Designing Agentic AI Workflow Portfolios under Imperfect Selection and Compute Cost

📅 2026-09-16
📈 Citations: 0
Influential: 0
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🤖 AI Summary
研究通过组合多种AI工作流并选择最佳结果的方法,以解决单一最优工作流可能错过正确答案的问题,使用线性规划等技术优化组合。
📝 Abstract
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 compute and introduce plausible distractors that complicate final selection. We formulate this as a workflow portfolio problem in which the firm jointly chooses run size and allocation across workflow types. We summarize selector quality through an odds-lift index and derive sharp bounds on the value of workflow variety. For finite workflow pools, we develop exact formulations, linear programming relaxations, randomized rounding procedures, and computable performance certificates. For large implicit workflow classes, we derive a finite-dimensional dual and an ellipsoid method using a pricing oracle to identify workflows with high weighted accuracy net of recurring compute cost. Under a weak condition, the method obtains a near-optimal solution to the relaxation with polynomially many oracle calls. We evaluate the framework on three datasets: ABCD, Schema-Guided Dialogue, and HotpotQA. Relative to the best standalone workflow, portfolio optimization improves held-out selector accuracy by 3.1, 7.5, and 0.9 percentage points, respectively. Dual-guided workflow generation adds 3.5 points on ABCD and 24.1 on HotpotQA, with no additional gain on Schema-Guided Dialogue.
Problem

Research questions and friction points this paper is trying to address.

Agentic AI
workflow portfolio
compute cost
selector quality
performance optimization
Innovation

Methods, ideas, or system contributions that make the work stand out.

workflow portfolio
selector quality
compute cost
linear programming relaxations
dual-guided workflow generation
Mojtaba Abdolmaleki
Mojtaba Abdolmaleki
Ross School of Business, University of Michigan
Stefanus Jasin
Stefanus Jasin
Unknown affiliation
B
Boyu Wang
TrueFoundry
B
Boyu Wang
School of Management, University of San Francisco, United States