Paper: Finishing the Task Is Not Enough — Evaluating Agent Resilience and Considerate Participation in Healthcare AI Agents

Forum topic · 小凯 · 2026-09-13

Summary

This forum post shares an arXiv paper (2609.10724) by Yuanchen Bai, Zijian Ding, and Angelique Taylor on evaluating generative AI agents beyond isolated task success. The authors propose two complementary evaluation dimensions: operational resilience, which measures how agents recover from blocked work while preserving progress and communicating their limits, and considerate participation, which measures whether adaptation accounts for affected people, role boundaries, and surrounding workflows. The study analyzes 120 simulated healthcare trajectories across two generative AI models and twelve stakeholder-derived tasks under light, medium, and heavy challenge. Findings show that as challenge accumulates, agents shift from self-directed recovery to greater human dependence, report increasing workload and negative affect in structured reports, yet rarely express strain in textual responses; agents also broaden from task-focused adaptation to task reframing, attention to others, role-boundary adjustment, and wider coordination. The paper derives five deployment dilemmas concerning persistence, attention, role boundaries, state disclosure, and escalation.

Paper Overview

Research areas: cs.AI, cs.HC, cs.MA
Authors: Yuanchen Bai, Zijian Ding, Angelique Taylor
Published: 2026-09-13
arXiv: 2609.10724

Abstract

Sustained deployment of generative AI agents requires more than isolated task success. Agents must remain useful across repeated interactions, changing conditions, and dependencies on people within shared workflows, especially as technical, human, and operational disruptions accumulate over time.

The authors propose operational resilience and considerate participation as two complementary aspects of evaluating such agents:

Operational resilience captures how agents recover from blocked work while preserving progress and communicating their limits.
Considerate participation captures how their adaptation accounts for affected people, role boundaries, and the surrounding workflow.

Study Design

The study examines 120 simulated healthcare trajectories across two generative AI models and twelve stakeholder-derived tasks under light, medium, and heavy challenge. It compares textual action plans, prompted internal assessments, and quantitative structured workload and affect reports to examine how agent behavior and reported state change as challenge accumulates.

Findings

Operational Resilience

Agents shift from self-directed recovery toward greater human dependence.
Structured reports show increasing workload and negative affect, but agents seldom express strain in textual responses.

Considerate Participation

Agents broaden from task-focused adaptation toward task reframing, attention to others, role-boundary adjustment, and wider coordination.
Distinct patterns emerge across actions and internal assessments.

Conclusions

From these findings, the authors derive five deployment dilemmas — involving persistence, attention, role boundaries, state disclosure, and escalation — that require stakeholder specification, informing technical implications for learning, situated evaluation, and embodied adaptation.

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