- Updated: July 18, 2026
- 2 min read
RetailBench: Evaluating Long‑Horizon Autonomous Decision‑Making in Realistic Retail Environments
RetailBench: A Benchmark for Long‑Horizon Autonomous Decision‑Making
Large language model (LLM) agents have shown impressive performance on short‑horizon, well‑scoped tasks. However, their ability to sustain coherent, strategic decisions over extended periods in dynamic environments remains largely unexplored. RetailBench addresses this gap by providing a data‑grounded, supermarket‑simulation benchmark that evaluates tool‑using LLM agents across a realistic, multi‑day retail operation.
Why RetailBench Matters
- Real‑World Relevance: Simulates a single‑store supermarket with pricing, replenishment, supplier selection, shelf assortment, inventory aging, customer feedback, external events, and cash‑flow constraints.
- Long‑Horizon Evaluation: Supports thousand‑day‑scale simulations, enabling assessment of strategy stability over months.
- Comprehensive Metrics: Net worth, sales outcomes, and behavioral analyses reveal strengths and weaknesses of LLM agents.
Key Findings
We evaluated seven contemporary LLMs using representative agent frameworks over a 180‑day horizon and compared them with a privileged oracle policy. Results show:
- Only a small subset of models survive the full evaluation horizon.
- The strongest LLMs still lag significantly behind the oracle in final net worth and sales.
- Performance gaps stem from incomplete evidence acquisition, surface‑level decision making, and lack of consistent long‑term policy.
Implications for Autonomous AI
RetailBench provides a controlled testbed for studying reliable autonomy in economically grounded, long‑horizon decision‑making. Researchers can use it to develop and benchmark more robust, strategic LLM agents capable of operating in real‑world business contexts.
Read the full paper on arXiv and explore related resources on ubos.tech.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.