In this final post, we will explore how to instrument an AI agent built with Semantic Kernel. As useful as an application may be, it is indeed nearly useless without proper monitoring capabilities.
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In the previous post, we saw that calling a separate API for each plugin can quickly become a cumbersome and error-prone task. In this post, we’ll explore how to overcome this limitation by using a dedicated protocol—MCP.
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In this post, we’ll tap into the real power of Semantic Kernel: moving beyond its role as a simple orchestrator. Enter plugins, and get ready to experience AI on steroids.
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In this post, we'll explore what Ollama is and how it allows us to run language models locally, without relying on any cloud platform.
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In this post, we'll set up Semantic Kernel and see it in action through a simple example. Specifically, we'll connect it to Azure OpenAI and try querying it with a few basic prompts.
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Before diving into the nitty-gritty details of Semantic Kernel, this post will first focus on understanding the context in which it operates and the problems it aims to address.
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Starting today, we’re launching a series that dives into a timely and important topic: empowering AI within our applications. While there are plenty of tutorials available online, this series will specifically focus on using Semantic Kernel, Microsoft’s tool for building enterprise-grade AI agents.
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In this post, we’ll explore how to strengthen the security of our web applications when accessing credentials for resources hosted outside of Azure.
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In this post, we'll discover how to enhance the security of our web applications hosted on Azure by leveraging managed identities.
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In this post, we explore why the traditional method of storing credentials in web applications can pose significant security risks.
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