Sidekick's continual learning loop

Frontier models are often the fastest way to launch a new AI product. A small team can get something useful in front of users quickly and learn from real-world usage. But as usage grows, the economics change: frontier models can be too slow and expensive to serve every request at scale. Frontier models are general-purpose, not tailored to your product. More importantly, they do not learn from production on their own. A user correction, rejected output, or recurring failure does not make the next response better. But each failure is hard-won knowledge about your product, and continual learning begins by capturing that knowledge and feeding it back into the system. A deployed frontier model is also frozen. It has no mechanism for internalizing what production teaches it. Instead, improvements accumulate in the discrete artifacts around it: prompt edits, retrieval examples, routing rules, and harness code. Production knowledge piles up in words and code while the model’s weights remain untouched. The flywheel is our answer: a continual learning loop that compresses production experience into the continuous space of the model’s weights. Shopify's GraphQL agent is our clearest example of…