Building Production-Ready AI Agents with .NET and MCP
A deep dive into creating robust, observable AI agents using the Model Context Protocol in .NET. Covers agent lifecycle, tool registration, error handling, and deployment patterns.
Technical articles on backend engineering, AI, distributed systems, and more.
A deep dive into creating robust, observable AI agents using the Model Context Protocol in .NET. Covers agent lifecycle, tool registration, error handling, and deployment patterns.
Exploring event-driven patterns for building resilient distributed systems. Covers event sourcing, CQRS, saga patterns, and practical Kafka implementation strategies.
Master Redis caching patterns for .NET applications. Covers distributed caching, cache-aside, write-through, refresh-ahead, and advanced patterns like cache stampede prevention.
A comprehensive guide to building Retrieval-Augmented Generation systems. Covers chunking strategies, embedding models, vector databases, reranking, evaluation, and production hardening.
Implementing Clean Architecture in real-world .NET applications. Covers project structure, dependency rules, domain modeling, infrastructure, testing strategies, and common pitfalls.
Understanding MCP — the open standard for connecting AI models to tools and data. Covers protocol basics, server/client implementation, transport layers, and building custom MCP servers.