Designing Agentic AI Workflow Portfolios under Imperfect Selection and Compute Cost

The paper investigates how to design portfolios of agentic AI workflows that vary in reasoning strategy, verification structure, and compute cost. It proposes a portfolio-and-selector framework where multiple workflow executions are run and the best output is chosen, balancing additional compute with potential gains in accuracy. The authors develop exact and approximate optimization methods, evaluate them on three datasets, and show modest improvements over the best single workflow.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• agents