Even a skilled model must have the ability to dissect a goal into smaller tasks, evaluate different strategies, and identify errors in execution. These reasoning patterns, such as Chain-of-Thought, ReAct, Reflexion, Tree of Thoughts, and Plan-and-Execute, are what enable agents to possess such capabilities.
Agent planning and reasoning The foundational layer consists of techniques that enable an AI agent to break down a goal, determine the next steps, and adjust course if necessary, rather than providing a single automatic response. Chain-of-Thought In addition, there are four active production patterns sitting on top of that. These patterns are currently being utilized. ReAct (interleave reasoning with tool actions), Reflexion (self-critique and retry), Tree of Thoughts (explore multiple reasoning paths, not just one), and Plan-and-Execute / ReWOO When committing to a full plan upfront, most production planners rely on one of five strategies: task decomposition, multi-plan selection, external module-aided planning, reflection and refinement, or memory-augmented planning.
Beneath each agentic workflow lies a reasoning engine responsible for two tasks. Planning breaks down an open-ended objective into a series of smaller, manageable logical steps. Tool calling Every step is carefully validated, whether it involves fact-checking, querying, or utilizing an API, to ensure that the agent's subsequent decisions are based on concrete information rather than mere speculation.
On straightforward tasks, a detailed planning stage may not be needed. Instead, an agent can continuously evaluate and enhance a response without formal planning. Planning becomes essential for tasks with intricate decision-making processes, such as multiple viable strategies, interdependent steps, or a risk of errors.
Chain-of-Thought serves as the foundational reasoning baseline. While subsequent patterns may incorporate action, reflection, branching, or upfront commitment, they do not supplant it.
Chain-of-Thought prompting requires the model to display its step-by-step reasoning process leading to an answer, without skipping ahead. It solely relies on reasoning, breaking down the problem into a series of smaller logical steps with no outside assistance.
It significantly enhances multi-step reasoning and decomposition, explaining why it is a crucial component in nearly every advanced agent pattern instead of being substituted by other methods.
ReAct connects logic to tool utilization by having the agent think, act, observe, and incorporate feedback in a continuous loop of Thought → Action → Observation until completion.
As each intermediate decision is documented, the entire trajectory is open to inspection and audit. This transparency is a key reason why ReAct has become the standard pattern in production agent frameworks: it combines adaptability with a logical trail that can be verified. However, its primary drawback is a tendency to get stuck in loops, where the same actions and thoughts are repeated without making any real advancement if the model cannot find a new perspective.
The majority of agent architectures include a clear planning phase that utilizes one or more of these five strategies prior to carrying out any actions.
Production planners often combine two or three of these strategies for more challenging tasks, as they are not mutually exclusive.
Divide a goal into smaller, sequential tasks that can be addressed individually.
Create multiple potential plans and select the most robust one prior to implementation.
Rely on a specialized planning module or solver in addition to the LLM.
Modify a plan during the course of action due to updated information or a discovered error.
Utilize past experiences to shape the development of the current plan.
Reflexion enhances a reasoning pattern, such as ReAct, by incorporating a self-reflection loop. Following each attempt, the agent provides a verbal critique of errors made, such as hitting a dead end or making incorrect assumptions. This critique is carried forward into the next attempt, preventing the repetition of mistakes.
The benefits of using Reflexion in coding benchmarks are remarkable, as models have shown significant increases in pass rates that exceed their own baseline performance solely due to the reflection step, not a stronger underlying model.
Chain-of-Thoughts sticks to one path of reasoning, while Tree of Thoughts delves into multiple branches simultaneously, considering numerous potential next thoughts and eliminating unpromising ones to delve further into the remaining branches.
The extra compute required justifies its use in situations where correcting a wrong turn early on is costly, such as in puzzles, intricate planning scenarios, or any task where backtracking results in wasted time.
ReWOO ('Reasoning WithOut Observation') differs from ReAct by planning the entire sequence of actions upfront and executing them without pausing to re-reason after each tool response, rather than interleaving thought and tool call at every step.
Breaking down the task into subtasks, executing them using real tools, and synthesizing the results into a final answer is the process followed by the planner, worker, and solver modules. While this approach can outperform ReAct on specific benchmarks and reduce the number of repeated tool calls, it comes with a downside. The fixed plan may struggle in situations where the environment lacks the expected context or when additional tools are introduced that were not accounted for in the initial plan.
No one pattern excels in all areas - cost, reliability, and interpretability each have their own priorities.
If you struggle with hallucinations during knowledge-heavy tasks, ReAct's reliance on real tool observations can be beneficial. If you tend to provide brittle one-shot answers for tasks with checkable outcomes, such as passing or failing code, Reflexion's self-critique loop may be worth the slight delay. If you are concerned about committing too early to a wrong approach, Tree of Thoughts' exploration of multiple branches can be valuable despite the increased computational requirements.
| Pattern | Core mechanism | Strength | Main cost or risk |
|---|---|---|---|
| Chain-of-Thought | Single-path step-by-step reasoning | Cheap, fast | No grounding, no correction |
| ReAct | Interleaved thought → action → observation | Inspectable, grounded | Can loop without progress |
| Reflexion | Self-critique after each attempt | Learns from failure | Extra latency per retry |
| Tree of Thoughts | Explore and prune multiple branches | Avoids early wrong turns | Higher compute cost |
| Plan-and-Execute / ReWOO | Plan upfront, then execute without pausing | Fewer redundant tool calls | Brittle to unplanned surprises |
These patterns often overlap, with production systems typically utilizing more than one. A popular pairing includes Reflexion and ReAct for adaptive, self-correcting behavior during lengthy tasks, or Plan-and-Execute with Tree of Thoughts for complex problems requiring both structure and flexibility.
The same fundamental loop guides them all: observe, think, act, and review before observing again. The key difference between a dependable agent and a flashy demo lies in the reasoning pattern chosen for each step in the loop.
Chain-of-Thought relies solely on reasoning, while ReAct incorporates action into the process. The agent interchanges between reasoning and implementing a tool, observing the tangible outcome before proceeding to the next thought. This approach provides a more dependable foundation for reasoning than relying solely on the model's assumptions.
Opt for Tree of Thoughts in situations where a mistake early on can be difficult to rectify, such as puzzles, complex planning with dependencies, or problems with multiple viable solutions. This method requires more computational resources as it explores various branches, making it unsuitable for straightforward tasks where Chain-of-Thought is sufficient.
After a failed attempt, reflection prompts the agent to write out a self-critique, identifying what went wrong in their own words. This feedback is then used to inform future attempts, especially beneficial for tasks with measurable outcomes like passing or failing code tests.
When the model relies on the same observation for each new thought, it can get stuck in a loop of regenerating similar thoughts and actions without moving forward. Adding a reflection step or hard step limit can help prevent this issue when using ReAct.
ReWOO carefully maps out a series of actions in advance and follows through without pausing to reevaluate after each tool response, in contrast to ReAct which alternates between reasoning and action at every turn. While this approach minimizes unnecessary tool calls and can excel in certain tests, it is less adaptable to unexpected environmental changes that were not accounted for in the initial plan.
Infrequently, production systems typically integrate various patterns such as Reflexion paired with ReAct for adaptive self-correction, or Plan-and-Execute mixed with Tree of Thoughts for long-term issues requiring both organization and exploration. These patterns serve as fundamental components within a larger perceive-reason-act-reflect loop, rather than being mutually exclusive alternatives.
Begin by identifying the failure mode that concerns you the most - whether it be hallucination, brittle one-shot answers, or early wrong turns - and allow that to dictate the pattern, rather than the other way around.