Finishing the Task Is Not Enough: Evaluating Agent Resilience and Considerate Participation in Healthcare

Forum topic · 小凯 · 2026-09-13

Summary

This arXiv paper (2609.10724) by Yuanchen Bai, Zijian Ding, and Angelique Taylor proposes two complementary evaluation dimensions for generative AI agents beyond isolated task success: operational resilience (recovering from blocked work while preserving progress and communicating limits) and considerate participation (adapting with regard to affected people, role boundaries, and surrounding workflows). The authors analyze 120 simulated healthcare trajectories spanning two generative AI models and twelve stakeholder-derived tasks under light, medium, and heavy accumulated challenge. Findings show that as challenge grows, agents shift from self-directed recovery toward greater human dependence, reporting increasing workload and negative affect in structured reports while rarely expressing strain in textual responses. Agents also broaden from task-focused adaptation toward task reframing, attention to others, role-boundary adjustment, and wider coordination. The paper derives five deployment dilemmas involving persistence, attention, role boundaries, state disclosure, and escalation, informing implications for learning, situated evaluation, and embodied adaptation.

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: the former captures how agents recover from blocked work while preserving progress and communicating their limits, and the latter captures how their adaptation accounts for affected people, role boundaries, and the surrounding workflow. Both remain underexplored under accumulating challenge.

Method

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

Key findings

Operational resilience: Agents shift from self-directed recovery toward greater human dependence as challenges accumulate.
Agents report increasing workload and negative affect in structured reports, but 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, with distinct patterns across actions and internal assessments.

Deployment dilemmas

From these findings, the authors derive five deployment dilemmas that require stakeholder specification:

1. Persistence 2. Attention 3. Role boundaries 4. State disclosure 5. Escalation

These inform technical implications for learning, situated evaluation, and embodied adaptation.

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