AI Weekly Malaysia

Anthropic engineers Ali Shazal and Matthew Koen distill patterns from a year of building production commerce agents with Claude across retail, travel, telecom, and ticketing. The guide covers a single-model agent loop architecture with skills (not subagents), latency/cost techniques including prompt caching, and production concerns like session-surviving memory, safety enforcement in the harness, and evals for non-deterministic systems. A reference implementation repo (anthropics/commerce-agents) provides harnesses, guardrails, and example shopping/merchant agents.

Why: If you're shipping a consumer-facing agent, the specific architectural choices here—skills over subagents for the long tail, safety enforcement living in the harness rather than the model, and prompt caching for latency—are concrete decisions you can adopt from a reference repo rather than rediscover. The eval section is especially relevant: shipping a non-deterministic system without a strong eval suite is the most common failure mode for teams new to agents, and this gives a production-tested framework.