[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82846-en":3,"doc-seo-82846-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82846,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Strategic Buying Agents","Strategic Buying Agents examines how agentic AI can decide when to purchase a single item within a finite shopping window under uncertainty about future price changes. The study formulates a purchase-policy design problem across three information regimes—stationary, Bayesian, and robust—and derives optimal policies that translate observed prices, remaining time, and future-price information into actionable buy-or-wait rules. The approach evaluates performance using Amazon price histories and discusses integration with language-model buying agents.","arXiv :2607 .04708v1 [ econ .TH] 6 Jul 2026  \nStrategic Buying Agents  \nMingyang Fu  \nRotman School of Management, University of Toronto, [mingyang.fu@utoronto.ca](mingyang.fu@utoronto.ca)  \nMing Hu  \nRotman School of Management, University of Toronto, [ming.hu@rotman.utoronto.ca](ming.hu@rotman.utoronto.ca)  \nThe emergence of agentic AI is shifting online shopping from search and recommendation toward delegated purchasing by autonomous buying agents that monitor markets, reason about uncertainty, and make purchase decisions on behalf of consumers. We study the design of strategic buying agents that decide when to purchase an item within a finite shopping window. The central challenge is to translate price observations, the shopping window, and information about future price changes into a purchase policy. We formulate this problem across three information regimes: stationary, Bayesian, and robust, and use the resulting optimal policies as a policy menu for implementation. In the stationary regime, price adjustments follow an exogenously specified Poisson arrival process, and post-adjustment prices are drawn from a known stationary distribution. We show that the optimal policy is a dynamic purchase-threshold policy, with the threshold characterized by an ordinary differential equation. In the Bayesian regime, the adjustment intensity is assumed known, but the price-adjustment distribution is uncertain. We show that the optimal rule remains threshold-based, with the threshold depending on posterior beliefs. We also bound the value of knowing the true price-adjustment distribution. In the robust regime, the agent relies only on price bounds and seeks worst-case protection. We show that randomized threshold policies yield optimal guarantees for both competitive ratio and minimax regret. Finally, we evaluate our proposed policies using Amazon product price histories tracked by Keepa, comprising 367 items and 48,933 time-stamped price observations. We also examine how these policies can be incorporated into language-model buying agents. On our test instances, the stationary and Bayesian policies perform competitively in terms of mean normalized consumer surplus despite their stylized assumptions, while the robust policy performs best at the 10th percentile of the normalized surplus distribution. The results also suggest that language models are better suited to choosing among the three information regimesand selecting price samples for calibration than to making buy-or-wait decisions directly.  \n1. Introduction  \nOnline shopping platforms have historically treated human consumers as the primary decision makers. Consumers search, compare products, evaluate prices, and complete checkout, while platforms provide search tools, recommendations, and payment systems. Recent developments in agentic commerce suggest a different mode of interaction, in which consumers may delegate parts of the shopping process to AI agents that monitor products, compare prices, and execute transactions subject to user-specified conditions (Bloomberg Odd Lots 2026 , OpenAI 2025a, Stripe 2025a,b, 2026b,a) . More  \nbroadly, deployed web-agent systems show that agents can already navigate interfaces and execute user-directed actions (OpenAI 2025c,b) .  \nTogether, these AI technologies make delegated shopping feasible. A broad literature on online commerce has generated rich insights into search, recommendation, and consumer choice. Recent work on LLM-enabled operational decision support further shows how language models can structure decision inputs, invoke analytical tools, and communicate recommendations (Simchi-Levi et al. 2025 , Baek et al. 2026) . These advances provide important functional ingredients for shopping agents: an agent may be able to browse product pages, monitor price adjustments, interpret a consumer’s request, update its assessment of the shopping task, and complete checkout through the payment infrastructure. Yet they do not by themselves d","cbCaifTOtU2MW4sX","https://ap.wps.com/l/cbCaifTOtU2MW4sX","pdf",993852,1,67,"English","en",105,"# Introduction\n# Strategic Buying-Agent Decision Problem\n# Information Regimes and Policy Design\n## Stationary Regime\n## Bayesian Regime\n## Robust Regime\n# Evaluation and Empirical Results\n# Integration with Language-Model Buying Agents","[{\"question\":\"What decision does a strategic buying agent need to make?\",\"answer\":\"The agent must determine when to buy a single item within a finite shopping window by choosing between buying now and waiting for potentially better prices.\"},{\"question\":\"How does the paper handle uncertainty about future price changes?\",\"answer\":\"It studies three regimes—stationary, Bayesian, and robust—each providing a different level of information about how prices may evolve, then derives optimal or guarantee-optimal purchase policies accordingly.\"},{\"question\":\"What do the results suggest about using language-model buying agents?\",\"answer\":\"Language-model approaches are suggested to be better suited for selecting among information regimes and choosing calibration price samples rather than directly making the buy-or-wait decision.\"}]",1784183392,169,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"strategic-buying-agents","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/strategic-buying-agents/82846/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What decision does a strategic buying agent need to make?","Question",{"text":74,"@type":75},"The agent must determine when to buy a single item within a finite shopping window by choosing between buying now and waiting for potentially better prices.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the paper handle uncertainty about future price changes?",{"text":79,"@type":75},"It studies three regimes—stationary, Bayesian, and robust—each providing a different level of information about how prices may evolve, then derives optimal or guarantee-optimal purchase policies accordingly.",{"name":81,"@type":72,"acceptedAnswer":82},"What do the results suggest about using language-model buying agents?",{"text":83,"@type":75},"Language-model approaches are suggested to be better suited for selecting among information regimes and choosing calibration price samples rather than directly making the buy-or-wait 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