harness-engineering

A comprehensive guide to building AI agent harnesses, covering tool execution, context management, state/memory, and guardrails, based on lessons from building Claude Code and other harnesses for enterprise.

A GitHub repository with two books on harness engineering for Claude Code and Codex, exploring how to structure constraints and execution for code-writing AI in real engineering environments.

An open-source project teaches you to build a simplified version of Claude Code from scratch, thoroughly explaining the harness engineering of AI Agents. It has received 66.5K Stars.

A curated collection of resources, patterns, and templates for building reliable AI agent harnesses, focusing on the scaffolding around agents rather than the models themselves.

Elvis Sun shares a detailed playbook on using AI coding agents with harness engineering and loss function development to autonomously solve complex engineering problems, demonstrating how to avoid common pitfalls like agent cheating.

The author built an AI research tool that reduces hallucination through strict orchestration and harness engineering, enabling users to supervise research decisions and verify sources.

Loop Engineering will completely replace Harness Engineering in the coming months, becoming the hottest paradigm in AI.

GitHub open-source course Learn Harness Engineering teaches you to build a controllable workflow framework for AI coding assistants (e.g., Claude Code, Codex). It includes 12 theory lessons and 6 hands-on projects, covering core mechanisms: instruction, state, validation, scope, and session.

Recommends the free open-source Harness Engineering course provided by Walkinglabs, covering theory and practice, and argues that ordinary people should learn Harness Engineering instead of continuing to compete in large model development.

This article introduces the concept of 'Harness Engineering,' a discipline focused on designing the systems that constrain and guide AI agents to make them reliable in production, arguing that the harness matters more than the model itself.

AI news roundup covering Fireworks' $15B and Baseten's $11B funding rounds, OpenRouter's $113M round, and the emerging importance of agent harness engineering over base models in coding agents.

A tip for Codex users to implement agent research papers directly into their Codex environment using goal mode and local config, with SkillOpt as an example that improved a GPT-5.5 agent by +24.8 points.

This article by yan5xu (former ManusAI) proposes a spiral evolution model for LLM engineering paradigms: from Prompt Engineering (2022-2024) to Context Engineering (2025), then to Harness Engineering (2026-), and discusses the bottlenecks and driving factors at each stage.

The article discusses three stages of LLM engineering evolution from Prompt Engineering to Harness Engineering, reflecting the progression of AI engineering practices.

OpenAI shares how its team built a full software product with zero manually-written code using Codex agents, focusing on designing environments and feedback loops for reliable agent operation.

This article discusses the concept of AI-First organizational structure, transforming AI from a supporting tool to a productivity leader, redesigning company processes, and introducing new ideas such as Harness Engineering and Agent Economy.

LukeDing14 points out that many people still don't use Codex correctly for projects, and recommends the learn-harness-engineering resource along with his own harness file, sharing tips for efficient coding and project organization.

Harness Engineering is not mysticism, but an engineerable living product. The article proposes a six-layer engineering framework (Rule, Skill, Sub Agent, Workflow, Scripts, dev-map), emphasizing starting simple, relying on scripts rather than prompts, and improving through iteration.

Akshay Pachaar clarifies three distinct AI engineering concepts — prompt engineering (the message), context engineering (the memory), and harness engineering (the machine) — explaining their roles and interplay in building LLM-based agents, with a link to a deeper article on agent harness engineering.

A concept: Use Multica to manage local runtimes (e.g., Claude Code, Codex), Helio to predefine Agents, Obsidian as the memory/context system, combined with Harness Engineering—exploring the best approach for a local multi-agent system.