AI agent swarms are a massive waste of tokens with zero quality gain, says OpenAI Codex developer
OpenAI Codex developer Eric Provencher warns that running more than two parallel AI sub-agents burns tokens without improving output quality.
Eric Provencher, a Codex developer at OpenAI, said on X that parallel agent swarms incur a 'coordination tax' because agents redundantly verify each other's work. He cited a project where 1,393 Fable agents spent $20,000 in tokens refactoring a single Python file, arguing a single Astra agent could have done it for a fraction of the cost. He recommends delegating tasks to threads that notify the main agent on completion rather than constant polling, and acknowledged OpenAI still needs better solutions.
- Provencher says over two parallel sub-agents yield zero quality gain
- Cited case: 1,393 Fable agents spent $20,000 refactoring one Python file
- Recommends completion-notification delegation over status polling
- System prompts execute redundantly across sub-agents lacking context
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Eric Provencher, a Codex developer at OpenAI, is warning against reckless token spending in agentic workflows. In posts on X, he argues that more than two parallel sub-agents almost always burn tokens without improving quality. Agents don't trust each other, so they end up "double-checking everyone's homework."
Provencher calls this the "coordination tax" and says it's "really hard to keep so many parallel lanes running without spinning out and burning tokens on excessive verification." When a project surfaced where someone spent $20,000 in tokens refactoring a single Python file with 1,393 Fable agents, he commented that a single Astra agent could have done it for a "fraction of the cost." Agent swarms may save time, but the token overhead is a "trap," he writes.
As a fix, he recommends delegating tasks to separate threads that only notify the main agent once done, instead of constantly polling for status. System prompts also get executed multiple times across sub-agents, and sub-agents often lack needed context, causing redundant work. Provencher acknowledges OpenAI still needs to ship better solutions here.
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