Modern autonomous agents improve themselves through structured updates to models and their infrastructure, requiring systematic approaches for production deployment.
AI agents can be trained as data scientists to automatically generate high-quality synthetic training data, which continuously improves through meta-optimization.
Deterministic security models are no longer sufficient when AI systems make unforeseen decisions at runtime and interact with APIs and environments in unanticipated ways.
