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, […]
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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 →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 […]
Read More →I have built a Retrieval Augmented Generation (RAG) solution using the Azure AI Search SDK and Azure OpenAI SDK. There is a lot to know about not only coding, configuring, and managing those components, but how they must be tuned, fed, and optimized are aspects which we are still in a state of learning and […]
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I recently wrote this article Integrate custom tools into an AI Agent which illustrates how to configure tools and an AI Agent to call those tools. In that article there was no Model Context Protocol (MCP) capability utilized. MCP provides the following benefits when compared to integrating custom tools directly from code. Dynamic tool discovery […]
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I recently wrote this article Integrate custom tools into an AI Agent which illustrates how to configure tools and an AI Agent to call those tools. In that article there was no Model Context Protocol (MCP) capability utilized. MCP provides the following benefits when compared to integrating custom tools directly from code. Dynamic tool discovery […]
Read More →Before learning what I discovered while integrating tools into an AI Agent, I was a bit perplexed regarding how the LLM was able to determine which tool to use and even which tools are available for use. Well, how it works is not a mystery, it is clearly described in the code. In this post […]
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