Compare planning strategies: ReAct, Plan-and-Execute, and Reflection.

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ReAct decides one step at a time. It is flexible but greedy and can wander. Plan-and-Execute writes a full plan first, executes the steps (often with a cheaper model), and re-plans when needed. It is more coherent on long tasks and cheaper. Reflection (e.g. Reflexion) adds a self-critique step after an attempt and feeds the lessons into the next try. It improves quality when there's a clear feedback signal.

A planner LLM writes steps 1 to n, an executor runs step i, and a check asks whether the step was ok and the plan still valid: yes moves to the next step, no goes to re-plan and back to the executor, and when all steps are done the agent gives the final answer.

	ReAct	Plan-and-Execute	Reflection
Planning horizon	One step	Whole task	Across attempts
Strength	Adapts to surprises	Coherent long tasks, cheaper executors	Learns from failures
Weakness	Drifts, repeats, no big picture	Plans go stale; needs re-planning	Extra calls; critic can be wrong
Good for	Short interactive tasks	Multi-step research, ETL-like jobs	Code (tests as signal), writing with rubric

Practical hybrid: most production agents keep a lightweight, editable plan, often a to-do list in context or a file, and execute it ReAct-style, updating the list as they learn. Coding agents work this way, and so does LangChain's "Deep Agents" pattern.

Reflection caveat: self-critique without external feedback is limited. A model reviewing its own answer often approves it. Reflection works best when it is grounded in a signal: test failures, a validator, retrieved evidence, or a separate judge with a rubric.

Follow-ups to expect

How would you decide when to re-plan? (When a step fails, new information contradicts plan assumptions, or every N steps.)

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Compare planning strategies: ReAct, Plan-and-Execute, and Reflection.

Follow-ups to expect

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