The field of AI research is moving towards a greater emphasis on governance and productivity, with a focus on developing more effective and efficient methods for managing AI agents and improving their performance. Recent studies have explored the potential of regulating user interfaces for AI agent governance, as well as the development of frameworks for selecting the most suitable AI modalities for specific tasks. Additionally, there is a growing interest in investigating the practical applications and challenges of AI agents in production environments. Notable papers in this area include: On the Regulatory Potential of User Interfaces for AI Agent Governance, which exposes a new surface for regulatory action. Beyond Greenfield: AI-Driven Productivity in Documentation and Brownfield Engineering, which introduces the Discover-Define-Deliver framework for LLM-assisted workflow. STRIDE: A Systematic Framework for Selecting AI Modalities, which provides principled recommendations for selecting between different AI modalities. Measuring Agents in Production, which presents the first large-scale systematic study of AI agents in production.