![[Day 7/100] Chain-of-Thought, Tree-of-Thought, and Plan-and-Execute](https://miro.medium.com/v2/resize:fit:700/1*k0smUGP4XmT47yGa-iZPGg.png "[Day 7/100] Chain-of-Thought, Tree-of-Thought, and Plan-and-Execute")
[Day 7/100] Chain-of-Thought, Tree-of-Thought, and Plan-and-Execute
Last Updated on August 25, 2026 by Editorial Team
Author(s): Montasir Mahmud
Originally published on Towards AI.
[Day 7/100] Chain-of-Thought, Tree-of-Thought, and Plan-and-Execute
ReAct is the workhorse of modern agents, but it is not the only pattern. Yesterday we named three places ReAct struggles: long-horizon tasks where the model loses the plan, problems that benefit from exploring alternatives, and workflows where the plan should be approved before any action runs.
After introducing ReAct’s shortcomings, the article presents three alternative agentic reasoning patterns—Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Plan-and-Execute—and explains when each is most effective. CoT is the “think step by step” approach for problems that can be solved from internal knowledge without tools, improving correctness and audibility by constraining intermediate inferences. ToT treats reasoning as a search tree, generating multiple candidate paths, evaluating them, and pruning poor branches; it shines on search-heavy tasks like puzzles, creative planning, and combinatorial problems but costs significantly more tokens due to many model calls. Plan-and-Execute separates planning from execution: a planner first outputs an ordered list of steps, and an executor then runs them (often with smaller models, parallelization, and human approval), reducing latency and cost—though it may require replanning if the world changes or a step fails. The piece also provides a decision table, discusses how these patterns can be composed, and ends with a comparison example and homework exercises to practice choosing the right pattern based on whether plans are dynamic, tools/search are needed, and cost constraints matter.
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