#Agent Loop
2133 related articles
·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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·3 min
Agent Infinite Loop Engineering Guide: Detection, Breaking, and Root Cause Analysis
How to handle Agent infinite loops? This guide covers three-layer loop detection, four strategy-switching techniques, root cause analysis, and multi-layer fallbacks for building stable, production-grade Agent systems.
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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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·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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·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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·4 min
Loop Engineering in Practice: Designing and Deploying a Multi-Agent Collaborative Development System
A deep dive into Loop Engineering: how multi-agent collaborative dev systems achieve automated coding loops through workflow scheduling, step isolation, and validation.
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·3 min
The Complete Guide to Looping Engineering: Building Efficient Agent Loops from Scratch
A deep dive into Looping Engineering — covering the five core loop elements (Trigger, Goal, Judgment, Feedback, Memory), when to use loops, and a step-by-step guide to building a topic-selection loop with Claude Code.
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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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·1 min
Agentic Coding in Practice: Test Feedback Loops, LLM Benchmarks, and an Engineering Adoption Guide
An in-depth look at agentic coding: how test-driven loops enable AI self-correction, the real limits of LLM benchmarks, and key engineering lessons on context management and human-AI collaboration.
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·4 min
TigrimOSR: An Open-Source Agent Framework in Rust with Configurable Agent Loops
TigrimOSR is an open-source multi-agent system written in Rust, supporting full agent loop definition via YAML config files with only 250MB memory usage. A deep dive into Loop Engineering, Rust advantages, and self-hosted Agentic AI.
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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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·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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·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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·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
Deep Dive into Hermes Agent Architecture: Loop Mechanism, Memory System, and Gateway Design
Deep dive into Hermes Agent's core architecture: Agent Loop mechanism, three-layer memory system (Markdown/SQLite/external), Gateway multi-platform integration, context compression, and Cron jobs.
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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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