The Orchestrator pattern reduces LLM calls from seven to two, cutting latency from 4.2 s to 1.1 s and cost by about 70%, by separating routing and synthesis from deterministic execution and supporting single, parallel, and sequential agent strategies.
This article explains what an agentic loop is, contrasts it with single‑pass chatbots, outlines its five stages, shows a visual architecture, walks through a concrete multi‑step example, provides Python pseudocode and a LangChain implementation, and discusses when to use or avoid such loops.
The article breaks down Claude Code’s persistent agentic loop into four layers—QueryEngine, Tool System, Permission/Hook, and React + Ink—explaining how each turn gathers context, makes model decisions, executes actions, verifies results, handles errors, and renders a terminal UI.
This article explains the concept of Conditional Edge in LangGraph, shows how to add conditional edges with three parameters, demonstrates rule‑based, multi‑branch, and loop routing patterns, compares rule‑based versus LLM‑based routing, provides a complete customer‑service agent example, and lists common pitfalls and best‑practice checklists.
This guide explains what an AI Agent is, how it differs from a chatbot, the importance of tools and prompt design, common pitfalls, multi‑agent coordination, and practical steps to build, monitor, and deploy production‑grade agents.
