Building production-ready agentic systems: Lessons from Shopify Sidekick (2025) - Shopify
Aug 26, 2025

Summary
This post details the evolution of Sidekick's architecture with the introduction of Just-in-Time (JIT) instructions to manage tool complexity, moving away from a monolithic system prompt. It also elaborates on building robust LLM evaluation systems by using Ground Truth Sets (GTX) and LLM-as-a-Judge calibrated against human judgment, and employing LLM-powered user simulation for comprehensive testing. Furthermore, it discusses GRPO training with N-Stage Gated Rewards, highlighting the challenges of reward hacking and iterative improvements to address it.
More from the AI Assistant (Sidekick) Capabilities and Training trail
- Gisting: Compressing LLM Agent context to ↑ throughput and ↓ cost (2026) - Shopify
- Sidekick's continual learning loop (2026) - Shopify
- Building an agentic harness that outlasts the model (2026) - Shopify
- Teaching Sidekick to say no: automated data curation with LLM judge consensus (2026) - Shopify
- Building a Magic Mirror: AI retail experiences with Remix (2026) - Shopify