Learning
The system gets better the more you use it. Here's how.
It remembers what works
Most AI tools start from zero every time you open them. Same context limit. Same blind spots. Same mistakes you corrected last week showing up again this week.
This system has a memory layer. After every build, hooks capture what happened: which agents modified which files, what patterns they followed, what decisions were made and why. This information feeds back into future sessions.
How it works in practice
When an agent starts up, it receives relevant expertise from previous sessions. The database agent knows what schema patterns worked well in your project. The designer knows which component patterns you've used before. The connector knows your caching strategy.
This isn't a static knowledge base. It grows. The first feature you build teaches the system about your codebase. By the fifth feature, agents are working with accumulated knowledge about your specific project's patterns, conventions, and decisions.
The expertise layer
Every agent has access to project-specific expertise indexed by domain. Frontend patterns, backend conventions, database decisions, design choices. When the orchestrator spawns an agent, it injects the relevant slice of expertise for that agent's domain.
The system also captures when things go wrong. If a build error required a specific fix, that fix gets recorded. If a type pattern caused problems, the system notes it. Over time, agents avoid the mistakes they've already seen.
What this means for you
Early builds might need more of your review. By the time you're ten features deep, the agents are producing code that matches your project's conventions with less back-and-forth. They've learned your naming patterns, your component structure, your data flow preferences.
The framework doesn't just build your product. It builds its understanding of your product while it goes.