Automate replenishment with MMF, Databricks Genie, and Amazon Quick
Not a pilot. Databricks and Amazon built a closed detect-decide-act loop that places replenishment orders unattended, escalating to humans only when suppliers can't cover demand.
Why it matters
This is agent infrastructure in production: a multi-step autonomous workflow (forecast → check supplier inventory → place orders → escalate) deployed on Databricks Genie and Amazon Quick. It shows how practitioners are moving past single-tool forecasting into multi-agent decision loops.
The key facts
10 to knowClosed detect-decide-act loop for supply-chain automation
Integrates: demand forecasting (MMF) → inventory checking (Amazon Quick) → autonomous ordering
Escalates to human only when no supplier can cover surge
Built on Databricks Genie and Amazon Quick (agent infrastructure)
Supply-chain replenishment as a multi-step autonomous workflow
Closed detect-decide-act loop: forecast → supplier availability check → automated order placement
Built on Databricks Genie (agent orchestration) + Amazon Quick (supplier integration)
Handles catalog-wide demand surges autonomously
Escalation logic: human-only when no supplier can cover demand
Published as AWS blog post / reference architecture (Sep 2026)
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: Foundation models made catalog-wide demand forecasting easy; the hard part is now acting on the forecast. This post builds a closed detect-decide-act loop on Databricks and Amazon Quick that reconciles demand surges against live supplier availability and places replenishment orders unattended,…