# Adapting to Evolving Requirements: Agentic AI for Retail Supply Chain Operations

arXiv: https://arxiv.org/abs/2609.03860 · HTML: https://mlsanity.com/paper/2609.03860/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 3 Sep 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 12 of 12)

- [ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling](https://mlsanity.com/paper/2405.17743/) (2024)
- [OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models](https://mlsanity.com/paper/2402.10172/) (2024)
- [LLMs for Mathematical Modeling: Towards Bridging the Gap between Natural and Mathematical Languages](https://mlsanity.com/paper/2405.13144/) (2024)
- [Autoformulation of Mathematical Optimization Models Using LLMs](https://mlsanity.com/paper/2411.01679/) (2024)
- [MM-Agent: LLM as Agents for Real-world Mathematical Modeling Problem](https://mlsanity.com/paper/2505.14148/) (2025)
- [Evaluating LLM Reasoning in the Operations Research Domain with ORQA](https://mlsanity.com/paper/2412.17874/) (2024)
- [Structure-Informed Deep Reinforcement Learning for Inventory Management](https://mlsanity.com/paper/2507.22040/) (2025)
- [Gala: Global LLM Agents for Text-to-Model Translation](https://mlsanity.com/paper/2509.08970/) (2025)
- [Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches](https://mlsanity.com/paper/2605.18692/) (2026)
- [DAOpt: Modeling and Evaluation of Data-Driven Optimization under Uncertainty with LLMs](https://mlsanity.com/paper/2511.11576/) (2025)
- [A Real-Time Scalable Heuristic DSS Framework for Capacity-Constrained Retail Allocation under Supply Chain Uncertainty](https://mlsanity.com/paper/2605.02330/) (2026)
- [Context-Aware Synthesis of Optimization Pipelines for Warehouse Optimization](https://mlsanity.com/paper/2606.26852/) (2026)

## Abstract (arXiv)

Retail supply chain operations rely on coupled decision modules that must adapt as requirements evolve. LLMs offer a natural-language interface for this task, but existing methods primarily focus on individual optimization models. Extending them to heterogeneous decision pipelines is challenging because a requirement may admit multiple intervention paths with different downstream effects. We formulate requirement-driven adaptation as the joint selection of an intervention route and an admissible module-level change, and propose a graph-constrained agentic framework in which domain agents expose admissible reformulation interfaces and a central processor searches over bounded intervention paths. Candidates are validated and compared using downstream KPIs. In collaboration with a large retail partner, we evaluate 100 warehouse requirements elicited from practitioner interviews, with GPT, Qwen, and DeepSeek as base LLMs. Relative to direct LLM reformulation, our framework improves correctness and end-to-end success across all three models, raising end-to-end success from 72--76% to 79--83%.