Frontier Research
Open problems and emerging directions in agent evaluation.
Cross-Domain Generalization Measurement
Measuring whether agent capabilities transfer across domains – from coding to research, from customer service to data analysis – is essential for predicting real-world performance and designing benchmarks that reflect genuine competence rather than narrow specialization.
Evaluating Emergent System Behavior
Emergent behaviors arise from component interactions in ways that no single component exhibits alone, making them invisible to unit-level testing and demanding fundamentally different evaluation strategies.
Evaluation for Learning Agents
Agents that improve through feedback, experience, or self-modification present a moving-target evaluation problem where capabilities change during the assessment period, requiring dynamic evaluation frameworks that measure learning itself, not just learned outcomes.
Human-Agent Collaboration Evaluation
Evaluating human-agent teamwork requires measuring joint performance, handoff quality, shared understanding, and trust calibration – metrics that neither human-only nor agent-only evaluation frameworks can capture.
Long-Horizon Task Evaluation
Evaluating tasks that span hours, days, or weeks requires fundamentally different approaches than short-task benchmarks, including milestone-based progress measurement, context persistence strategies, and principled handling of environmental change.
Multi-Agent Evaluation Theory
Evaluating systems of cooperating and competing agents requires game-theoretic metrics, communication analysis, and coordination quality measures that go far beyond single-agent performance scoring.
The Evaluation Scaling Problem
As AI agents approach and exceed human-level capability in specific domains, the fundamental assumption underlying all evaluation – that the evaluator is more capable than the evaluated – breaks down, creating an asymmetry that may define the central challenge of advanced AI development.