Agentic Commerce

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Agentic commerce studies how AI agents can act for buyers, sellers, or businesses across product discovery, recommendation, negotiation, and transactions. Systems combine language models with catalog retrieval, preference tracking, planning, deterministic constraint checks, and payment or authorization protocols. Benchmarks test long-horizon shopping, hidden requirements, budget-constrained baskets, auctions, and business operations, while simulations and experiments examine privacy, persuasion, fraud, and how gains are shared. Agents can complete useful tasks, but reliable performance remains difficult when intent is ambiguous or constraints change, and safe deployment requires evidence-based decisions, secure payments, and protections against conflicting commercial incentives.

Latest papers

46 papers13 papers in the last four weeks, up 63% on the four weeks before. 0.1% of all new papers.

Can LLM Agents Price Competitively? A Dynamic Multi-Attribute Auction Benchmark for Agentic Commerce
Jul 30, 2026Shimaa Ahmed, Yiwei Cai, Mohsen Minaei +1LLM Agent Evaluation AI Agent Benchmarks
Visa Research
Paying for Honesty Without Knowing the Truth: Reputation-Penalty Design for LLM Marketplace Agents
Jul 30, 2026Mingdai Yang, Shicheng Fan, Kejing Yu +5Agent Reliability Deception in Language Models
Univ. of Illinois Chicago Chicago, IL, USA · Springbrand Inc. Hangzhou, China · Beijing University of Posts and Telecommunications Beijing, China +2
Building Trust in Autonomous Commerce: A Verifiable Global Event Timeline and AI-Ready Fraud Intelligence Layer
Jul 21, 2026Rajat SrivastavaData Provenance AI Agent Security
Independent Researcher, Gurugram, India
A Decision-Centered Reference Architecture for Trustworthy Agentic Commerce
Jul 20, 2026Dimitrios S. SfirisAI Agent Security Agentic Commerce
AspectSoft, Xanthi, Greece
Strategic Buying Agents
Jul 6, 2026Mingyang Fu, Ming HuSequential Decision Making Optimal Stopping
Rotman School of Management, University of Toronto
ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping
Jun 30, 2026Jiacheng Chen, Tao Zhang, Manxi Lin +26Large Language Model-Based Recommendation E-Commerce
Alibaba Taobao & Tmall Group
Paying to Know: Micro-Transaction Markets for Verified Product Information in Agentic E-Commerce
Jun 23, 2026Filippos Ventirozos, Matthew ShardlowActive Information Gathering E-Commerce
Manchester Metropolitan University Manchester, United Kingdom
EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent
Jun 16, 2026Zeyao Du, Tong Li, Haibo ZhangWeb Agents Long-Horizon Agent Evaluation
Shopee
Bittensor Agent Arenas as a Trajectory Primitive: Distilling a Shopping Agent from ShoppingBench Subnet Traces
Jun 8, 2026Shardul Bansal, Seth Schilbe, Jarrod BarnesAI Agent Benchmarks LLM Agent Training
1ORO AI · 2Dynamical Systems
RAILS: Verification-Native Clearing For Agentic Commerce
Jun 7, 2026Adrian de Valois-Franklin, Alex BogdanAgent Reliability Agentic Commerce
Evolutionairy AI · Toronto, Canada
When Agents Shop for You: Role Coherence in AI-Mediated Markets
Apr 29, 2026Soogand Alavi, Salar NozariPrivacy Leakage in Language Models Agentic Commerce
Tippie College of Business, University of Iowa
SoK: Security of Autonomous LLM Agents in Agentic Commerce
Apr 15, 2026Qian'ang Mao, Jiaxin Wang, Ya Liu +3LLM Agent Security Agentic Commerce
Nanjing University · Southern University of Science and Technology · City University of Hong Kong
Commercial Persuasion in AI-Mediated Conversations
Apr 5, 2026Francesco Salvi, Alejandro Cuevas, Manoel Horta RibeiroPersuasion in Language Models Agentic Commerce
Princeton University
APEX: Agent Payment Execution with Policy for Autonomous Agent API Access
Apr 2, 2026Mohd Safwan Uddin, Mohammed Mouzam, Mohammed Imran +1Agentic Commerce Tool Access Control for LLM Agents
Capability-Priced Micro-Markets: A Micro-Economic Framework for the Agentic Web over HTTP 402
Mar 4, 2026Ken Huang, Jerry Huang, Mahesh Lambe +8Agentic Commerce Multi-Agent Systems
CSA and DistributedApps.ai · Kleiner Perkins · Unify Dynamics +4
Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants
Mar 3, 2026Alejandro Breen Herrera, Aayush Sheth, Steven G. Xu +8Multi-Agent LLM Systems LLM Agent Workflow Optimization
Metis · DoorDash

Jul 30, 2026J/K move · Enter open · S save

Can LLM Agents Price Competitively? A Dynamic Multi-Attribute Auction Benchmark for Agentic Commerce

Shimaa Ahmed, Yiwei Cai, Mohsen Minaei, Rahul Rachuri

Visa Research

LLM Agent Evaluation AI Agent Benchmarks LLM Agents Agentic Commerce Agentic AI

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Agentic commerce is moving from concept to deployed infrastructure: payment networks, retailers, and AI platforms are setting the stage for agents to transact on behalf of merchants and consumers. Yet whether the LLMs behind these agents can price competently in real markets, where customer preferences are hidden, competitors adapt in real time, and demand can shift without warning, has not been systematically tested. We introduce Bazaar, a dynamic sealed-bid benchmark for multi-attribute auction under these conditions. Despite its dynamics, the benchmark is grounded in closed-form customer utilities, enabling exact evaluation. Across 11 frontier LLMs from four providers, the leading agents on customer acquisition (e.g. Gemini 3.1 Pro) are often not the leading agents on profit (e.g. Opus 4.6). The ranking shifts again under demand shocks: agents that learned fastest pre-shock are typically the slowest to revise their beliefs afterwards, while Gemini 3.1 Pro recovers fastest despite not leading on profit. However, even the strongest agent captures less than a third of hindsight-optimal profit, suggesting current LLMs are progressing in agentic commerce but leave substantial headroom.