Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·26d ago·5 min read
Models as Governed Interfaces for AI-Native MBSE: Read-Side Adequacy and Write-Side Admissibility
TL;DR
A new data architecture pattern, termed epistemic adequacy, is proposed to enhance AI participation in systems engineering by ensuring that AI-generated contributions are verifiable and governed. This framework aims to improve the reliability of AI interactions with machine-readable models like SysML v2.
✦ Why It Matters
Implement the Governed-Query Architecture Framework to enhance the reliability of AI contributions in your systems engineering projects.
Key Takeaways
- Adopt read-side adequacy to improve AI query responses.
- Implement write-side admissibility to vet AI contributions.
- Utilize the Governed-Query Architecture Framework in projects.
- Test AI models against a retrieval-augmented baseline.
How It Works
The Governed-Query Architecture Framework introduces a structured approach to AI participation in systems engineering. It defines read-side adequacy, which ensures that AI can accurately derive information from models, and write-side admissibility, which regulates the quality of AI-generated data before it is recorded.
This dual approach aims to enhance the reliability of AI insights by addressing gaps in model data architecture.