When LinkedIn open-sourced Kafka in 2011, few predicted it would become the de facto standard for real-time data streaming. Fourteen years later, Kafka processes trillions of messages daily across organizations of every size, from startups to Fortune 500 companies. Having architected event-driven systems for over two decades, I’ve watched Kafka evolve from an interesting alternative […]
Read more →Month: September 2025
Building Multi-Agent AI Systems with Microsoft AutoGen: A Comprehensive Introduction to Agentic Development
📖 Part 1 of 6 | Microsoft AutoGen: Building Multi-Agent AI Systems 📚 Microsoft AutoGen Series Introduction to Agentic Development Agent Communication Patterns Automated Code Generation RAG Integration Production Deployment Advanced Patterns Next: Part 2 → 🎯 What You’ll Learn in This Series Part 1: AutoGen fundamentals, core concepts, and your first multi-agent system Part […]
Read more →Agentic Workflow Patterns: Building Autonomous AI Systems That Plan, Act, and Learn
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: […]
Read more →Multi-Agent Coordination: Building Systems Where AI Agents Collaborate
Introduction: Single agents hit limits—they can’t be experts at everything, they struggle with complex multi-step tasks, and they lack the ability to parallelize work. Multi-agent systems solve these problems by coordinating multiple specialized agents, each with distinct capabilities and roles. This guide covers practical multi-agent patterns: orchestrator agents that delegate and coordinate, specialist agents with […]
Read more →Tool Use Patterns: Building LLM Agents That Can Take Action
Introduction: Tool use transforms LLMs from text generators into capable agents that can search the web, query databases, execute code, and interact with APIs. But implementing tool use well is tricky—models hallucinate tool calls, pass invalid arguments, and struggle with multi-step tool chains. The difference between a demo and production system lies in robust tool […]
Read more →Agent Memory Patterns: Building Persistent Context for AI Agents
Introduction: Memory is what transforms a stateless LLM into a persistent, context-aware agent. Without memory, every interaction starts from scratch—the agent forgets previous conversations, learned preferences, and accumulated knowledge. But implementing memory for agents is more complex than simply storing chat history. You need short-term memory for the current task, long-term memory for persistent knowledge, […]
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