Wiki Glossary

Designing the system that prompts, verifies, and iterates AI agents autonomously — the successor to Prompt Engineering.

Single-turn optimization vs autonomous iterative systems — when to use each approach.

Goal definition, autonomous execution, verification, state persistence, error recovery, and bounded iteration.

From Prompt Engineering in 2022 through Agentic Workflows to the formalization of Loop Engineering in 2026.

Master the repeating cycle of reason, act, observe, and verify — from Claude Code to custom LangChain systems.

Task-oriented iteration, decomposition patterns, and how coding agents manage task queues.

Deep dive into Windsurf's Cascade agent — Planning Agent + Execution Agent dual-layer architecture, AI Flow paradigm, and real-world workflow patterns.

Architecture analysis of Devin's sandbox environment, full-stack development capabilities, tool mechanism, and comparison with SWE-Agent and OpenHands.

Managing state across iterations — checkpointing, CLAUDE.md, and memory strategies.

Self-correction mechanisms, verifier/grader patterns, error classification, and retry strategies.

Agent-Computer Interface (ACI) concepts, OpenHands Docker sandbox, Agent Canvas, multi-task orchestration, and SWE-Agent's LM-centric GitHub Issue auto-fix.

Open loop vs closed loop from control theory, convergence criteria, and real examples from production agent systems.

Product Manager to Architect to Engineer to QA role-based SOP-driven development, and comparison with CrewAI's role model.

Sequential, parallel, and hierarchical coordination — communication protocols, shared state, and conflict resolution.

The foundational AI agent loop pattern with core components and real implementations.

Learn how to build loops within loops in AI agent systems, including sequential, parallel, and conditional nesting patterns, state sharing between nested loops, and practical implementation examples.

Hierarchical decomposition with loops at each level — from strategic planning to sub-task execution.

Comprehensive guide to making AI agent loops production-ready, covering monitoring, observability, error handling, graceful degradation, resource limits, timeout management, testing strategies, and a production readiness checklist.

Building autonomous coding loops with Aider — the pioneering open-source AI pair programmer with Git-native loop workflows.

LLMs, coding agents, and orchestration frameworks compared — find the right model for your loops.

From fully autonomous loops to human-supervised workflows — design the right level of oversight.

From Addy Osmani and Boris Cherny to Peter Steinberger — the pioneers who shaped the discipline.

Verification criteria, convergence strategies, state management, and loop testing from production systems.

Nested, cascading, and recursive loop patterns for complex multi-dimensional tasks.

Using Codex CLI, GPT-4o, and agent frameworks to build autonomous coding loops.

How AI agents interact with computers — from SWE-Agent to Claude Code shell. Covers browsing, editing, executing, and the LM-centric design paradigm.

The foundational Reason + Act pattern that powers every modern AI agent — from theory to production implementation.

Parallel isolation, maker-checker, and specialized delegation — how to coordinate multiple AI agents for complex tasks.

Systematic loop design vs general agent autonomy — how loop engineering is the discipline within agentic coding that ensures reliability.

Iterative control loops vs tool connectivity protocol — understanding the relationship between loop architecture and MCP server integration.

Sequential planning patterns vs iterative loop systems — how modern loop engineering absorbs and extends the plan-execute paradigm.

Adaptive AI-driven loops vs static rule-based workflows — when to use traditional automation and when loop engineering replaces it.

The discipline of designing loops vs the autonomous systems that run them — understanding the relationship between loop engineering and AI agents.

Comparing Claude Code and Cursor IDE as loop engineering platforms — CLI agent vs IDE-integrated AI, hooks, sub-agents, and autonomous coding workflows.

Claude Code vs Aider for autonomous loop engineering — terminal-native agents compared: hooks, sub-agents, Git-native commits, and model flexibility.

Comparing Cline and Roo Code as open-source AI coding agents for loop engineering — VS Code extensions with autonomous coding capabilities.

Comparing Windsurf (Codeium) and Trae (ByteDance) as AI-powered IDEs for loop engineering — agentic coding, cascade flows, and autonomous workflows.

Comparing OpenHands and Devin as autonomous AI software engineers for loop engineering — cloud-based agents, sandbox environments, and end-to-end coding loops.

Essential design patterns for loop engineering — retry, circuit breaker, supervisor, pipeline, and state machine patterns for autonomous AI agent loops.

Error handling strategies for autonomous AI agent loops — classification, recovery patterns, graceful degradation, and fault tolerance in loop engineering.

Testing strategies for AI agent loops — unit testing, integration testing, golden path testing, and evaluation frameworks for autonomous coding systems.

How to define and detect loop convergence in autonomous AI systems — termination conditions, success metrics, stagnation detection, and convergence patterns.

Security considerations for autonomous AI coding loops — prompt injection defense, sandboxing, permission models, supply chain security, and safe autonomous execution.

Complete guide to using Cursor IDE for loop engineering — Composer mode, auto-correction loops, multi-file editing, and autonomous coding workflows.

Complete guide to Cline (Claude Dev) for loop engineering — VS Code extension for autonomous AI coding with terminal access and MCP support.

Complete guide to Roo Code for loop engineering — open-source AI coding agent with multi-model support, task decomposition, and autonomous coding workflows.

Complete guide to Windsurf (Codeium) for loop engineering — Cascade agentic flows, multi-file editing, and autonomous coding in an AI-native IDE.

Complete guide to Trae (ByteDance) for loop engineering — build-on-reference mode, autonomous agent, and free AI-powered coding workflows.