The core idea is that AI-generated code should follow the same rules as human code: versioned, validated, observable. Aegis enforces this through blueprint-based development, drift detection, and runtime compliance systems. It’s designed for teams using tools like Copilot, Kilo, or Lovable to build production systems with confidence.
This isn’t a library — it’s a way to architect AI-native engineering workflows.
Would love feedback, questions, and critiques. Especially curious if others are facing similar issues with AI output governance or system reliability in their workflows.
Happy to dive into internals or philosophy if there's interest.