Declarative beats imperative when working with agents

Karpathy went from 80% manual coding to 80% agent coding in one month (November to December 2025). After years of programming, his fundamental workflow changed in weeks. His key lesson: don’t tell the agent what to do — give it success criteria. Write tests first, then have it pass them. Write the naive version first, then optimize.

Agents have tenacity humans lack — they never get tired or demoralized. Watching an agent struggle with something for 30 minutes and eventually succeed, where a human would have given up, reveals that stamina was a bottleneck to work that just got removed. This connects directly to why In agent-native architecture, features are prompts — not code — you describe outcomes, not procedures.

The honest limitations are real though: wrong assumptions, no confusion management, sycophancy, over-engineering, bloated abstractions. The solution is to watch them like a hawk and review everything. This is why Technical knowledge can become a liability when working with AI is nuanced — you still need expertise to evaluate output, even if you stop writing code manually.