#Agent Loop
2570 related articles
Expert Opinions·3 min
Gumloop Founder: 50 AI Agents Running Your Company? Don't Fall for It
Gumloop founder Max shares the truth about AI automation: processing 4M daily workflows taught him why 50 AI Agents running a company is a lie, and why the real AI philosophy is acceleration, not replacement.
Read more →
Tutorials·3 min
Agent Loop Explained: Solving Code Refactoring Pain Points with the ReAct Pattern
Deep dive into Agent Loop, the core mechanism of AI coding tools. Learn how the ReAct pattern's reason-act-observe cycle enables autonomous multi-step code refactoring.
Read more →
Industry Insights·2 min
OpenAI Codex Powers a Self-Improving Tax AI Agent: A New Paradigm for Closed-Loop Evolution
OpenAI and Thrive Holdings launch a Codex-based Tax AI with closed-loop self-improvement: error tracing, auto-fixing, and test validation. A deep dive into this new AI Agent evolution paradigm.
Read more →
Industry Insights·1 min
Claude Code Architecture Deep Dive: Why a Single-Loop Design Beats Multi-Agent Systems
Deep dive into Claude Code's architecture: single-loop control, no RAG, 9,400-token tool prompts, and model tiering. The minimalist design behind a $1B AI coding tool.
Read more →
Tutorials·2 min
Replicating Manus for $30: A Practical Breakdown of Building an AI Agent's Minimum Viable Loop
A detailed breakdown of replicating the Manus AI agent platform for $30, covering Docker container integration, task orchestration, UI design, and the course-based monetization strategy.
Read more →
Deep Dives·2 min
How AI Agents Work: The Four Core Elements and ReAct Loop Decision-Making Mechanism Explained
Deep dive into AI Agent's four core elements (LLM, Tools, Prompts, Executor) and ReAct loop decision mechanism, with a weather query example walkthrough.
Read more →
Deep Dives·4 min
Agent Loop Core Mechanism Explained: Building a ReAct Loop from Scratch (With Debugging War Stories)
Deep dive into Agent Loop, the core mechanism of AI Agents, based on ReAct architecture. Covers Tool Call ID handling, context management, assistant message feedback, with full Python code and common bug fixes.
Read more →
Deep Dives·1 min
Ralph Loop: The New AI Programming Paradigm That Beats Multi-Agent Systems Through Iterative Trial and Error
Ralph Loop is a 2025 AI programming paradigm: a memoryless AI Agent repeatedly reads the same Prompt, accumulates experience via file systems, beating multi-agent architectures through iterative loops.
Read more →
Tutorials·2 min
Harness Engineering Explained: A Guide to Building a Four-Step Closed-Loop AI Agent Work System
A deep dive into the Harness Engineering four-step closed-loop principle (Goal, Action, Verification, Memory), clarifying its relationship with Prompt Engineering, Context Engineering, and MCP.
Read more →
Tutorials·3 min
AI Agent Context Management in Practice: A Complete Guide from Death Loops to Intelligent Truncation
Deep dive into AI Agent context management: a three-step strategy from naive truncation to intelligent memory, covering sub-Agent architecture and long session evals to solve LLM context bloat.
Read more →
Deep Dives·3 min
AI Agent Core Principles Explained: The Decision Loop and Three-Component Architecture
Deep dive into AI Agent core principles: the Sense-Plan-Act-Observe decision loop, and how Planning, Memory, and Tools components transform LLMs into autonomous digital employees.
Read more →
Deep Dives·4 min
Deep Dive into Claude Code Source Code: A Complete Guide to Agent Loops, Context Engineering, and Architecture Design
Deep analysis of Claude Code's source architecture: Agent loop mechanism, context engineering strategies, tool system design, and permission controls. Based on the viral GitHub project how-claude-code-works.
Read more →
·2 min
AI Agents Running a Real Company: Experiment Results and Capability Boundary Analysis
What happens when AI agents are tasked with running a real company? This analysis examines agent performance, critical shortcomings, and practical enterprise deployment advice.
Read more →
·3 min
Self-Hosting a Code Review AI Agent: From Architecture Design to Engineering Implementation
A deep dive into building and self-hosting a code review AI Agent from scratch, covering architecture design, context management, model selection, and noise control.
Read more →
·5 min
Have AI Doomers Actually Used AI? A Developer Exposes the Gap Between Hype and Reality
AI Doomers warn AI will destroy humanity, but have they actually built AI apps? A developer's sharp critique reveals the vast gap between AI demos and real engineering practice.
Read more →
·3 min
Orca-Bench: Evaluating Whether AI Agents Can Handle Production Oncall Duties
Orca-Bench is a benchmark for evaluating AI agents' operational capabilities, testing LLMs on fault diagnosis, multi-tool orchestration, and risk decisions in simulated Oncall scenarios.
Read more →
·3 min
Graph Engineering: The Core Paradigm and Design Patterns for AI Agent Orchestration
Deep dive into how graph engineering uses state machines and directed graphs to constrain AI agent behavior, covering reflection, routing, human-in-the-loop, and parallel execution patterns.
Read more →
·3 min
Graph Engineering: The Core Paradigm and Design Patterns for AI Agent Orchestration
Explore how graph engineering uses state machines and directed graph structures to constrain AI agent behavior, covering reflection, routing, human-in-the-loop, and parallel execution patterns.
Read more →
·3 min
13 Models, 4 Agents: Who Is the Best Coding AI?
A systematic evaluation of 13 LLMs, 4 agent frameworks, and 5 programming languages reveals the real differences in AI coding capabilities and optimal model-framework pairing strategies.
Read more →
·4 min
Tiling Window Management: A New Interaction Paradigm for Multi-Agent Conversations
Exploring tiling window management for multi-agent AI conversations: how it solves parallel monitoring and observability challenges, real-world limitations, and the evolution from chat boxes to control consoles.
Read more →