Cohere tests found that a strong lead AI agent paired with cheaper planning agents could match a more expensive team. Using Luna planners under a Sol lead cut costs by 60% without reducing performance against an all-Sol team, but results varied by task.
Cohere senior researcher Nouha Dziri presented tests of AI agent teams using a lead that delegates work and combines reports in a shared workspace. Pairing the more capable Sol lead with cheaper Luna planners delivered the same result as using Sol throughout, at a lower cost.
The tests used ProgramBench, which asks models to reconstruct a program from its binary and specification so it compiles and passes tests. Restricting workers to exploration achieved the full team's performance while halving costs, suggesting that discovering what to build was the main bottleneck in this setup.
For companies building AI agents, the findings support testing where extra agents help before paying for them throughout a workflow. They do not establish a general cost advantage: another benchmark, Commit Zero, rewarded implementation help when documentation and tests were supplied.
Builders could reduce spending by testing cheaper planning workers before assigning expensive models to every role.
The contrasting benchmark results make the workflow bottleneck more important than a blanket preference for larger agent teams.
Savings outside this setup remain unproven, and public costs per run are still needed to judge the economics.
Written automatically by the Made in 10 Newsroom from the source above.
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