loop-engineering: Design and Operate AI Coding-Agent Loops

Overview

loop-engineering provides patterns, starter configurations, and CLI tools for running AI coding agents around a codebase. It addresses the operational work between prompts: finding tasks, handing them to agents, checking their output, and keeping track of state.

It is aimed at developers and teams who want structured, repeatable agent workflows rather than a one-off code-generation command. The project offers patterns for tasks such as issue triage, dependency updates, CI maintenance, and PR monitoring. It is a way to design and operate those workflows, not a button that automatically rewrites a module.

Key Features

• CLI commands to initialize a pattern, check a project, inspect status, audit readiness, and estimate costs.
• Reusable workflow patterns for daily triage, PR babysitting, CI and dependency sweeping, and other maintenance tasks.
• Starters for running agent loops in GitHub Actions, including a thin-loop option.
• Examples for tools including Claude Code, Grok, Codex, OpenClaw, and OpenCode.
• Readiness checks and a staged rollout model, from reporting to assisted and unattended operation.
• Documentation on safety, failure modes, anti-patterns, and operating loops.

Use Cases

• A maintainer wants an agent to report on repository issues, CI, or dependencies on a regular schedule before granting it permission to make changes.
• A team needs a workflow to monitor pull requests and flag follow-up work while keeping human review in the process.
• A developer wants to break a refactor into smaller agent-assisted tasks and verify each result.
• A project wants to automate routine repository checks through GitHub Actions with a lightweight starter.

Getting Started

Run the initializer with a pattern and agent tool:

npx @cobusgreyling/loop init . --pattern daily-triage --tool claude

See the README for setup guidance, available patterns, and examples.

Alternatives

• loopy: Loopy packages bounded workflows as installable skills, while loop-engineering provides a TypeScript toolkit and patterns for building repeatable agent workflows.
• agent-factory: Agent Factory turns natural-language tasks into executable Python agent workflows, while loop-engineering focuses on TypeScript tools and patterns for managing workflow cycles.

Considerations

• The project recommends starting in report-only mode and increasing autonomy only after the verifier has performed reliably. Unattended loops can make unattended mistakes, so outputs need review.
• Agent runs can have substantial token costs, particularly for frequent workflows such as PR monitoring and CI checks. The CLI includes a cost command, but actual costs depend on how a loop is configured and used.
• The patterns and tools are for operating AI coding agents around a repository. They do not remove the need to select an agent, configure access, and decide what changes it may make.