What a scaffold is
The loop around the model: decompose the goal, pick tools, order steps, verify outputs, retry on failure. Traditionally humans hand-wire this (LangChain graphs, state machines). "Self-scaffolding" models learn to author their own step structure at inference time.
ReAct vs Plan-and-Execute
ReAct interleaves think → act → observe one step at a time — cheap but myopic; early mistakes compound. Plan-and-Execute drafts the whole route, critiques it, then executes with checkpoints. Research consistently shows planning helps most on long-horizon, multi-tool tasks.
Why small models win
Error rates multiply: a 95%-per-step agent has just 60% success over 10 steps (0.95¹⁰). Planning removes doomed branches before execution, so a smaller model with verification beats a larger one improvising — capability is loop-shaped, not just parameter-shaped.
Try it in your prompts
Before letting a model act, ask it to: 1) list steps with expected outputs, 2) mark risky steps and a fallback, 3) execute one step at a time, comparing actual vs expected, 4) replan on mismatch. That's a scaffold — no framework required.