Data Tech 2026

Can multiple AI agents, built across different frameworks, owned by different teams, and trained on different data sources, come together to solve an enterprise-scale problem? That's the central question SimRetail, developed at Target Corporation and accepted at AAMAS 2026 (the International Conference on Autonomous Agents and Multiagent Systems), is built to explore.

Retail assortment planning is a complex, high-stakes decision that has traditionally required human merchandisers to manually synthesize signals across market trends, sales performance, vendor constraints, and consumer behavior. SimRetail reimagines this as a multi-agent collaboration challenge, where specialist agents spanning trend research, merchandising analytics, and vendor intelligence must coordinate reasoning across multiple decision cycles to produce a coherent, consumer-validated assortment. A persona-based scoring agent, powered by NVIDIA's Nemotron-Personas-USA dataset of over 181,000 synthetic consumer profiles, then evaluates the output against eight buyer archetypes, stress-testing whether the agents' collective judgment actually translates to real consumer resonance.

Attendees will walk away with practical insights into multi-agent system design, agent-to-agent communication protocols such as A2A, MCP, challenges and evaluation of various agentic AI frameworks, observability solutions, synthetic persona evaluation, and building explainable agentic pipelines.