Loop Engineering

Loop Engineering AI programming paradigm guide. Covers the core concepts of transitioning from single prompt calls to autonomous loop systems, key components, loop patterns, configuration examples, and anti-patterns.

Loop Engineering is a new AI programming paradigm -- designing autonomous loop systems that allow Agents to continuously execute tasks in an environment with goals, feedback, and self-verification, eliminating the need for manual prompting.

Use Cases

Use when users need to learn about "Loop Engineering", "AI loop systems", "autonomous Agent design", "/loop", "/goal", "AI programming paradigms", or "system architect".

Core Concepts

Transition from single prompt calls to continuous loop systems:

Paradigm	How It Works	Developer Role
Prompt Engineering	Manually enter prompts, model executes once, human judges results	Prompt Engineer
Harness Engineering	Build a constrained environment for a single Agent with pre-checks/fixes/hooks, still requires human triggering	Harness Engineer
Loop Engineering	Manage multi-Agent sequencing, decision-making, and autonomous loops, self-driven over long periods across many rounds	System Architect / Loop Engineer

Key Components

Component	Purpose	Reference
Automations	Trigger tasks by time/event, support periodic execution and archiving	See config-examples
Worktrees	Manage task state and context, maintain memory and state persistence across rounds	See config-examples
Skills	Encapsulate reusable task capabilities as skill modules for the loop system to invoke	See patterns
Plugins/Connectors	Extend system capabilities, interact with external tools or services	See integration
Sub-agents	Small specialized Agents for division of labor, supporting parallel multi-Agent task execution	See patterns / config-examples
External Memory & Feedback Loops	Achieve self-assessment and correction through mechanisms like logs, tests, and type checking	See anti-patterns

/loop and /goal Primitives

Primitive	Behavior	Use Case	Usage Tip
/loop	Execute tasks repeatedly on a cycle	Scheduled checks, periodic maintenance	Must set maxRounds; no termination condition = infinite loop
/goal	Run continuously until verifiable conditions are met	Goal-oriented autonomous tasks, with an independent model checking completion each round	Completion conditions must be objectively verifiable (file exists, tests pass, metrics met)

Quick Start

1. Define the goal: Do you want scheduled patrols, or are you pursuing a verifiable objective? Use /loop for the former, /goal for the latter.
2. Choose a pattern: Browse common loop patterns to find a design that matches your scenario.
3. Configure components: Refer to configuration examples to build the loop skeleton.
4. Avoid pitfalls: Read anti-patterns to prevent issues in your design early on.
5. Platform integration: Refer to platform integration to connect with your actual toolchain.

Design Principles

• Goal definition: Specify verifiable completion conditions to prevent Agents from falling into meaningless loops
• Feedback mechanisms: Enable Agents to self-assess through tests, validation, logs, and other mechanisms
• State management: Use Worktrees to maintain cross-round context and memory
• Modularity: Encapsulate reusable capabilities as Skills and Plugins
• Division of labor: Reasonably partition Sub-agent responsibilities, parallelize to improve efficiency
• Graceful termination: Set timeouts, max rounds, and abnormal exit conditions

File Navigation

File	Content	Who It's For
patterns.md	8 common loop patterns + selection guide	Need to quickly find a matching pattern
config-examples.md	Automation / Worktree / Sub-agent configuration examples	Starting to build a loop system
anti-patterns.md	Common mistakes + correction strategies	Troubleshooting or preventing issues
integration.md	OpenClaw / TaskFlow and other platform integration	Connecting to actual toolchains