# Multimodal Collaborative Agents for Next-Gen Commerce — Nidhi Kaushik Vyas, Google DeepMind

Source: [AI Engineer](https://www.youtube.com/watch?v=AhQpRalYlyg)  
Feed7 permalink: https://feed7.dev/p/multimodal-collaborative-agents-for-next-gen-commerce-nidhi-kaushik-vyas-0mh4h1b  
Published: 2026-09-01T17:00:27.000Z  
Trust: Source Linked (source_linked)

## Why Included

For fuzzy requests, an agent should identify the missing constraint with the most decision value, elicit it in the right modality, then choose a response format suited to the task.

## Source Summary

The proposed loop builds a working state from conversation history, personal context, references, hard constraints, soft preferences, confidence, and live variables such as inventory. It then asks the **single highest-utility question**, such as room width before suggesting furniture.

## Practical Implication

Treat elicitation as an agent policy: identify blockers, avoid repeated questioning, and select **text or visual preference boards** according to whether the user can articulate the constraint. Map each answer into the catalog ontology before retrieval, then choose a response format such as a comparison table or inspiration images.

## Agent-Ready Context

The proposed loop builds a working state from conversation history, personal context, references, hard constraints, soft preferences, confidence, and live variables such as inventory. It then asks the **single highest-utility question**, such as room width before suggesting furniture.

Treat elicitation as an agent policy: identify blockers, avoid repeated questioning, and select **text or visual preference boards** according to whether the user can articulate the constraint. Map each answer into the catalog ontology before retrieval, then choose a response format such as a comparison table or inspiration images.

This is a commerce-grounded framework, not reported performance evidence. Merchant ontologies remain important, and agent-to-agent shopping is described as **an early-stage possibility**, with direct user involvement still preferred during discovery.

## Connected Context

Feed7 judgment across 669 accumulated Signals:

This specializes context engineering into an explicit commerce elicitation policy: maintain structured state, ask the one question that removes the most uncertainty, translate answers into merchant ontology, and adapt the response medium. It reinforces process-first scoping while narrowing autonomous shopping claims, since discovery still favors direct user participation and no performance evidence is reported.

- [How Forward Deployed Engineering is done at Ramp — Leo Mehr](https://feed7.dev/p/how-forward-deployed-engineering-is-done-at-ramp-leo-mehr-1w1rztz) — Ramp’s intake work reinforces the value of interrogating ambiguity before execution; this signal makes that principle concrete as highest-utility questioning for commerce.
- [Healthcare’s Agent Bytecode: X12 as the Harness for AI Agents — Vasant Kearney, Onlay](https://feed7.dev/p/healthcare-s-agent-bytecode-x12-as-the-harness-for-ai-agents-vasant-kear-0zwdwwy) — Both use a domain schema to convert messy interaction into checkable structure, though merchant ontology guides retrieval while X12 constrains healthcare transactions.
- [Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution](https://feed7.dev/p/2607-13034v1-03g7ghx) — E3’s minimum-viable-path policy aligns with asking only the question needed to unblock progress, avoiding unnecessary expansion until evidence requires it.
- [AI tools for Forward Deployed Engineering — Vasuman Moza, Varick Agents](https://feed7.dev/p/ai-tools-for-forward-deployed-engineering-vasuman-moza-varick-agents-12kjg79) — The working-state design operationalizes Varick’s process-first guidance by capturing constraints, preferences, confidence, and live variables before automating a recommendation.

## Context Map

- Layer: agent
- Domains: None
- Topics: harness-engineering, tool-use, context-engineering

## Uncertainty

- This is a commerce-grounded framework, not reported performance evidence. Merchant ontologies remain important, and agent-to-agent shopping is described as **an early-stage possibility**, with direct user involvement still preferred during discovery.

## Agent Instruction

Use this item as source-backed context. Do not invent claims beyond the linked source. If this item conflicts with another source, call out the conflict.