Jesse Liberty

Note, the complete source code for the .NET version of this demo application is now available at https://github.com/JesseLiberty/blogMigration—public

In the previous post we finished up creating our agents. You’ll remember that each of the agents declared nodes. We’re finally going to put them to use in a class BlogWorkflow. However, up to now we’ve not fully taken advantage of the Microsoft Agent Framework (MAF). Let’s fix that up first. To do so we’ll add a class BlogExecutors. We’ll create MAF workflow executors that will wrap existing “node” chains so that the business logic is reused unchanged.

Note: In this post we’re only going to move towards Microsoft Agent Framework from where things currently stand. To see this fully migrated to an application that truly utilizes MAF, navigate to here

Let’s start with the BloggerExecutor which will allow the blogger to plan the task and seed the sub-task:

using Microsoft.Agents.AI.Workflows;

namespace BlogMigration;

/// <summary>Entry executor: lets the blogger plan the task and seed the sub-task.</summary>
internal sealed partial class BloggerExecutor(IBloggerChain blogger) : Executor("Blogger")
{
    [MessageHandler]
    private async ValueTask<ResearchState> HandleAsync(ResearchState state, IWorkflowContext context)
        => await blogger.BloggerNodeAsync(state);
}

An executor is a node in Microsoft Agent Framework. Each executor can receive messages, process them and emit new messages. In this case, whenever the workflow engine routes a ResearchState message to the executor, this method is invoked. The method takes the current workflow state and the workflow context and returns the updated ResearchState.

The executor is just a thin wrapper around the Blogger agent. The handler calls the Blogger node and the node updates the state.

In the previous post we looked at implementing the Researcher in C#. In this, as promised, we’ll look at the Author and the Reviewer.

The Author is handed two objects when instantiated: the llm (an IChatClient object) and the chatOptions. Its primary method is InvokeAsync, which is passed the current ResearchState.

public class AuthorChain(IChatClient llm, ChatOptions chatOptions) : IAuthorChain
{
    public async Task<string> InvokeAsync(ResearchState state)
    {
        List<string> research = state.ResearchFindings;
        string researchText = research.Count > 0 ? string.Join("\n\n", research) : "No research available.";

Its expectation is that the state object will have research information from the Researcher. It creates its prompt based on the state and then sends that prompt, along with its options, to the llm. What it gets back is its first draft which it will pass to the Reviewer