AI / Agentic AI Interview Questions
Why does ReAct carry higher latency and cost than Plan-and-Execute?
ReAct's core loop performs a full reasoning-action-observation cycle before every single step, which means every action in a multi-step task pays for another round of model inference.
- Each cycle consumes additional tokens for the reasoning text generated before the action
- Sequential reasoning and observation steps add latency, since each one depends on the result of the previous action
- Costs become less predictable, since the number of cycles needed to finish a task isn't known upfront
Plan-and-Execute avoids most of this overhead by generating the full plan once and executing it with minimal re-reasoning, at the cost of being less able to adapt if the plan turns out to be wrong partway through.
Why does each ReAct step add latency?
Why are ReAct's costs less predictable than Plan-and-Execute's?
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