Despite the hype about these agents being co-workers, from our experience, these agents are likely to work best when you consider them as tools that amplify existing skills, not because the autonomous co-workers the marketing language implies. They’ll produce impressive drafts fast but still require constant human course-correction.
The Frontier launch got here just three days after OpenAI released a brand new macOS desktop app for Codex, its AI coding tool, which OpenAI executives described as a “command center for agents.” The Codex app lets developers run multiple agent threads in parallel, each working on an isolated copy of a codebase via Git worktrees.
OpenAI also released GPT-5.3-Codex on Thursday, a brand new AI model that powers the Codex app. OpenAI claims that the Codex team used early versions of GPT-5.3-Codex to debug the model’s own training run, manage its deployment, and diagnose test results, much like what OpenAI told Ars Technica in a December interview.
“Our team was blown away by how much Codex was in a position to speed up its own development,” the corporate wrote. On Terminal-Bench 2.0, the agentic coding benchmark, GPT-5.3-Codex scored 77.3%, which exceeds Anthropic’s just-released Opus 4.6 by about 12 percentage points.
The common thread across all of those products is a shift within the user’s role. Reasonably than merely typing a prompt and waiting for a single response, the developer or knowledge employee becomes more like a supervisor, dispatching tasks, monitoring progress, and stepping in when an agent needs direction.
On this vision, developers and knowledge staff effectively turn into middle managers of AI. That’s, not writing the code or doing the evaluation themselves, but delegating tasks, reviewing output, and hoping the agents underneath them don’t quietly break things. Whether that may come to pass (or if it’s actually idea) continues to be widely debated.
