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Rohan Paul· @rohanpaul_ai · X· 2026-10-22 12:00 · Original publication time AI score54

Google paper proposes task-specific harnesses for AI agentsMachine translation

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The Google paper describes OO-Harness, which uses task-specific agent harnesses and decides whether to switch after a base run. The reported score on AutomationBench business workflows was 77.41%, compared with 61.62% for the base harness and 63.92% for the global Meta-Harness method. On unfamiliar apps, it kept the base harness and recorded a 59.18% pass rate.

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Rohan Paul@rohanpaul_ai

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.

12:00 · 2026-10-22 · UTC+8

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