Causal Framework Evaluates Interventions for AI Agent Trajectories
Original title: Knowing When to Intervene: Causal Evaluation of AI Agents
First method estimates causal effects of randomized interventions in AI agent trajectories using small datasets.
Static benchmarks limit AI agent evaluations. Authors develop a causal inference framework to assess intervention value at deployment. Randomized trajectories enable estimation of dynamic causal effects for contextual policies. Applications to tau-bench reveal varying effects across actions and states, enabling safety leaderboards.
- Framework uses small dataset of randomized verification or screening trajectories
- Estimates dynamic causal effects for rich class of intervention policies
- Structural parameters construct safety leaderboards for audit prioritization
- Intervention effects vary substantially across agent actions and states
From the original article
1 min read at arxiv.orgCurrent evaluations of agentic AI systems primarily rely on benchmarks. However, the static nature of benchmarks makes them uninformative about the value of auditing interventions at deployment time. For reliable deployment, we often need to determine within an agent's trajectory whether or not to intervene, e.
By Ayush Sawarni · arxiv.org
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