Introduction: Agentic workflows represent a paradigm shift from simple prompt-response patterns to autonomous, goal-directed AI systems. Unlike traditional LLM applications where the model responds once and stops, agentic systems can plan multi-step solutions, execute actions, observe results, and iterate until the goal is achieved. This guide covers the core patterns that make agentic systems work: […]
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Building AI Agents: A Complete Code Review Assistant from Scratch
Hands-on tutorial building a production-ready AI agent. Create a code review assistant with tool use, error handling, caching, and GitHub integration.
Read more →The IDE Wars Are Over: How Visual Studio 2025 and Modern Developer Tools Changed Everything
🎓 AUTHORITY NOTE Based on 20+ years using every major IDE from Visual Studio .NET 2003 to today’s AI-powered tools. This represents hands-on experience leading teams through multiple IDE migrations and tool standardizations. Executive Summary Remember when developers would argue passionately about whether Visual Studio, VS Code, JetBrains, or Vim was the “right” choice? Those […]
Read more →Agentic AI Explained: Building Autonomous Systems That Plan, Act, and Learn
Move beyond simple chat to autonomous AI agents. Understand ReAct, multi-agent architectures, memory systems, and what actually works in production today.
Read more →Building Enterprise AI Applications with AWS Bedrock: What Two Years of Production Experience Taught Me
When AWS announced Bedrock in 2023, I was skeptical. Another managed AI service promising to simplify generative AI adoption? After two years of production deployments across financial services, healthcare, and retail, I’ve learned what actually matters when building enterprise AI applications. AWS Bedrock Enterprise Architecture The Foundation Model Landscape Has Matured The most significant evolution […]
Read more →GenAI Frameworks Compared: Building Real Applications with LangChain and LlamaIndex
Compare LangChain, LlamaIndex, Semantic Kernel, and more. Learn when to use each framework and build production-ready RAG systems with practical code examples.
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