SOURCE-LINKED INTELLIGENCE
Designing Agentic AI Workflow Portfolios under Imperfect Selection and Compute Cost
Agentic AI systems often approach the same task through multiple workflows that differ in reasoning strategy, verification structure, and compute cost. A natural deployment policy is to use the workflow with the highest average performance, but this can be suboptimal because different workflows may succeed on different instances. We study a portfolio-and-selector paradigm in which a firm runs multiple workflow executions and selects the final answer after observing their outputs. Additional executions may uncover correct answers that the best standalone workflow misses, but they consume comput
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T05:02:11.000Z
First collected: 2026-09-20T08:20:57.646Z. This is not the publication date.