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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.
This paper studies harness design for LLM agents, separating it into task decomposition and guided execution, and shows that more elaborate harnesses are not uniformly better; it reveals failure modes and proposes partial harnesses as effective.
Learn Harness Engineering is an open source course that systematically organizes the concept of AI coding agent reliability engineering (Harness Engineering) proposed by OpenAI and Anthropic. It includes 12 lectures and 6 projects, aiming to help developers build reliable AI agent environments.
Learn Harness Engineering is a free course teaching the engineering of AI coding agents, covering environment design, state management, and verification to make agents like Codex and Claude Code more reliable.
A comprehensive course teaching harness engineering for AI coding agents, covering environment design, state management, and verification to make agentic coding tools like Codex and Claude Code more reliable.
This tweet summarizes an OpenAI article on Harness Engineering and Codex, discussing challenges and insights from building a 1M-line internal product using AI agents.
This article delves into the concept of Harness Engineering, noting that bare models achieve a 0% completion rate in complex engineering tasks. However, through layered context management, proper tool orchestration, and task structuring—along with other engineering infrastructure—AI coding efficiency can be significantly improved, enabling even small teams to build production-grade software. The article provides practical guidance across five core dimensions.
User recommends an article that delves into agent loops, memory mechanisms, harness engineering, and agent evaluation, highlighting its substantial value for readers who are studying agents in depth.
This article introduces the open-source Harness Engineering books, aiming to help zero-basis users efficiently Vibe Code through Claude Code and Codex, explaining in detail how to build constraint systems to deal with the behavioral consequences of AI programming models.
The article discusses how Addy Osmani argues that the performance difference between AI coding agents like Claude Code, Cursor, and Cline stems from their 'Harness'—the layer of prompts, tools, and constraints around the model—rather than the underlying model itself. It details best practices for harness engineering, including hooks, sandboxing, and context management, to bridge the gap between model capability and actual agent performance.
A project-based course repository on Harness Engineering for AI coding agents, covering environment setup, state management, verification, and control mechanisms to make AI coding agents work reliably. The course synthesizes best practices from OpenAI and Anthropic on building effective harnesses for long-running agents.