This article walks through the full lifecycle of a user message in Claude Code's Agent SDK, explaining each processing stage, the crucial tool‑use decision, parallel tool execution, error handling, and how Hooks let developers extend the loop without modifying core logic.
This tutorial walks through a minimal 50‑line Python implementation of an AI Agent Loop, covering the core four‑step cycle, dual termination strategies, deterministic vs. autonomous designs, tool registration, and a complete runnable example.
This article explains what an Agent Loop is, how it differs from a simple while loop by using intelligent LLM‑driven exit conditions, compares three mainstream design patterns—deterministic, SDK‑level, and multi‑agent orchestration—and offers guidance on selecting the right approach for various AI tasks.
This article walks you through building a Claude‑style AI programming agent from the ground up, breaking the architecture into twelve incremental versions, explaining the universal agent loop, tool integration, planning, memory compression, concurrency, and multi‑agent collaboration with concrete code examples in Python, Java, Go, and TypeScript.
The article explains Loop Engineering by distinguishing it from basic Agent Loops, outlines its six core components, showcases a text‑classification example, and discusses when the approach boosts efficiency versus when traditional Human‑in‑the‑Loop remains preferable.
The article explains how Loop Engineering turns multi‑round Agent execution into a maintainable, observable, and replayable closed‑loop by defining six core components, reusing traditional development patterns, presenting a CI‑failure triage demo, and highlighting architectural and practical pitfalls.
The article breaks down Loop Engineering—a new paradigm for AI agents—by exploring why it emerged, defining its scope, distinguishing it from Agent Loops and Context/Harness Engineering, detailing its building blocks, tools, applicability criteria, and the risks and limitations of fully autonomous loops.
The article dissects the agent loop concept, explains why durability must span the entire execution layer, presents a three‑layer architecture (loop, skill, orchestrator), shows concrete Inngest code, discusses error handling, observability, and how this model compares to existing tools, concluding with guidance for building production‑grade agent loops.
This article breaks down the three foundational elements of agent programming—Model, Tool, and the Agent Loop—showing how to obtain and configure models, define tools with TypeBox, and manage the reasoning‑tool‑feedback cycle, complete with code examples and a flow diagram.
When AI agents work well in a single run but fail to reproduce results, the problem lies not in prompts but in the lack of a structured runtime environment; Harness Engineering adds task specifications, context, tools, permissions, memory, skills, workflow, verification, logging and feedback to turn a one‑off agent into a stable, repeatable, and self‑evolving system.
Using Codex to take over a real Spring Boot codebase—modifying packages, fixing beans, refactoring modules, adding idempotency, and even auto‑repairing compile errors—revealed that AI can now understand project context and act as an engineering agent, fundamentally shifting Java development practices.
This article dissects OpenClaw's agent architecture, detailing how the System Prompt, Skill modules, and Agent Loop interact, explaining PromptMode variations, safety rules, tool definitions, skill loading pipelines, heartbeat handling, sub‑agent spawning, silent replies, and the context engine that assembles messages for LLMs.
This article dissects Claude Code’s architecture, detailing its simple while‑loop core, layered tool system with lazy loading, seven‑layer permission defense, five‑stage context compaction, sub‑agent collaboration, hook mechanisms, and the CLAUDE.md context‑engineering approach, revealing why token efficiency and deterministic security drive its design.
This article analyzes the hidden pitfalls of Qwen's official Jinja chat template, explains how the community‑maintained Qwen‑Fixed‑Chat‑Templates v19 fixes rendering errors, KV‑Cache loss, token waste and agent dead‑locks, and provides step‑by‑step installation instructions for LM Studio, llama.cpp, vLLM and MLX.
The article explains how AI agents that operate in a reasoning‑action‑observation loop outperform single‑shot LLM inference by continuously observing, planning, and correcting errors, illustrated through a ticket‑booking example and detailed analyses of ReAct, Plan‑Execute, OODA, and Steering Loop architectures.
This article provides a comprehensive technical analysis of Claude Code, covering its project scale, two‑layer agent loop design, five design principles, tool system architecture, fine‑grained permission model, multi‑agent collaboration strategies, context‑engineered system prompts, and a custom React‑based terminal UI, all illustrated with concrete code excerpts and diagrams.
The article explains that an AI Agent combines a large language model, a clear goal, and callable tools in a multi‑step reasoning loop, detailing its perception‑plan‑act architecture, differences from plain chat, common misconceptions, and practical questions for evaluating such systems.
This article walks through constructing a tiny, runnable AI agent loop that reads a user task, lets the model choose the next step, calls a tool, feeds the observation back, and repeats, then explains how to add harness, memory, permission, and validation layers to make the agent reliable in real‑world engineering environments.
The article walks through building a minimal 20‑line agent loop, explains each step—from reading a task to invoking tools and feeding observations back—then shows how real systems like Claude Code, OpenClaw and Pi add layers of harness, memory, permission and validation to make the loop safe and reliable in production.
The article dissects Anthropic's open‑source Claude Code—an AI coding agent built on half a million lines of TypeScript—by walking through its agent loop, tool registry, permission system, context‑window management, hierarchical CLAUDE.md configuration, and comparing its agent‑first design to IDE‑first tools like Cursor.
The article explains Harness Engineering—a set of tools that control an AI agent’s cognitive framework, capability boundaries, and behavior flow—showing how proper harnesses can turn modest models into high‑performing agents, while poor harnesses cause failures, with concrete examples, benchmarks, and research citations.
The article explains that while an Agent Loop can execute tasks, long‑running stability depends on a well‑designed Harness engineering layer that organizes knowledge, enforces rules, provides verification, and automates cleanup, turning a functional prototype into a reliable production system.
OpenClaw distinguishes itself from ordinary chatbots by employing an Agent Loop—a task‑driving execution chain that normalizes inputs, assembles context, makes model‑based decisions, suspends for tool results, and writes back state, enabling continuous task progression rather than single‑turn replies.
This article explains how to construct a functional coding agent by combining a large language model, a bash tool, and a message history loop, showing step‑by‑step code, system prompts, error handling, and a complete execution example.
This article explores the Learn Claude Code website, breaking down the universal agent loop into twelve incremental versions, demonstrating language‑agnostic implementations in Python and Java, and detailing progressive capabilities—from basic tool integration to memory compression, concurrency, and multi‑agent collaboration.
The article explains how AI‑powered agents change software delivery, why traditional notions of task completion no longer apply, and how a Fitness Function‑based harness engineering approach—illustrated with the Routa project—encodes executable, auditable completion criteria, hard‑gate checks, and contract consistency to reliably guide agents through the development loop.
This open‑source tutorial walks developers through twelve progressive lessons, expanding a minimal 84‑line agent to a full‑featured 694‑line Claude‑Code‑style AI system that covers tool calls, sub‑agents, context compression, and multi‑agent collaboration.
This article explains how the Agent Loop—an engineered while‑loop that repeatedly calls an LLM, decides when to use tools, executes them, and feeds results back—creates persistence, style, memory, judgment, and safety boundaries that together make an AI agent feel like it has its own personality.
This tutorial introduces the foundational Agent Loop that powers modern AI agents, explains why it is needed, breaks down its four core components, compares workflow‑based and agent‑based designs, and provides a minimal Python implementation with code, pitfalls, and a concrete RSS‑news use case.
This article provides an in‑depth technical analysis of GoClaw, a Go‑based personal AI assistant that mirrors OpenClaw's design while introducing a more robust agent loop, reflective reasoning, extensible skill system, sophisticated error handling, and multi‑provider failover, complete with code excerpts, architecture diagrams, and performance trade‑offs.
The article examines common pitfalls of AI programming assistants—premature exits, fragile single‑prompt workflows, costly re‑prompting, and context loss—explains that these stem from unreliable LLM self‑evaluation, and introduces the Ralph Loop (also called Ralph Wiggum Loop) as a robust, stop‑hook‑driven solution that forces continuous iteration until explicit, verifiable completion criteria are met.
This article traces Codex's evolution from a simple code‑completion tool to a full‑stack Agent Loop system, explains its loop‑based architecture and core components, showcases practical configurations, multi‑agent collaboration, real‑world case studies, and discusses technical challenges and future trends for AI‑assisted software development.
The article examines the emerging concept of Agent OS as a platform for Skills, surveys the few mature Agent OS offerings across code, desktop, and web domains, highlights the rise of response APIs, reviews available agent SDKs, and explains the central role of the agent loop and its various shells.
This guide explains Claude Code’s agent loop, model choices, built‑in tool categories, project access scope, session handling, checkpoint and permission controls, and practical tips for efficiently using the AI‑driven terminal assistant to write, test, and refactor code.
This article demystifies the buzzword "Agent" by explaining the fundamental agent loop, contrasting autonomous and collaborative agents, discussing tool integration, latency, user experience, and offering a checklist for evaluating AI agent systems.
