Lessons from Building Evals for Financial AI Agents (www.primerapp.com)

The article discusses the development of internal evaluation benchmarks for financial AI agents, emphasizing the limitations of existing public benchmarks that fail to capture the complexity and nuance of equity research. The author shares insights gained from transitioning from traditional financial analysis to enhancing AI's role in evaluating stock research. Key lessons highlight the inadequacy of absolute scoring methods and the importance of relative scoring to assess the quality of competing outputs effectively. By utilizing stronger AI models as judges and allowing them access to underlying data, the evaluations not only identify more nuanced differences but also reflect the real-world practices of portfolio managers who compare multiple analyses. The significance of these insights for the AI/ML community lies in the recognition that judgment calls are central to deep equity research, making traditional benchmarking insufficient. The article advocates for an approach that incorporates comparative evaluation, whereby multiple outputs are assessed side-by-side to reveal their strengths and weaknesses. This method not only enables a better understanding of the performance of AI models, such as differentiating between versions like GPT-5.4 and GPT-5.5, but also paves the way for more autonomous financial research, ultimately enhancing the capability of AI agents in generating insightful and actionable investment analyses.