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A curated, bilingual (English/Chinese) collection of resources, tools, templates, and implementations for Harness Engineering — the practice of shaping the environment around AI agents so they work reliably.
Harness Engineering sits at the intersection of context management, mechanical enforcement, evaluation, orchestration, and safe autonomy. This repo goes beyond link aggregation: it includes ready-to-use templates, deep concept guides, and a complete map of the emerging ecosystem.
Harness Engineering: Harnessing Codex in an Agent-First World — OpenAI's flagship field report. 3→7 engineers, 5 months, ~1M lines of code, ~1,500 PRs. Covers architectural constraints, repo-local instructions, browser validation, and telemetry.
Effective Harnesses for Long-Running Agents — Anthropic's core guide on initializer agents, feature lists, init.sh, self-verification, and handoff artifacts across many context windows.
The "Ralph Wiggum Loop" is the canonical Harness Engineering execution pattern: agents run in a loop with fresh context each iteration until the task completes.
Zero-code platform for auto-generating production-grade AI agents using HE principles — unified tools, skills, memory, orchestration, constraints, feedback loops.