AI Insights - Boosting Problem-Solving with Structured Methodologies
The recent blog posts highlight emerging methodologies for improving complex problem-solving with advanced AI models like gpt-5.2-codex. Central trends include the adoption of structured, living documents—specifically PLANS.md for comprehensive, self-contained execution plans and AGENTS.md for shorthand agent guidance. These practices prioritize clarity, ongoing documentation, and clear validation of outcomes to enable both expert and novice users to achieve complex project goals efficiently and transparently. The overall movement is toward more reliable, accessible, and efficient use of coding agents for extended, collaborative tasks.
New Cookbook Recipes
codex_exec_plans.md
Source: openai/openai-cookbook
The blog post discusses the use of PLANS.md and AGENTS.md for facilitating complex problem-solving with Codex, specifically the gpt-5.2-codex model. PLANS.md serves as a comprehensive execution plan (ExecPlan), guiding users through research, design, and implementation processes over extended hours. The document emphasizes the need for detailed, self-contained plans that allow novice users to achieve project goals without prior context.
Key requirements include maintaining “living documents” that record progress, decisions, and discoveries, ensuring clarity through plain language, and validating outcomes through observable results. Meanwhile, AGENTS.md offers shorthand guidance for when to use these execution plans. Overall, this methodology aims to enhance the efficiency and reliability of coding agents in executing complex tasks.