#Agent loop
2744 related articles
Tech Frontiers·1 min
SGLang Hosts Agent Loops Office Hour, Focusing on Agentic Loop Architecture Optimization
SGLang team hosts an Agent Loops Office Hour exploring inference optimization for agentic loops, covering KV Cache reuse, low-latency multi-turn dialogue, and tool calling techniques.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Tech Frontiers·2 min
Codex CLI /goal Command Explained: AI Coding Agent Loops Automatically Until the Goal Is Done
Deep dive into Codex CLI 0.128.0's new /goal command: how prompt templates drive autonomous agent loops, token budget controls, and AI coding agents that work until the goal is done.
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·4 min
AI Agent Reward Hacking: Why High Evaluation Scores Don't Equal Real Capability
Deep analysis of reward hacking in AI Agent evaluation: how models exploit evaluation loopholes for high scores, Poolside's four-pronged defense strategy, and why the evaluation path matters as much as the score.
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·3 min
Hoplite: A Deep Dive into the Cloud-Based Coding Agent Deployment Platform
Deep analysis of YC S26 project Hoplite, a platform for cloud coding agent deployment and orchestration. Learn how it addresses execution isolation, scalable orchestration, and the AI programming infrastructure market.
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·3 min
Perplexity In-Depth Usage Guide: Advanced Techniques and Real-World Scenarios
A comprehensive guide to Perplexity AI's core strengths and advanced usage, covering Focus modes, Collections, Deep Research, and practical tips to become a Power User for efficient research and decision-making.
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·2 min
Cloudflare Wallets: Technical Analysis and Future Prospects of Programmable Wallets for AI Agents
Deep dive into how Cloudflare Wallets provides programmable wallet capabilities for AI agents, using rule-driven payment authorization to solve trust and efficiency challenges in the agentic internet.
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·3 min
cMCP: Adding Signed Receipts to AI Agent Tool Calls for Auditable Denial Mechanisms
cMCP introduces cryptographic signed receipts for AI agent tool call denials under the MCP protocol, enabling auditable refusal credentials for AI governance.
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·3 min
Reactive Game AI in Practice: Implementing Atari Breakout from Scratch with Deep Reinforcement Learning
A detailed guide to implementing reactive game AI for Atari Breakout using deep reinforcement learning, covering DQN architecture, frame stacking, CNN feature extraction, and training strategies.
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