Category Archives: Essentials
An Overview of Agent Memory for .NET
The ability for Microsoft Agent Framework agents to retain and utilize knowledge across interactions is critical. One solution for this is Agent Memory for .NET, a cutting-edge, mind-blowing, graph-native memory engine that leverages the robust capabilities of Neo4j as its … Continue reading
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ChatClient Middleware vs. Agent Middleware
As noted in a previous post, middleware plays a pivotal role in enhancing the functionality and observability of agents. The Microsoft Agent Framework utilizes two primary types of middleware: ChatClient Middleware and Agent Middleware. Understanding the distinctions between these two … Continue reading
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Agentic Table of Contents (so far)
RAG – A quick example RAG in detail Deeper into RAG The R in RAG PEAS for Agentic AI AI Reasoning and Planning REACT and Agents in AI Creating a multi-agent system (Python & LangChain/LangGraph) part 1 of 6 Migrating … Continue reading
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Limit token usage in Microsoft Agent Framework
In the rapidly evolving landscape of artificial intelligence, managing costs associated with API usage is a critical concern for developers. The Microsoft Agent Framework offers a powerful tool in this regard: ChatClientAgentRunOptions. This component allows developers to cap the number … Continue reading
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Dependency Injection & Agent Framework
In the previous blog posts we ported a Python implementation of an agentic application to C# and Microsoft Agent Framework. We used interfaces, but we did not use Dependency Injection (DI). It is pretty easy to add. Agents, tools, executors … Continue reading
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Migrating Agentic Code Python -> C# Part 2
In Part 1 of this multi-part series, I laid out my goal to migrate the Python agentics program from the previous series to C#. To do this migration I’m going to work my way down through my Python script and … Continue reading
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Creating a multi-agent application. Part 2
In my previous post, I showed the output of a multi-agent application I wrote to create blog posts (not to worry, it is for demonstration purposes only). In this post, I will begin the process of working through the code, … Continue reading
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AI Reasoning and Planning
Until very recently, it was observed that LLMs had a very hard time with complex problems. Context was lost, memory of previous steps was distorted, and so forth. This led to unreliable results (hallucinations) and, consequently, to a lack of … Continue reading
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Tagged Agentics, AI, Chain-of-thought
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The R in RAG
In my previous post we looked at saving to the vector store. In this short post we’ll look at retrieving that information. The simple search is a good starting point and depends on writing a good prompt, but we can … Continue reading
Deeper into RAG
In the previous post we walked through creating a RAG example, line by line. Let’s take a closer conceptual look at the steps involved in creating a RAG
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RAG In Detail
In my previous post I walked through a RAG example but glossed over the details. In this post I’ll back up and walk through the program line by line. The key steps in RAG are
Mads Torgersen
Mads (Lead Designer of C#) joins me to discuss C# and AI as well as what to expect in C# 15. PodcastVideo
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