You know what’s cool? Having agents talk to each other and letting them figure out how to get to the answer you’re looking for.
One way to do this is by using the Agent-to-Agent protocol in your application. Version 0.3.0 is the latest released version, and there’s an RC v1.0 available already.
The Microsoft Agent Framework (MAF) also has an implementation of this protocol available. The current version of the MAF packages, 1.0.0b260130 at the time of writing, isn’t compatible with the proposed changes of 1.0, but I’m pretty sure this will be supported in upcoming releases. The team is adding and changing features quite quickly. There are also newer versions of MAF available now, but I have yet to validate those.

In my current project, we’re creating a dozen agents, each doing its own little thing. What we could do is create some workflow or state machine, invoking each agent in turn, much like the good old days. However, we don’t always need to run every agent or run them in a specific order. While it is possible to add this dynamic nature to an application, we can also leverage the power of a language model for this. Based on the knowledge of what an agent can do, the language model can figure out which agents to invoke and in what order. The A2A protocol can help here.

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.