Compiling repeated SOP nodes into environment-grounded, versioned tools cuts production p50 latency by 42% and end-to-end error rate by up to 53% in a 44-node fulfillment-center alarm-triage agent.
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track , pages=
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An evaluation suite for LLM retail-return agents under ambiguous policy shows frontier models rarely converge on the same resolution.
citing papers explorer
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Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems
Compiling repeated SOP nodes into environment-grounded, versioned tools cuts production p50 latency by 42% and end-to-end error rate by up to 53% in a 44-node fulfillment-center alarm-triage agent.
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DRIP-R: A Benchmark for Decision-Making and Reasoning Under Real-World Policy Ambiguity in the Retail Domain
An evaluation suite for LLM retail-return agents under ambiguous policy shows frontier models rarely converge on the same resolution.