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ATLAS: Dual-Horizon Diagnostic Evaluation for Industrial Tool-Use Agents

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Large language model (LLM) agents are increasingly deployed in user-facing services that require iterative tool use under dynamic business conditions. Reliable evaluation is essential for sustained improvement: it must reveal capability deficiencies, inform priorities, and assess interventions. Yet industrial agent service unfolds both through the iterative trajectory of a current request and through continued user interaction. Final-outcome assessment can therefore obscure where deficiencies arise and whether later service remains aligned with context from earlier exchanges. We propose ATLAS,

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