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This tutorial by Sharan Babu Paramasivam Murugesan guides readers through building AI agents using Semantic Kernel, a framework that bridges large language models with code and data. The article demonstrates creating a greeting agent that adapts to user location and time, showcasing how plugins can integrate real-world data. It also covers observability techniques like logging filters and OpenTelemetry for monitoring performance. Readers will gain practical insights into structuring agents with plugins, managing context, and scaling systems. This guide is valuable for developers seeking to implement production-ready AI solutions with Semantic Kernel.
Key facts
- Semantic Kernel connects large language models with code, data, and external systems to create functional AI agents.
- A sample agent uses location, time zone, and time-of-day plugins to deliver personalized greetings based on user context.
- Logging filters and OpenTelemetry enable real-time monitoring of function invocations and performance metrics.
- Advanced concepts like external memory stores (e.g., Redis) allow agents to retain user context across sessions.
- The tutorial emphasizes security, scalability, and token-aware workflows for production-ready AI implementations.
TAGS:
#AI agents #C# #Development #machine learning #Observability #OpenTelemetry #Plugins #Semantic Kernel #Tutorial
