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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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 →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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Artificial Intelligence (AI) is more than OpenAI or Anthropic who have created some very awesome LLMs that can do amazing things, especially with Natural Language Processing (NLP). AI can also extract information from documents, convert text-to-speech and speech-to-text, provide metadata and summaries of images and videos, of course AI Agents. All of the AI capabilities […]
Read More →The Agent-to-Agent (A2A) protocol is a method for utilizing AI Agents which are not cohosted together. For example, you have AI Agent X located at an HTTPS address #1 and AI Agent Y hosted at HTTPS address #2 and AI Agent Z that runs from a Chat and needs to utilize X and Y. This […]
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The Microsoft Agent Framework is an open-sourced SDK which combines the Semantic Kernel and AutoGen into a single, highly innovative set of AI capabilities into a single library. AI features like: Unified model connectivity and chat clients for OpenAI, Azure OpenAI, Anthropic, Ollama, and other leading LLM providers. Multi-agent orchestration, allowing specialized AI agents to […]
Read More →You may have noticed that in my previous articles I used Azure AI Projects client library for Python – version 2.5.0 library. Those articles illustrate how to build AI Agents, MCP Tools, and infer instructions using an LLM. Integrate custom tools into an AI Agent Integrate MCP Tools with AI Agents – Remote MCP server […]
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