Category Archives: AI
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
Posted in AI, C#, Essentials, Programming
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Migrating Agentic Code Python -> C# Part 1
In the last 5 posts we created an agentic application using Python. Let’s migrate that to C#. Here’s the set of files we’ll create: And here is the output after running it as a test using the prompt Use of … Continue reading
Posted in AI, C#, Programming, Python
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Creating a multi-agent application – Part 5 (final)
In part 4 of this series we created our final two agents. In this final part of the series we’ll review the workflow that we create with the StateGraph class of LangGraph.
Posted in AI, Programming, Python
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Creating a multi-agent application – Part 4
In part 3 we looked at creating the researcher. As promised, today we’ll look at the author. You’ll notice in the following code a great deal of similarity to what we’ve seen before. The goal is to create a code … Continue reading
Posted in Agents, AI, Programming, Python
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Creating a multi-agent application — Part 3
In the previous post, we examined how to load the libraries we need and how to create the Blogger agent. In this post, we’ll examine the Research agent. You’ll no doubt notice the pattern of defining the template, the agent … Continue reading
Posted in AI, Programming, Python
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Creating a multi-agent application – Part 1
The following text was created by a multi-agent application designed to create blog posts. In my next post we’ll take the application apart, step by step. For now, here is a test run with the prompt Use of multiagents in … Continue reading
Posted in AI
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ReAct and Agents in AI
In the previous post, we looked at the use of Chain of Thought (CoT) reasoning in the context of LLMs. For an LLM to take action in the world, however, it needs agents. The paradigm for this is called ReAct—that … Continue reading
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
Posted in Agents, AI, Essentials
Tagged Agentics, AI, Chain-of-thought
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PEAS for Agent AI
A classic AI framework to define an agent’s task environment is PEAS. It stands for:
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
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
RAG – A Quick Example
In the previous blog post, we imported a few Python modules and configured our AI key, using Colab. In this blog post we’ll use Retrieval-Augmented Generation (RAG) to extend an LLM that we’ll get from OpenAI. I’ll use a number … Continue reading
