EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent
arXiv:2606.17698v1 Announce Type: cross Abstract: As LLM-based shopping agents enter production, existing benchmarks fail to capture how a shopper's requirements arrive: stated implicitly in the query, recorded in a profile, or revealed only when the right question is asked. Benchmarks that expose full intent upfront and grade only the final choice can neither pose this long-horizon challenge nor explain which requirement an agent missed. To address this gap, we introduce EComAgentBench, a benchmark of 662 tasks grounded in real Amazon products and reviews. Each task scatters these requirement
As LLM-based agents move from research to production, the need for robust, real-world benchmarks that capture complex user intent becomes critical.
Existing AI agent benchmarks fall short in evaluating long-horizon tasks and implicit user requirements, which are crucial for effective commercial deployment.
The introduction of EComAgentBench changes how the performance and limitations of shopping agents are assessed, pushing towards more sophisticated, human-like interaction models.
- · AI agent developers
- · E-commerce platforms
- · Consumers
- · Responsible AI researchers
- · Overly simplistic AI models
- · Benchmarks lacking complexity
- · Companies with weak agent development
Shopping agents will develop more nuanced understandings of user intent, improving conversion rates and user satisfaction.
The competitive landscape for e-commerce platforms will increasingly be defined by the sophistication and autonomy of their AI shopping agents.
This improved benchmarking could accelerate the general development of AI agents capable of handling complex, multi-step tasks across various industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL