Google paper proposes OO-Harness: evolving specialized agent harnesses by task type
A new Google paper proposes OO-Harness, which evolves agent harnesses by code class: each specialized harness inherits from a parent class and changes only the parts that its task cluster needs adjusted, after which a router selects a specialized harness or keeps the base result based on the base run's outcome. On AutomationBench business workflows, this method scores 77.41%, versus 61.62% for the base harness and 63.92% for the global method Meta-Harness; on unfamiliar applications it retains the base harness and maintains a 59.18% pass rate, while Meta-Harness's global harness drops to 44.90%. The authors recommend using the base harness as a fallback for mixed workloads and routing to a specialized harness only when a run exposes a known problem.
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The original text presents a score comparison between OO-Harness and the global harness method on AutomationBench, showing the benefits and costs of task-specific harnesses.
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New Google paper shows that agents score higher when each task gets a specialized harness from a shared family, not 1 global harness.
An agent harness is the prompts, tools and control code around a model. Most tuning methods evolve 1 global harness, though a fix for 1 task type can hurt another.
OO-Harness evolves harnesses as code classes, and each specialist inherits from a parent and changes only what its cluster needs. A router reads a normal base run and picks a specialist or keeps the base result.
On AutomationBench business workflows, it scored 77.41% versus 61.62% for the base harness and 63.92% for Meta-Harness, a global method. On unfamiliar apps, it kept the base harness and held 59.18% pass rate, while Meta-Harness's global harness fell to 44.90%.
For mixed workloads, keep the base harness as a fallback and route to specialists only when a run shows a known problem.