Hung-yi Lee opens with a small model fixing a bug. gemma-4-E2B-it can't find parser.py, so it writes a fake one and declares victory. Add three short sections (the current environment, how to work, what counts as done) and the same model runs ls, cat, edits the file and runs the tests. The lecture splits the harness into three levers: natural language shapes the model's frame of mind (AGENTS.md), tools set its capability boundary (SWE-agent's ACI, rewriting CLIs for agents), and workflows control its behavior (the Ralph loop, Anthropic's long-running harnesses). The second half covers three extensions: scolding an agent can backfire, how a life-long agent learns from verbal feedback, and why evaluating agents is hard. It ends with agents improving their own harness (Meta-Harness).
Looplane resolves user and root-to-leaf project instructions before rendering named prompt sections for runtime, skills, workspace state, and the latest 20 explicit memories. The pipeline is traceable and reloadable, but it is not semantic memory and repository text does not become system authority.
An OpenAI internal team spent 5 months with 3 people and 0 lines of hand-written code, delivering a complete product using Codex. This article distills their core lessons on AGENTS.md design, repo-local knowledge bases, architecture enforcement, and entropy management.
Claude Code only reads CLAUDE.md; Codex only reads AGENTS.md. Teams using both end up maintaining two identical files. Fix: make CLAUDE.md a symlink pointing to AGENTS.md — one source of truth.
Skill paths are almost always runtime-specific. AGENTS.md is the reliable way to share rules across agents. Put personal reusable capabilities in each agent's supported global directory; put project workflows inside the repo.
Standing orders grant an agent permanent operating authority for a defined program, written into AGENTS.md and injected into every session. They define what it may do; automations define when — and the automation prompt should reference the standing order rather than duplicate it.
Every Claude Code session starts with a clean context window. Three memory mechanisms carry knowledge across sessions: CLAUDE.md files loaded every session, .claude/rules/ files that load conditionally via paths frontmatter, and auto memory Claude writes itself. All CLAUDE.md layers are concatenated into context — not inherited by override. This guide covers layer behavior, @path imports, monorepo strategies with nested CLAUDE.md, and sharing one instruction file across tools via @AGENTS.md.
Hooks are automated safety nets (blocking bad commits), Skills are interactive workflows (running checks + auto-fixing), and instruction files (CLAUDE.md / AGENTS.md) are behavioral guidelines. Each layer operates independently, but together they enable an AI agent to automatically run lint, typecheck, and build checks before every commit.