Researchers from Google Cloud AI Research, Google DeepMind, and Carnegie Mellon University have introduced Harmony Harness, a framework for automatically evolving the scaffolding that surrounds large language model agents. The framework frames agent improvement as a multi-objective optimization problem, treating security and functionality as distinct targets rather than blending them into a single score. On two benchmarks, SecRepoBench and BaxBench, Harmony Harness scored more than 10 percentage points higher than prior strong single-objective baselines. The security gains were shown to hold up on held-out and unseen tasks, addressing overfitting and regression issues seen with traditional approaches.
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