The power of tools like Codex lies beyond writing software; they are becoming general 'computer use agents' that leverage the command line to automate personal tasks. This includes organizing messy file directories, managing desktop files, or sorting emails, reclaiming the power of the terminal for everyday automation.
Standard benchmarks fall short for multi-turn AI agents. A new approach is the 'job interview eval,' where an agent is given an underspecified problem. It is then graded not just on the solution, but on its ability to ask clarifying questions and handle changing requirements, mimicking how a human developer is evaluated.
To increase developer adoption, OpenAI intentionally trained its models on specific behavioral characteristics, not just coding accuracy. These 'personality' traits include communication (explaining its steps), planning, and self-checking, mirroring best practices of human software engineers to make the AI a more trustworthy pair programmer.
OpenAI recommends a bifurcated approach. Startups building bleeding-edge, code-focused agents should use the specialized Codex model line, which is highly opinionated and optimized for its tool harness. Applications requiring more general capabilities and steerability across various tools should use the mainline GPT model instead.
A major trend in AI development is the shift away from optimizing for individual model releases. Instead, developers can integrate higher-level, pre-packaged agents like Codex. This allows teams to build on a stable agentic layer without needing to constantly adapt to underlying model changes, API updates, and sandboxing requirements.
AI models develop strong 'habits' from training data, leading to unexpected performance quirks. The Codex model is so accustomed to the command-line tool 'ripgrep' (aliased as 'rg') that its performance improves significantly when developers name their custom search tool 'rg', revealing a surprising lack of generalization.