{"paper":{"title":"Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Autonomous AI agents with optimized reasoning models outperform human teams in supply chain management by reducing costs up to 67 percent, but require post-training to control decision unreliability.","cross_cats":["cs.LG","cs.MA","cs.SY","eess.SY"],"primary_cat":"cs.AI","authors_text":"Andre P. Calmon, Carol Xuan Long, David Simchi-Levi, Feng Zhu, Flavio P. Calmon, Huangyuan Su","submitted_at":"2026-05-16T15:11:35Z","abstract_excerpt":"This paper studies autonomous generative AI agents in multi-echelon supply chains using the MIT Beer Game. We identify four inference-time levers that shape performance: model selection, policies and guardrails, centralized data sharing, and prompt engineering. Model capability is the dominant factor: an out-of-the-box reasoning model exceeds human-level performance, and optimized reasoning models reduce costs by up to 67% relative to human teams. However, strong average performance masks substantial reliability risks. We introduce the agent bullwhip effect, the amplification of decision unrel"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Model capability is the dominant factor: an out-of-the-box reasoning model exceeds human-level performance, and optimized reasoning models reduce costs by up to 67% relative to human teams. GRPO post-training substantially reduces tail events, curtails agent bullwhip, and improves the reliability of autonomous supply-chain agents.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The MIT Beer Game simulation with its ordering rules and information delays sufficiently captures the coordination dynamics of real multi-echelon supply chains (the entire experimental and theoretical framework is constructed on this testbed).","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Autonomous AI agents outperform humans in supply chain simulations but exhibit an inherent agent bullwhip effect of amplified decision unreliability, mitigated by GRPO reinforcement learning post-training.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Autonomous AI agents with optimized reasoning models outperform human teams in supply chain management by reducing costs up to 67 percent, but require post-training to control decision unreliability.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d42ede4a3fbdc781deb3268be42be4ab6b4f5e795789a128a31eea46c23110b4"},"source":{"id":"2605.17036","kind":"arxiv","version":1},"verdict":{"id":"16948f34-9179-43f3-bd71-b961b42af81c","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T20:09:12.106368Z","strongest_claim":"Model capability is the dominant factor: an out-of-the-box reasoning model exceeds human-level performance, and optimized reasoning models reduce costs by up to 67% relative to human teams. GRPO post-training substantially reduces tail events, curtails agent bullwhip, and improves the reliability of autonomous supply-chain agents.","one_line_summary":"Autonomous AI agents outperform humans in supply chain simulations but exhibit an inherent agent bullwhip effect of amplified decision unreliability, mitigated by GRPO reinforcement learning post-training.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The MIT Beer Game simulation with its ordering rules and information delays sufficiently captures the coordination dynamics of real multi-echelon supply chains (the entire experimental and theoretical framework is constructed on this testbed).","pith_extraction_headline":"Autonomous AI agents with optimized reasoning models outperform human teams in supply chain management by reducing costs up to 67 percent, but require post-training to control decision unreliability."},"integrity":{"clean":false,"summary":{"advisory":1,"critical":0,"by_detector":{"doi_compliance":{"total":1,"advisory":1,"critical":0,"informational":0}},"informational":0},"endpoint":"/pith/2605.17036/integrity.json","findings":[{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. 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