AI Engineering/Phase 14/Lesson 02

ReWOO and Plan-and-Execute: Decoupled Planning

ReAct interleaves thought and action in one stream. ReWOO separates them: one big plan up front, then execute. 5x fewer tokens, +4% accuracy on HotpotQA, and you can distill the planner into a 7B model. Plan-and-Execute generalized it; Plan-and-Act scaled it to web navigation.

Build 1 hr Python (stdlib)

Prerequisites: Phase 14 · 01 (Agent Loop)

#Learning Objectives

Explain why ReWOO's Planner / Worker / Solver split saves tokens and improves robustness over ReAct's interleaved loop.
Implement a plan DAG, a dependency-ordered executor, and a solver that composes worker outputs — all stdlib.
Decide when a task should run as plan-then-execute vs interleaved ReAct, using the 2026 "five workflow patterns" framing (Anthropic).
Recognize when Plan-and-Act's synthetic plan data is needed for long-horizon web or mobile tasks.

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.