BizBench: Unveiling Hidden Challenges in Deploying Long-Horizon Agents for Real-World Business
Existing long-horizon agent benchmarks often emphasize final task success while providing limited assessment of intermediate execution validity. Yet real-world business operations require agents to navigate extensive, potentially outdated or conflicting policies while satisfying critical constraints throughout execution. These requirements are particularly consequential in high
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Existing long-horizon agent benchmarks often emphasize final task success while providing limited assessment of intermediate execution validity. Yet real-world business operations require agents to navigate extensive, potentially outdated or conflicting policies while satisfying critical constraints throughout execution. These requirements are particularly consequential in high
This benchmark addresses a critical gap in evaluating long-horizon agents, focusing on intermediate execution validity in high-stakes business environments. For ML engineers, understanding these challenges is crucial for developing robust and reliable AI systems that can operate effectively and safely in complex real-world scenarios, particularly where financial or regulatory compliance is paramount.