OpenAI Codex developer says agent swarms mostly waste tokens

Eric Provencher, a developer on OpenAI's Codex, says running more than two parallel AI sub-agents on a task rarely improves quality because the agents don't trust each other's work and end up redundantly re-checking it. He cites a case where 1,393 agents spent $20,000 in tokens on a Python refactor that one agent could have done far more cheaply.

Why it matters: This is direct pushback from inside a major AI lab against the popular multi-agent-swarm pattern being marketed as the next step in agentic coding tools. It's a useful data point for teams deciding how to architect AI coding workflows, and it tempers claims from agent-orchestration vendors.