This article examines the transition from Harness engineering to Loop engineering in building enterprise-grade long-horizon agents. It provides a detailed architectural analysis of three major agent frameworks: Claude Code, Codex, and OpenClaw, highlighting their layered designs and best practices. The author discusses key patterns for managing complex, multi-step tasks and ensuring reliability in production environments. For engineering teams designing scalable AI agent workflows, this offers practical insights into state management, error handling, and task orchestration. The content is timely given the rapid adoption of agent-based systems in enterprise settings.
Explore the evolution from Harness engineering to Loop engineering for long-horizon agents, with architectural breakdowns from Claude Code, Codex, and OpenClaw.