![[2602.22442] A Framework for Assessing AI Agent Decisions and Outcomes in AutoML Pipelines](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.22442] A Framework for Assessing AI Agent Decisions and Outcomes in AutoML Pipelines
Summary
This article presents a framework for evaluating AI agent decisions in AutoML pipelines, emphasizing decision-centric metrics over traditional outcome-based evaluations.
Why It Matters
As AI systems become more prevalent in automated machine learning (AutoML), understanding the decision-making processes of these agents is crucial. This framework addresses gaps in current evaluation practices, promoting transparency and reliability in AI decision-making, which is essential for trust and governance in autonomous systems.
Key Takeaways
- The proposed Evaluation Agent (EA) assesses AI decisions in AutoML without interfering with operations.
- EA evaluates decisions based on validity, reasoning consistency, model quality risks, and counterfactual impacts.
- The framework identifies decision flaws that traditional outcome metrics may overlook.
- Results indicate the EA can detect faulty decisions with high accuracy (F1 score of 0.919).
- This approach shifts the focus from outcome-based evaluations to a more nuanced understanding of AI decision-making.
Computer Science > Artificial Intelligence arXiv:2602.22442 (cs) [Submitted on 25 Feb 2026] Title:A Framework for Assessing AI Agent Decisions and Outcomes in AutoML Pipelines Authors:Gaoyuan Du, Amit Ahlawat, Xiaoyang Liu, Jing Wu View a PDF of the paper titled A Framework for Assessing AI Agent Decisions and Outcomes in AutoML Pipelines, by Gaoyuan Du and 3 other authors View PDF HTML (experimental) Abstract:Agent-based AutoML systems rely on large language models to make complex, multi-stage decisions across data processing, model selection, and evaluation. However, existing evaluation practices remain outcome-centric, focusing primarily on final task performance. Through a review of prior work, we find that none of the surveyed agentic AutoML systems report structured, decision-level evaluation metrics intended for post-hoc assessment of intermediate decision quality. To address this limitation, we propose an Evaluation Agent (EA) that performs decision-centric assessment of AutoML agents without interfering with their execution. The EA is designed as an observer that evaluates intermediate decisions along four dimensions: decision validity, reasoning consistency, model quality risks beyond accuracy, and counterfactual decision impact. Across four proof-of-concept experiments, we demonstrate that the EA can (i) detect faulty decisions with an F1 score of 0.919, (ii) identify reasoning inconsistencies independent of final outcomes, and (iii) attribute downstream perform...
