Codex benchmark tests when multi-agent AI engineering actually beats solo work

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A new dependency-free benchmark published in Codex How To evaluates whether splitting a software task across multiple AI agents genuinely outperforms a single-agent approach. The test uses an incident-response application with two clearly separated code surfaces — backend persistence and a browser client — assigned to distinct agents under a controller. In an initial smoke run, both single-agent and orchestrated setups passed all supplied tests and external evaluator checks, though the multi-agent run additionally produced live browser interaction evidence. The benchmark concludes that parallel agents are only justified when tasks have frozen interfaces, exclusive write paths, and coordination costs that are measurably outweighed by gains. The framework is designed to be rerun repeatedly so teams can build evidence before committing to an orchestrated workflow.

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ProgrammingDEV Community ·

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