s not relevant for this post.