# ai-engineering

OpenAI's Cookbook frames Codex Goals as a thread-scoped completion contract: the objective persists, but completion must be checked against evidence. This post fills in the official spec angle around GP-192, GP-197, and GP-207.

Jarrod Watts looked inside Codex Goals and found that it solves early stopping, not long-run drift. The real long-running agent stack needs upfront clarification, multi-agent review, and memory outside the context window.

A reverse-engineering tour of Claude Code's 55 directories and 331 modules: execution loop, context compaction, subagents, permissions, hooks, and the core lesson that environment design—not the model alone—determines agent outcomes.

The Claude Code source leak had everyone excited about KAIROS and model codenames. But the same codebase had a 3,167-line function, zero tests, silent model downgrades, and regex emotion detection. These aren't just Anthropic's mistakes — they're AI-generated code's default failure modes.

Paweł Huryn proposes the Vibe Engineering framework: instead of accepting raw AI output, use Context Engineering, Intent Engineering, and Sub-agent orchestration to upgrade AI coding from 'lucky demos' to 'reliable products'.