Build a production-ready Research Assistant AI agent using Python. Complete tutorial covering async patterns, @ai_function decorators, multi-turn conversations, and best practices.
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Observability Practices in AI Engineering: A Complete Guide to LLM Monitoring
Master AI observability with this comprehensive guide. Compare Langfuse, Helicone, LangSmith, and other tools. Learn which metrics matter, how to build evaluation pipelines, and implement production-grade monitoring for LLM applications.
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Build a production-ready Customer Support AI agent using C# and .NET 8. Complete tutorial covering project setup, tools, multi-turn conversations, middleware, and error handling.
Read more →Evaluating Agent Performance: Metrics and Testing Strategies
Evaluating agent performance is harder than evaluating models. After developing evaluation frameworks for 10+ agent systems, I’ve learned what metrics matter and how to test effectively. Here’s the complete guide to evaluating agent performance. Figure 1: Agent Evaluation Metrics Framework Why Agent Evaluation is Different Agent evaluation is more complex than model evaluation: Multi-step reasoning: […]
Read more →Frontend State Management for AI Applications: Redux, Zustand, and Jotai Patterns
Frontend State Management for AI Applications: Redux, Zustand, and Jotai Patterns Expert Guide to Choosing and Implementing State Management for AI-Powered Frontends I’ve built AI applications with Redux, Zustand, Jotai, Context API, and even plain React state. Each has its place, but for AI applications—with their streaming updates, complex conversation state, and real-time interactions—the choice […]
Read more →Introduction to Microsoft Agent Framework: The Open-Source Engine for Agentic AI Apps (Part 1)
Learn about Microsoft Agent Framework (MAF), the unified open-source SDK for building production-ready AI agents. This comprehensive guide covers the architecture, key features, and how MAF combines the best of Semantic Kernel and AutoGen for enterprise agentic AI development.
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