An AI-powered dev workflow combined Claude, Playwright, and a Postgres-backed REST API to ship 2–3 features per day. But as complexity grew, multi-agent loops broke down, tests ballooned, and schema drift demanded increasingly precise prompts and manual corrections. The result: more time spent managing context and debugging automation than writing code — exposing the technical debt baked into LLM-driven development.
Implication: The role of the engineer is morphing — from creator to curator of machine-generated complexity. As LLMs accelerate output, they offload syntax but amplify cognitive overhead: debugging opaque logic, aligning fragmented context, and safeguarding brittle systems. Without strong architecture and deep domain fluency, teams risk trading velocity for shallow control and compounding fragility.
