ATLAS: Dual-Horizon Diagnostic Evaluation for Industrial Tool-Use Agents
Quick summary
arXiv:2608.30685v1 Announce Type: new Abstract: 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 c
Key takeaways
- arXiv:2608.30685v1 Announce Type: new Abstract: 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.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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