AI

What Did the Agent Actually Do? Evidence-Grounded Oversight for Long-Horizon Agents

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Briefings

This article presents AgentMonBench, a benchmark for evaluating long-horizon autonomous agents, and introduces the Evidence-Grounded Behavior Graph (EBG), a training-free method to organize evidence into behaviors. Experiments across eight models show EBG improves decision identification and evidence localization compared to direct context access, with gains persisting across input scales and hyperparameters.

Key points

• The study introduces AgentMonBench, a benchmark with three subsets covering alignment between requirements and behavior, and awareness of consequential autonomous decisions.
• The Evidence-Grounded Behavior Graph (EBG) groups source-linked evidence into behaviors and organizes their relationships into a graph for interpretation.

Context

As users shift from individual decisions to overseeing autonomous execution, determining which agent decisions warrant verification is difficult. This study addresses that challenge by proposing EBG to help monitors interpret behavior in context.

Limitations

Experiments were conducted across eight models with no specific city, region, or country identified. Results are based on software-engineering benchmark experiments and do not include real-world application data.

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