#AI Loop
2940 related articles
·3 min
PIRL Framework Explained: A Smooth Transition from Open-Loop Exploration to Closed-Loop Reinforcement Learning
Deep dive into the PIRL reinforcement learning framework: how to smoothly transition from open-loop exploration to closed-loop RL, mitigating the exploration-exploitation dilemma and improving sample efficiency.
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·5 min
Loop Engineering Explained: The New Programming Paradigm Where AI Prompts Itself
Loop Engineering is a paradigm shift in AI usage. Learn how to build automated loops where agents explore, execute, and verify tasks autonomously, with a hands-on e-commerce case study.
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·2 min
Three Flow Control Nodes in Dify Workflows: Question Classifier, Conditional Branch, and Human-in-the-Loop Explained
An in-depth look at three core flow control nodes in Dify workflows: the Question Classifier for intelligent LLM-based routing, the Conditional Branch for precise if-else logic, and Human-in-the-Loop for high-risk confirmation checkpoints.
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·3 min
AI Not Working? Learn to Diagnose Which Layer Is the Problem — Loop, Harness, or Context
AI agents underperforming? The root cause usually isn't the model. This guide breaks down Loop, Harness, and Context Engineering so you can diagnose the real issue fast.
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·3 min
Agent Loop: A Design Guide for AI Autonomous Reasoning-Action-Verification Workflows
A deep dive into AI Agent Loops: from single loops to multi-agent collaboration. Master goal definition, validation mechanisms, and stop conditions to avoid blindly building agent fleets.
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·2 min
Agent Loop Explained: Core Principles and Implementation of the Agentic Cycle
A deep dive into the Agent Loop: how agents autonomously cycle through think→act→think, the difference from regular LLMs, ReAct paradigm origins, and how to implement one from a while loop.
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·2 min
Loop Engineering Explained: The New Development Paradigm of Closed-Loop AI Agent Iteration
Loop Engineering is an emerging AI dev paradigm where Agents iterate in controlled loops instead of one-shot outputs. Learn the 4-year evolution and what it means for developers.
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·4 min
GPT-5.6 & OpenAI Super App Deep Dive: Loop Engineering as the New Paradigm Replacing Prompt Engineering
Deep dive into GPT-5.6 (Sol/Terra/Luna) and OpenAI's Super App: Loop Engineering, Parallel Agents, and Computer Use — unpacking the shift from prompt to loop engineering with real test cases and a startup framework.
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·2 min
Chrome DevTools for Agents: The Closed-Loop Debugging Powerhouse for AI Coding Agents
Chrome DevTools for Agents, unveiled at Google I/O, gives AI coding agents a closed feedback loop with Lighthouse audits, user input simulation, and device emulation.
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·4 min
Self-Healing Agent Loops: Using AI to Automatically Discover and Fix Programming Language Bugs
How Boundary built a self-healing AI Agent loop that automatically writes BAML code, discovers bugs, and generates fix PRs — a practical software factory with deduplication, human-in-the-loop leverage points, and extensible multi-loop design.
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·2 min
Loop Engineering: Why Unsupervised AI Loops Are a Recipe for Disaster
Loop Engineering lets AI run autonomously until criteria are met. This deep dive exposes its three core risks: unbounded token costs, hidden quality failures, and goal misalignment — and why humans remain irreplaceable.
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·3 min
AI Testing in a Closed Loop: Full Pipeline from API Collection and Test Case Generation to Agent-Driven Execution
Full breakdown of a real AI testing pipeline: API collection, doc enrichment, AI test case generation, Agent-driven execution, and test reports — with Skills, RAG, and Harness engineering.
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·3 min
AI Code Getting Worse with Every Fix? A Three-Layer Closed-Loop Engineering Methodology to Break the Cycle
AI code spiraling out of control? This article breaks down a three-layer engineering system — Prompt rules, Skill workflows, and Harness feedback loops — with real-world results showing pass rates rising from 70% to 98%.
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Agentic Loop Explained: The Three-Loop…
·2 min
Agentic Loop Explained: The Three-Loop Architecture of Reasoning, Tool Use, and Orchestration
A deep dive into the Agentic Loop — breaking down the three-layer architecture of reasoning, tool use, and orchestration to help developers build and debug reliable AI agent systems.
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·4 min
Agent Loop in Practice: A Guide to Building Self-Improving Automation with Guardrails
A deep dive into building verifiable, self-evolving Agent automation loops with Claude Code and Codex — covering Loop Contracts, four trigger types, three-phase execution architecture, and Evolve Loops.
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AI Agent Human-in-the-Loop (HITL) Desi…
·3 min
AI Agent Human-in-the-Loop (HITL) Design: Risk Classification and Best Practices
A deep dive into AI Agent Human-in-the-Loop design: balancing automation with oversight using risk tiers, async approval, and confidence thresholds.
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·4 min
Claude's New Reflect Feature: Visualizing the AI Dependency Loop Behind Your Data
Anthropic's Reflect feature visualizes Claude usage data — but it's also quietly building dependency and driving paid conversion. A deep dive into the design ethics and psychology behind it.
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AI-Generated Videos That Activate the …
·3 min
AI-Generated Videos That Activate the Brain: Breakthroughs and Concerns in Neuroscience Closed-Loop Optimization
Scientists use AI closed-loop optimization to auto-generate videos that maximally activate specific brain regions. Explore the science, medical potential, and ethical concerns around neural manipulation.
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·7 min
Getting Started with AI Agents: A Deep Dive into the Perception-Decision-Action Loop
A clear, in-depth guide to how AI Agents work: the paradigm shift from traditional programs, the perception-decision-action loop, and the four pillars—LLMs, tool calling, memory, and RAG.
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·4 min
FTPO: A Training-Level Solution to AI Repetition Loops
FTPO (Final Token Preference Optimization) tackles AI "Doom Loops" at the training level rather than patching them at inference time — exploring its principles, value for small/quantized models, and open challenges.
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