Why the AI stack for modern engineering teams requires both coding and context

This post introduces a two-layer model for AI stacks in engineering, emphasizing the separation of coding assistants (IDE-based) from enterprise context platforms. It argues that while coding assistants are crucial for raw output, the real productivity bottleneck is chasing context across various tools. The proposed solution involves a foundational context layer that indexes and provides information across teams and repos, including incidents, tickets, and design docs, while also enforcing security and privacy policies. This layer enables AI tools to provide context-rich, compliant results. The post highlights three key trends: AI moving from experiments to infrastructure, the need for blended tools rather than single vendors, and the increasing importance of security, governance, and explainability.