AI Weekly Malaysia

Meta's Muse agent now handles agentic shopping: it browses retail sites, surfaces matching listings inside the Muse app or website, and can prep a checkout that the user approves via an 'approval card'. Meta announced retail partnerships with Walmart, Gap, Sephora, Expedia, Wayfair and Best Buy, and a Meta spokesperson said shopping has quickly become one of Muse's biggest usage drivers. The piece also notes shopper concern about price-gouging as retailers lean on AI-driven dynamic pricing.

Why: Agentic checkout is moving from demo to shipped flow with named retail partners, so the pattern to watch is the human approval step before payment — the 'approval card' — rather than the browsing. If you build commerce or agent tooling, that approval-and-handoff design, and the dynamic-pricing objection attached to it, are the parts worth copying or defending against; the partners listed are US retail, so there is no stated Malaysia or SEA availability to build on yet.

On an interview with investor Patrick O'Shaughnessy, Instinct founder Noah Shinn said more than 50% of transactions on the invite-only agent platform are travel-related, and that the platform is approaching a billion dollars in annual transactions while growing "10% day-by-day" with transaction volume rising at a similar rate. Shinn did not specify how the transaction rate is calculated. He also described agents checking restaurant sites every five seconds for open slots and said he wants to "reinvent reservations" to give users preferential treatment, comments that drew criticism.

Why: Treat the numbers as self-reported with no stated methodology: a 10% day-over-day growth rate and ~$1B annual transaction figure would be extraordinary at that compounding rate, so don't use them for capacity planning, market sizing, or a pitch deck without your own verification. The one reusable technical detail is the pattern of an agent polling a third-party restaurant site every five seconds for availability — if you build anything similar, expect rate limits, bot blocking, and server-cost questions from the sites you scrape, and design caching or webhook/API fallbacks rather than tight polling loops.