#AI Loop
3814 related articles
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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·3 min
Loop Engineering: The New Paradigm for AI Self-Evolution
Loop Engineering by Anthropic is a new AI paradigm using four components—Mutator, Executor, Evaluator, Selector—to build self-iterating closed loops. Learn the architecture, use cases, and how to get started.
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·5 min
Loop Engineering: A Comprehensive Guide to AI Agent Loop Design
A deep dive into Loop Engineering for AI Agents — what loop feedback mechanisms are, how they differ from Harness Engineering, and a complete guide from principles to production implementation.
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AI Engineer World's Fair Closing Day: …
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AI Engineer World's Fair Closing Day: The Loops Debate, State of the Industry, and What to Build Next
AIEWF closing day recap: the agent loops debate, the State of AI Engineering report, and a keynote on what to build next — covering AI engineering's key divides and trends.
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·2 min
AI-Powered Logic Analyzer and Socket Debugging: A Breakthrough in Closed-Loop Embedded Programming
点线面 v4.3.0 enables AI to directly invoke logic analyzers and Socket debugging, closing the hardware-to-network feedback loop for embedded and IoT development.
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·7 min
Agent Loop Explained: The Core Runtime Mechanism and Think-Act Cycle of AI Agents
Deep dive into Agent Loop mechanics: the think-act cycle, how agents differ from LLMs, termination conditions, and design principles for building autonomous AI Agent systems.
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·8 min
The Enterprise AI Flywheel: Building a Model-Eval Loop as a Competitive Moat
The core of enterprise AI isn't calling general models—it's building a self-reinforcing "model-harness-sandbox-eval" flywheel. This article analyzes the four components, tacit knowledge moats, and the "token value per watt" efficiency metric.
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Industry Insights·2 min
Nadella Introduces the Loopcraft Framework: Building AI Ecosystems Through Feedback Loops
Microsoft CEO Satya Nadella's Loopcraft framework explains how to build frontier AI ecosystems through nested feedback loops across technology, business, and ecosystem dimensions.
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·3 min
AI Agent Core Principles Fully Explained: Decision Loops and the Three Key Components in Practice
A deep dive into AI Agent core principles: the Perceive-Plan-Act-Observe decision loop, plus Planning, Memory, and Tools — the three key components explained with practical examples.
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·4 min
The Four Stages of AI Engineering Evolution: From Prompt to Loop Engineering Explained
A systematic breakdown of the four stages of AI engineering: Prompt Engineering, Context Engineering, Runtime Environment Engineering, and Loop Engineering — with core logic, bottlenecks, and real-world use cases.
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·3 min
AI Programming Power User Workflow: A Practical Guide to Automated Loops and Multi-Model Collaboration
A deep dive into expert AI programming workflows covering Cursor rules, skills systems, automated loops, cloud agent parallel development, and multi-model collaboration strategies.
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·3 min
Loop Engineering Explained: An Engineering Approach to Making AI Coding Agents Run Autonomously in Loops
A deep dive into Loop Engineering and the Rhythmic framework: how closed-loop systems replace repetitive prompting to enable autonomous AI coding agents with state management and budget control.
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·3 min
What Is AI Loop Programming? A Deep Dive into the Real Value and Hype Behind Loop Engineering
AI Loop Engineering is called a new programming paradigm, but is it truly revolutionary? This article analyzes its core principles, mathematical limitations, real-world details, and the hype behind it.
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·2 min
Getting Started with Loop Engineering: Making AI Agents Run Workflows Autonomously
A deep dive into Loop Engineering: core concepts and hands-on setup including Codebase Harness, shared file systems, triggers, and Loop Contracts to make AI agents run autonomously.
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