Beyond Binary Correctness: Scaling Evaluation of Long-Horizon Agents on Subjective Enterprise Tasks

1. 1Identify subjective enterprise tasks that require long-horizon agents
2. 2Develop a three-pillar evaluation design that considers organizational goals, user intent, and intermediate artifacts
3. 3Implement LH-Bench, a benchmarking framework for evaluating long-horizon agents on these tasks
4. 4Analyze results to improve agent performance and adapt to changing enterprise needs

AI researchers and engineers working on long-horizon agents and enterprise tasks can benefit from this evaluation framework, as it provides a more nuanced assessment of agent performance

💡 Evaluating long-horizon agents requires considering organizational goals, user intent, and intermediate artifacts, not just binary correctness

💡 Move beyond binary correctness when evaluating long-horizon agents on subjective enterprise tasks!