“The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin.”Richard S. Sutton · The Bitter Lesson · 2019 · The Bitter Lesson (eseu, 13 martie 2019) — prima frază
An agent's scaffold must be thin enough that a better model improves it rather than breaks it.
Sutton's argument: hand-coded human knowledge wins in the short run, then loses to search and learning that scale with compute. For agent builders, thick scaffolding — rigid step lists, fixed plans, parsers for the model's exact wording — is the human-knowledge bet, and it breaks at the next model release. Better: give the model tools, the goal and the constraints, verify the outputs, and keep the scaffold thin. Where determinism matters (compliance, money), keep it outside the model as a workflow, not inside it as a prompt. The "bitter" part: the cleverest prompt engineering has a shelf life of one model generation.
Why it matters Models change every 3–6 months; an agent with thick scaffolding gets rewritten every time.