Princeton-led team outlines challenges and research agenda for AI agent evaluation

A Princeton-led research team, including collaborators from Stanford, the UK AI Security Institute, and Brookings, published a paper titled *Emerging Trends in AI Agent Evaluation* that synthesizes challenges and opportunities in evaluating AI agents. The authors identify 33 emerging trends across three dimensions: the science and methods of evaluation, the research agenda, and the evaluation ecosystem.

The paper highlights that evaluating AI agents is fundamentally different from evaluating models, as agents operate in tool-rich environments and may take shortcuts that obscure true performance. Evaluators must now scrutinize entire trajectories—such as tool use and intermediate steps—rather than relying solely on final outcomes. The authors note that this shift has made evaluation more complex and resource-intensive, with some benchmarks (e.g., ExploitBench) costing tens of thousands of dollars in compute credits to run, excluding the engineering effort required to set up and maintain evaluation infrastructure.

The authors argue that the scope of evaluation must expand beyond technical benchmarks to include sociotechnical dimensions, such as how agents interact with humans and institutions. They point to evidence that lab-based evaluations do not always translate to real-world performance, citing a randomized controlled trial by METR that found agent tools increased overall completion time for software development tasks despite expectations to the contrary.

Independence and participation in agent evaluation are constrained by several factors, including funding, access to frontier models, and fears of retaliation. The paper notes that frontier labs’ release of summarized Chain of Thought traces—rather than raw traces—further limits evaluators’ ability to conduct thorough assessments. The authors emphasize that meaningful independence requires not only financial resources but also transparent access to models and traces, as well as protections for evaluators to share findings without fear of retaliation.

The paper calls for an interdisciplinary research agenda that includes both technical and sociotechnical evaluation methods. It encourages participation from disciplines not yet engaged in AI evaluation and urges current evaluators to examine their methodological assumptions. For field-builders such as funders and conference organizers, the authors recommend raising standards for validity and rigor, while leaderboard aggregators are urged to incorporate best practices for reporting and to highlight the rising costs of evaluation.

The authors conclude with actionable recommendations for stakeholders: prospective evaluators should identify roles and dimensions of independence that align with their expertise; current evaluators should embrace interdisciplinary perspectives; funders and coalitions should foster collaboration and set higher standards; and deployers should contribute real-world insights to inform evaluation work.