Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants
#conversational shopping assistants #multi-agent systems #AI evaluation #prompt optimization #grocery AI #LLM-as-judge #arXiv 2603.03565 #GEPA optimizer
📌 Key Takeaways
- Researchers developed a blueprint for evaluating and optimizing conversational shopping assistants
- Multi-faceted evaluation rubric decomposes shopping quality into structured dimensions
- Two prompt-optimization strategies were investigated: Sub-agent GEPA and Multi-Agent Multi-Turn Herrera
- Rubric templates and evaluation design guidance have been released for practitioners
📖 Full Retelling
A team of researchers led by Alejandro Breen Herrera, along with Aayush Sheth, Steven G. Xu, Zhucheng Zhan, Charles Wright, Marcus Yearwood, Hongtai Wei, and Sudeep Das, presented a comprehensive blueprint for evaluating and optimizing conversational shopping assistants in a paper submitted to arXiv on March 3, 2026. The research addresses critical challenges in developing production-ready AI shopping assistants, particularly focusing on evaluating complex multi-turn conversations and optimizing systems with multiple interconnected agents. Grocery shopping scenarios present unique difficulties as user requests are often vague, highly dependent on personal preferences, and subject to practical constraints like budget limitations and product availability. The researchers developed a multi-faceted evaluation rubric that breaks down shopping quality into structured dimensions and created a calibrated LLM-as-judge system aligned with human annotations. Building on this foundation, they investigated two complementary prompt-optimization strategies: Sub-agent GEPA, which optimizes individual agent components against specific criteria, and Multi-Agent Multi-Turn Herrera, a novel system-level approach that jointly optimizes prompts across multiple agents using multi-turn simulation and trajectory-level scoring. The team has released rubric templates and evaluation design guidance to support practitioners developing production conversational shopping assistants.
🏷️ Themes
Artificial Intelligence, Conversational Shopping Assistants, Multi-Agent Systems, Evaluation Methods
📚 Related People & Topics
Continual improvement process
Ongoing effort to improve
A continual improvement process, also often called a continuous improvement process (abbreviated as CIP or CI), is an ongoing effort to improve products, services, or processes. These efforts can seek "incremental" improvement over time or "breakthrough" improvement all at once. Delivery (customer v...
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--> Computer Science > Artificial Intelligence arXiv:2603.03565 [Submitted on 3 Mar 2026] Title: Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants Authors: Alejandro Breen Herrera , Aayush Sheth , Steven G. Xu , Zhucheng Zhan , Charles Wright , Marcus Yearwood , Hongtai Wei , Sudeep Das View a PDF of the paper titled Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants, by Alejandro Breen Herrera and 7 other authors View PDF HTML Abstract: Conversational shopping assistants represent a compelling application of agentic AI, but moving from prototype to production reveals two underexplored challenges: how to evaluate multi-turn interactions and how to optimize tightly coupled multi-agent systems. Grocery shopping further amplifies these difficulties, as user requests are often underspecified, highly preference-sensitive, and constrained by factors such as budget and inventory. In this paper, we present a practical blueprint for evaluating and optimizing conversational shopping assistants, illustrated through a production-scale AI grocery assistant. We introduce a multi-faceted evaluation rubric that decomposes end-to-end shopping quality into structured dimensions and develop a calibrated LLM-as-judge pipeline aligned with human annotations. Building on this evaluation foundation, we investigate two complementary prompt-optimization strategies based on a SOTA prompt-optimizer called GEPA (Shao et al., 2025): (1) Sub-agent GEPA, which optimizes individual agent nodes against localized rubrics, and (2 Multi-Agent Multi-Turn Herrera et al., 2026), a novel system-level approach that jointly optimizes prompts across agents using multi-turn simulation and trajectory-level scoring. We release rubric templates and evaluation design guidance to support practitioners building production CSAs. Subjects: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Machine Learning ...
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