Most teams treat a cache as a black box: URL-keyed blobs with a TTL, useful for speed and nothing else. In Harper, cached data lands in a real table inside the same query engine. That means filtering, joining, real-time subscriptions, and vector search all work against it.
Deserializing entire records to read one field is a bottleneck at scale. Structon stores objects in a binary format where any field is reachable by byte offset, with lazy getters that never allocate until you access a property. It's the encoding Harper has used internally for years, now a standalone package.
Production agents spend 56–74% of their time waiting on tool calls and infrastructure, not the model. Microservices were the right answer for the web era; agentic workloads need the stack pulled back into a single unified runtime.
Watch Austin rebuild his personal website live using Claude AI and Harper, including a custom Markdown CMS, GraphQL schema design, React scaffolding, and full deployment. A real-time pair coding session from design to launch.
AI has made it dramatically cheaper to get software to a working version, but most companies still plan like building is the expensive part. The new bottleneck is the product loop: forming sharp hypotheses, living inside the user experience, fixing friction as it appears, and feeding evidence back into the roadmap faster than ticket-based planning allows.
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