Codex Knowledge Base
The Asymmetric Feedback Problem: Why Coding Agents Silently Fail at Business Logic
Your coding agent just wrote a purchasing module, ran all the tests, and reported success. The code compiles, the API calls return 200s, and the database.
I Used This Setup → This Is What Changed: An Agentic Engineering Case Study
Agentic engineering promises a fundamental shift in how software gets built. But promises are cheap. What actually changes when you commit to an agent-first.
Three Terminals Meet the Editor: Codex CLI vs Claude Code vs Gemini CLI vs Cursor
Cursor 3, released on 2 April 2026, ships parallel cloud agents, a visual design mode, and a tabbed agent window for managing local, cloud.
The DNA of Coding Agents: Codex CLI vs Gemini CLI vs Claude Code vs Pi — System Prompts Compared
The system prompt is the soul of a coding agent. It is the document that transforms a general-purpose language model into an opinionated software engineer.
Does Pi Outperform Codex CLI for Local Models? A Harness Comparison on the NVIDIA GB10
The system prompt tax is the cost you pay before your coding agent writes a single line of code.
Codex CLI Offline Mode: Local Models, Air-Gapped Setups, and What Works Without Internet
Can I run Codex CLI without internet? is one of the most common search queries that leads nowhere.
Your First 30 Minutes with Codex CLI: From Install to First Fix
Thirty minutes. That is all you need to go from zero to watching an AI agent autonomously diagnose a bug, write a fix, run the tests.
Transferring ChatGPT Conversations to Codex CLI
Many developers start their thinking in ChatGPT — brainstorming architecture, researching APIs, sketching out approaches with web search — then need to hand.
The Subagent Resource Leak Problem: Why MCP Process Trees Accumulate and What McpConnectionManager::shutdown() Fixes
If you run Codex CLI with multiple MCP servers and use subagent workflows, you have almost certainly experienced the symptoms.
Self-Evolving Agents in Practice: Implementing the OpenAI Cookbook Retraining Loop with Codex CLI Hooks
Most agentic systems plateau after proof-of-concept. The agent handles the happy path, but edge cases accumulate, prompts drift, and performance quietly.