Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 10·5 min read
Towards Assurance Closure in AI-Native Large-Scale Agile Software Development
TL;DR
A framework for assurance closure in AI-native large-scale agile software development is proposed, enabling better governance of intent, risk, and exceptions. This architecture aims to enhance the credibility and validity of evidence used in the software engineering process.
✦ Why It Matters
Evaluate your current software development processes for gaps in assurance reasoning and consider adopting the proposed framework.
Key Takeaways
- Assess your software development for assurance reasoning gaps.
- Implement a shared semantic assurance layer in your projects.
- Explore the proposed architecture for AI-native development.
- Engage in research to address the identified gaps.
How It Works
The proposed architecture integrates various assurance mechanisms, such as formal methods and digital twins, into a cohesive framework. By establishing a semantic assurance layer, it allows for better communication and understanding of evidence across different AI agents and human stakeholders.
This layer ensures that the evidence used in decision-making is credible and valid, even as the system evolves.