The article explains that the fundamental distinction between AI Agents and Workflows lies in who holds decision‑making control, outlines common Workflow patterns, describes the step‑by‑step operation of Agents, and provides clear criteria for choosing the appropriate approach in interview scenarios.
The article explains how to choose between Sub‑Agent and Agent Team structures for multi‑agent systems by evaluating whether sub‑tasks share context, need isolation, compression, parallelism, or continuous collaboration, and provides practical guidelines, pitfalls, and a decision framework to avoid over‑engineering.
The article analyzes why many AI projects misuse multi‑agent setups, explains the fundamental differences between Sub‑Agent (isolated parallel executors) and Agent Team (collaborative teams with shared state), and provides concrete guidelines, code examples, and design principles to select the appropriate architecture for a given task.
The article analyzes Anthropic's guidance on building effective AI agents, contrasts workflow and agent architectures, outlines criteria for choosing agents, presents six incremental design patterns, and shares practical principles such as simplicity, transparency, and robust tool interfaces.
This article explains the seven core multi‑agent design patterns—workflow, routing, parallel, loop, aggregation, network, and hierarchical—detailing their mechanics, use cases, implementation tips, and why modern agent frameworks are essential for dynamic, cross‑system AI applications.
This guide distills Anthropic’s frontline experience into a concise framework for building high‑performing AI agents, covering the workflow‑vs‑agent distinction, five composable architecture patterns, core design principles, tool‑centric optimization, and pragmatic advice on using or bypassing agent frameworks.
The article explains how breaking complex LLM tasks into sequential steps—known as prompt chaining—improves answer accuracy, debuggability, flexibility, and enables sophisticated AI workflows such as report generation, chatbots, and content creation using tools like n8n and Ollama.
This article introduces five core workflow design patterns for AI agents—Prompt Chaining, Routing, Parallelization, Orchestrator‑Worker, and Evaluator‑Optimizer—explaining their mechanics, concrete examples, suitable scenarios, and how they help build reliable, maintainable LLM‑driven systems.
This tutorial explains the fundamental differences between AI workflows and autonomous agents, compares their strengths, outlines when to use each approach, and provides concrete LangChain/LangGraph code examples, framework references, and best‑practice recommendations for building reliable LLM‑powered systems.
