Swapping only the orchestration layer around six AI models cut token cost per task 41% and latency 44% with quality at parity, making orchestration a bigger cost lever than model choice.
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ISOPro replaces learned reward models with deterministic verifiers in a continuous evaluation setup for LLMs, delivering larger average capability gains than GRPO-LoRA across small models in scheduling and MBPP domains while characterizing a buffer-skew failure mode.
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The Harness Effect: How Orchestration Design Sets the Token Economics of Enterprise Agentic AI
Swapping only the orchestration layer around six AI models cut token cost per task 41% and latency 44% with quality at parity, making orchestration a bigger cost lever than model choice.
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Beyond Static Snapshots: A Grounded Evaluation Framework for Language Models at the Agentic Frontier
ISOPro replaces learned reward models with deterministic verifiers in a continuous evaluation setup for LLMs, delivering larger average capability gains than GRPO-LoRA across small models in scheduling and MBPP domains while characterizing a buffer-skew failure mode.