Software Architecture

Introduction “AI agents” are everywhere in current discourse: customer support agents, coding agents, research agents, planning agents. But the term is often used loosely, sometimes referring to: A single large language model (LLM) call A script that calls a model and then an API A complex system that plans, acts, remembers, and adapts over time To design, evaluate, or improve AI agents, you need a clear mental model of what an agent actually is and how its parts work together. ...

Modern generative AI applications—especially those built on large language models (LLMs) and Retrieval-Augmented Generation (RAG)—can become chaotic very quickly if they’re not organized well. Multiple model providers, complex prompt flows, vector databases, embeddings, caching, inference orchestration, and deployment considerations all compete for space in your codebase. Without a clear structure, your project becomes difficult to extend, debug, or hand off to other engineers. This article walks through a practical and scalable project structure for a generative AI application: ...

Introduction In the fast-paced world of software development, architects and teams often grapple with complexity—balancing user needs, technical components, infrastructure, and timelines. Mind mapping emerges as a powerful, visual technique that simplifies this chaos, proving useful in 99% of real-world cases from small features to enterprise-scale projects.[1][2] By starting with a central idea and branching into related concepts, mind maps foster brainstorming, clarify relationships, and streamline planning without rigid templates.[3][4] ...

Introduction Microservices promise speed, scalability, and team autonomy by decomposing a system into small, independently deployable services. But they also introduce complexity in distributed systems, data consistency, and operational overhead. This in-depth, zero-to-hero guide walks you through microservices architecture from fundamentals to production-ready practices. You’ll learn when to choose microservices, how to design services and APIs, what tooling to adopt, and how to deploy, secure, and observe them at scale. Code snippets and reference patterns are included to bridge theory and practice. We end with curated resources for further study. ...