AI agent swarms are a massive waste of tokens with zero quality gain, says OpenAI Codex developer
Eric Provencher, a Codex developer at OpenAI, warns 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 and 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.
One approach he suggests is delegating tasks to separate threads that notify the main agent only once done, instead of constantly polling for status. System prompts also add up across sub-agents, and without enough context, sub-agents make redundant tool calls. "Duplicate effort is the big one for sure," Provencher agrees. He acknowledges OpenAI still needs to ship better solutions here. Ad Ad
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