First and foremost, you need a goal, then you need the plan to achieve that goal. I cannot think of any scenario where one would do some work without either of those. Although AI and to some extent AI Agents have been around for a long time, they have only recently (2 years?) become mainstream […]
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As I was writing these other post and coding the agents, I was having some doubts about whether or not they agents were actually running or not. I did add some Terminal prompting but there was still some doubt. I needed to discover some additional perspectives on how to monitor and measure my AI Agents […]
Read More →In my last post Advanced multi-agent orchestration architectures I wrote about Hub-and-Spoke, Hierarchical, and Supervisor patterns. In this article, as the title makes clear I will get a bit deeper into hub-and-spoke. But before that, as I was writing the hub and spoke code I learned a few hard lessons. I share them in case […]
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In a previous article, The difference between Agentic AI and Multi-Agent Architectures, I touched on multi-agent systems and briefly on pattern selection. This article will discuss those 3 patterns in more detail. In this article, Orchestrate a multi-agent solution using the Microsoft Agent Framework flat / foundational orchestration patterns were discussed. They are Concurrent, Sequential, […]
Read More →Every multi-agent system is agentic, every agentic system is not multi-agent. You can see textually some differences between them in Table 1 and Figure 1. An Agentic system is one which would respond to and infer the following prompt: The autonomous Agentic AI chooses the action instead of executing a pipeline or multiple agents. The […]
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As I consume AI NLP responses I very often accept without much resistance, second guessing, or contemplation for or against the result presented to me. I simply read it and if I agree, I take it as true and continue forward with my activity. If I don’t agree I tend to respond with my doubt […]
Read More →I remember way back when I was coding web applications that ran on a single server that as the consumption and usage of that web application grew, we ran out of CPU and memory. This required us to add another server and setup a, what we call now, a web farm. But what happened was […]
Read More →Agent instructions are system prompts which define identity, purpose, goals, behavioral guidelines, tone, style, safety constraints, knowledge, capability boundaries, tool usage rules, output formatting requirements, task prioritization, domain specific knowledge, and reasoning instructions to name most. This is why we should never overlook the non-code aspect of building AI Agents and AI solutions. The manner […]
Read More →As Azure AI Foundry adoption grows, developers are increasingly faced with an important architectural decision: The answer depends on where your agent lives and how you want to manage it. Although both SDKs are part of the Azure AI ecosystem, they solve different problems. Understanding the distinction can save significant development effort and help you […]
Read More →There are numerous ways to optimize model performance. Retrieval Augmented Generation (RAG) and Prompt Engineering are 2 relevant strategies, but also consider fine-tuning a model. Fine-tuning influences the model with additional examples that reflect specific requirements. These examples adjust the pretrained internal weights so that it produces responses consistent with patterns in the training data. […]
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