Loop Engineering — Patterns and CLI Tools for AI Agent Orchestration

Introduction

Loop Engineering provides practical patterns and CLI tools for building deterministic, repeatable AI agent workflows. Instead of one-shot prompting, it helps developers design structured loops where agents iterate on tasks with clear convergence criteria and cost controls.

What Loop Engineering Does

• Provides CLI tools (loop-init, loop-audit, loop-cost) for agent workflow management
• Includes starter templates for common loop patterns like review-fix-verify cycles
• Estimates token cost before running expensive multi-turn agent interactions
• Audits existing agent configurations for anti-patterns and inefficiencies
• Documents proven orchestration patterns from production deployments

Architecture Overview

The toolkit consists of three CLI utilities backed by a shared configuration format. loop-init scaffolds a project with loop definitions in YAML. loop-audit analyzes your prompt chains and flags unbounded loops or missing exit conditions. loop-cost simulates token usage across the loop topology without calling any model.

Self-Hosting & Configuration

• Install tools globally via npm or use npx for zero-install execution
• Configure loop definitions in a loops.yaml file at project root
• Set cost budgets and iteration caps per loop in configuration
• Integrates with any AI provider via environment variable configuration
• Works alongside Claude Code, Codex, Cursor, or custom agent setups

Key Features

• Cost estimation before execution prevents budget overruns
• Audit tool catches infinite loops and missing convergence criteria
• Starter templates for 12 common patterns (review, migrate, test, etc.)
• Provider-agnostic design works with any LLM backend
• Deterministic execution order makes debugging reproducible

Comparison with Similar Tools

• LangGraph — code-heavy DAG definition vs YAML-based loop patterns
• CrewAI — role-based multi-agent vs single-agent loop orchestration
• Temporal — general workflow engine vs AI-loop-specific tooling
• Dagger — CI/CD pipelines vs prompt-loop pipelines
• Claude Code workflows — built-in orchestration vs external loop management

FAQ

Q: Does this replace my AI coding agent? A: No. It orchestrates how you use your agent by defining structured iteration patterns around it.

Q: What is a loop in this context? A: A loop is a defined cycle of prompt-execute-evaluate steps with explicit exit conditions, iteration caps, and cost limits.

Q: Can I use this with local models? A: Yes, the tools are provider-agnostic. Configure any OpenAI-compatible endpoint.

Q: How does loop-cost estimate without calling a model? A: It counts tokens in your prompt templates and multiplies by expected iterations based on the loop topology.

Sources

• https://github.com/cobusgreyling/loop-engineering
• https://github.com/cobusgreyling/loop-engineering#readme