Engineering

A pragmatic retrospective on the transition from AI autocomplete to autonomous agents: where real engineering velocity was gained, what created hidden debt, and how the role of the senior engineer evolved.

Why the next phase of AI leadership is not about raw model speed, but about bounded autonomy, verifiable guardrails, and turning digital responsibility into a durable competitive advantage.

When AI coding agents modify multiple files across a repository, how do you verify downstream impacts? A deep dive into blast radius, call graphs, and architectural guardrails.

Why rushing to automate surface workflows with AI creates invisible tech debt and team burnout, and how technical leaders can build durable leverage through purpose and architecture.