Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
Cloud & Infrastructure
The New Stack·Jun 23·5 min read
GitLab just surveyed 1,500 developers. Here’s why it matters for your codebase.
TL;DR
Many developers are experiencing a gap in productivity despite the rapid adoption of AI coding tools. GitLab's survey revealed that while 60% of developers see a strong return on investment (ROI) from AI, only 21% report productivity gains beyond code generation.
✦ Why It Matters
Engineers should consider integrating AI tools with a focus on improving the entire software development lifecycle, not just coding speed.
Key Takeaways
- Assess your AI tool adoption speed against governance policy development.
- Implement machine-scale execution to handle increased agent activity.
- Create a context graph to improve AI tool effectiveness.
- Integrate governance into your software development lifecycle for better control.
How It Works
Agentic infrastructure requires four interconnected layers: execution, context, governance, and orchestration. Execution must support machine-scale operations, while context ensures agents have the necessary information.
Governance ties agent actions to policies, and orchestration coordinates these elements across the software lifecycle.