---
title: "ReAct vs. Plan-and-Execute: Two Agent Architectures"
url: "https://atlan.com/know/ai-agent/react-vs-plan-and-execute-agent-architecture/"
description: "Compare ReAct and Plan-and-Execute across adaptability, token cost, latency, failure modes, and the enterprise context both require to work correctly."
author: "Karthik Pasupathy"
author_role: "Contributing Writer — AI Context & Agents"
published: "2026-08-13"
updated: "2026-08-13T00:00:00.000Z"
---

---

ReAct works step by step: the agent thinks, acts, observes the result, and reasons again before choosing its next action. Plan-and-Execute creates the full plan upfront, hands each step to an executor, and returns to the planner when something breaks or changes. Atlan's Context Layer for AI sits beneath either choice, supplying the governed definitions and evidence both patterns depend on to reason correctly.

Their practical differences show up across five dimensions:

* **Planning approach:** ReAct reasons, acts, and adjusts one step at a time; Plan-and-Execute maps the task before execution begins.

* **Workflow design:** ReAct keeps reasoning and tool use in one repeating loop; Plan-and-Execute separates the planner from the executor.

* **Adaptability:** ReAct uses each tool result to decide what happens next; Plan-and-Execute changes course when its re-planning logic is triggered.

* **Latency:** ReAct waits for a new model decision after every observation; Plan-and-Execute adds an upfront plan but can be faster with smaller executors or parallel branches.

* **Best fit:** ReAct suits unpredictable or exploratory tasks; Plan-and-Execute suits stable, multi-step workflows with known dependencies.

Below, we examine how each architecture works, where it breaks, how hybrid variants change the tradeoffs, and why enterprises need governed context regardless of the pattern they choose.

---

## What does the ReAct vs. Plan-and-Execute choice actually decide?

The choice determines when an agent creates a plan and when new observations can change it. ReAct reassesses the next action after every tool result. Plan-and-Execute defines a sequence upfront and returns to planning when a failure, invalid assumption, or other re-planning condition is detected.

Atlan sits beneath these patterns as the Context Layer for AI. The agent architectures determine the order of work, while governed context supplies the definitions, relationships, policies, and evidence used to make each decision.

This distinction separates three design choices. [Reasoning techniques](https://atlan.com/know/llm-reasoning/) determine how the model works through an individual step. The choice between [single-agent and multi-agent systems](https://atlan.com/know/single-agent-vs-multi-agent-systems/) determines who performs the work. ReAct versus Plan-and-Execute determines when planning happens and when the agent can change course. Both patterns can operate within the broader [perceive-reason-act loop](https://atlan.com/know/ai-agent/ai-agent-architecture-explained/).

The choice also does not determine autonomy. [Autonomous agents and copilots](https://atlan.com/know/autonomous-agents-vs-copilots/) can use either architecture, with different levels of human review and permission.

In [a complete AI agent stack](https://atlan.com/know/ai-agent-stack/), ReAct or planner-executor logic belongs in the orchestration layer or agent runtime. The context layer remains shared infrastructure beneath it, allowing teams to change frameworks or loop structures without rebuilding the business meaning their agents consume.

---

## How does the ReAct loop work, and where does it break?

ReAct follows a repeating cycle: the agent reasons about its current state, chooses an action, observes the result, and reasons again. Because each observation informs the next decision, the agent can change course after every step instead of following a fixed multi-step plan.

According to Yao et al. (2023), the [original ReAct paper](https://arxiv.org/abs/2210.03629) generated reasoning traces alongside task-specific actions, allowing new observations to update the model's evolving plan. On ALFWorld, ReAct outperformed the named imitation and reinforcement-learning methods by 34 percentage points in absolute success rate. This result applies to the paper's benchmark and prompting setup, not every production ReAct agent.

That feedback loop is useful when tool outputs are uncertain or likely to change what the agent should do next. It makes [tool use and function calling](https://atlan.com/know/ai-agent/ai-agent-tool-use/) responsive to actual results rather than assumptions made at the beginning of the task.

The same feedback loop can produce three failure modes:

* **Repeated reasoning:** The agent spends tokens reconsidering decisions that a stable plan could have fixed once.

* **Goal drift:** Actions that seem useful in the moment can accumulate into a path that takes the agent away from its overall objective.

* **Faulty grounding:** Stale or conflicting context is retrieved and trusted again at every step.

The last failure shows why [context-aware AI agents](https://atlan.com/know/context-aware-ai-agents/) still require approved, current, and relevant context. Repeated reasoning cannot improve [AI agent accuracy](https://atlan.com/know/ai-agent/ai-agent-accuracy/) when every new decision starts from the same incorrect business meaning.

Plan-and-Execute reduces some of this repeated decision-making by creating the plan upfront. Its weakness appears when that initial plan no longer matches what happens during execution.

---

## How does Plan-and-Execute separate planning from execution, and where does it break?

Plan-and-Execute separates high-level planning from step-level execution. The planner breaks down the objective into a sequence of tasks, while the executor determines which tools or actions are needed to complete each one. Re-planning is an additional mechanism that must be triggered when execution invalidates the original plan.

LangChain's 2023 [Plan-and-Execute reference implementation](https://www.langchain.com/blog/plan-and-execute-agents) used a language model as the planner and an action agent as the executor. LangChain noted that the design required more model calls, although smaller and faster executor models could offset some of that overhead. Its initial implementation created one plan at the start and did not revisit it.

Breaking down the task upfront works best when dependencies are stable enough to map in advance. It also makes the [task breakdown and tool selection](https://atlan.com/know/ai-agent/ai-agent-planning/) easier to inspect before execution begins. [Versioned context](https://atlan.com/know/ai-agent/context-versioning-for-ai-agents/) matters because every planned step inherits the definitions, relationships, and assumptions available to the planner.

Plan-and-Execute commonly breaks in three ways:

* **Incorrect premise:** Stale or conflicting context at planning time shapes the entire plan. The executor can complete every step correctly and still produce the wrong result.

* **Plan staleness:** The plan begins with valid assumptions, but conditions change during execution. Without validation checkpoints or re-planning, the executor may continue until a later step fails or the final result is checked.

* **Poor re-planning triggers:** The system fails to revisit the plan after a tool error, a result that contradicts an assumption, a completed step that does not produce the expected progress, or a scheduled checkpoint. If triggers are too broad, the agent re-plans so often that it loses the benefit of planning upfront.

A 2025 [security-focused Plan-then-Execute preprint](https://arxiv.org/abs/2509.08646) argues that separating strategic planning from tactical execution can improve integrity when combined with least privilege, task-scoped tools, sandboxing, and re-planning. This is the authors' architectural analysis, not a universal security benchmark.

An [agent harness](https://atlan.com/know/what-is-an-agent-harness/) can enforce these controls, but it cannot correct the definitions or assumptions used to create the plan. The [AI agent cold-start problem](https://atlan.com/know/ai-agent-cold-start-problem/) therefore exists before the planner begins its work.

Plan-and-Execute produces a clearer plan for review, but its efficiency and reliability depend on how often that plan must be checked, revised, or rebuilt.

---

## ReAct vs. Plan-and-Execute: token cost, latency, and accuracy tradeoffs

Neither architecture guarantees lower token use, lower latency, or higher accuracy. Results depend on task length, how often conditions change, model choices, prompt size, tool latency, dependencies between steps, retries, and re-planning.

Use these dimensions as hypotheses to test on the same workload:

| Dimension | ReAct | Plan-and-Execute | What to evaluate |
| :---- | :---- | :---- | :---- |
| Planning | Reasons and chooses the next action after each observation; may update its evolving plan | Creates a multi-step plan before the executor begins | Match the planning pattern to how often new results change the task |
| Token use | Prior history, observations, and tool instructions may be repeated as the loop grows | Adds a planning call and executor calls; smaller executor models may offset some overhead | Measure total tokens and model cost per completed task |
| Latency | A model decision and tool call occur sequentially at each step | Basic implementations also execute sequentially and add an initial planning call; graph variants may parallelize independent steps | Measure end-to-end latency instead of assuming either pattern is faster |
| Adaptability | Each observation can influence the next action | The plan changes only when the implementation checks results and re-plans | Define plan-validity checks and re-planning triggers |
| Traceability | Can expose a step-level record of actions and observations | Can expose the initial plan separately from execution and revised plans | Store actions, observations, plan versions, tool outputs, and overrides |
| Accuracy risk | Incorrect context or observations can redirect later actions | An incorrect or stale premise can affect every downstream step | Measure completed-task accuracy and validate the context behind the result |

Token efficiency and [agent cost at scale](https://atlan.com/know/ai-agent/cost-to-run-ai-agents-at-scale/) are implementation-dependent. ReAct can increase prompt size by carrying prior observations and tool history into later decisions. Plan-and-Execute adds planning and executor calls, although smaller executor models can reduce the cost of individual steps.

Latency requires a separate qualification. Basic Plan-and-Execute is still sequential and may be slower because it adds planning overhead. Planning-based variants such as LLMCompiler can reduce latency only when they identify independent steps and schedule them concurrently.

Accuracy needs the same caution. ReAct's reported gains apply to specific benchmarks and prompting setups. Plan-and-Execute can preserve a global plan, but it can also execute every step correctly against a false premise. [Production agent failures](https://atlan.com/know/why-ai-agents-fail-in-production/) can also result from missing context, permissions, tools, or evaluation rather than control flow.

Architecture is one part of [building an agent stack](https://atlan.com/know/ai-agent/ai-agent-applications/how-to-build-ai-agent-tech-stack/). Compare both patterns using the same models, tools, context, permissions, stopping rules, and evaluation set before attributing a difference in cost, latency, or accuracy to the architecture itself.

---

## How do ReWOO, LLMCompiler, and re-planning variants change the tradeoffs?

ReWOO, LLMCompiler, and re-planning agents do not combine ReAct and Plan-and-Execute in the same way. Each changes a different design variable: when reasoning occurs, whether independent tasks can run together, or when execution results can revise the plan.

Three variants show the differences:

* **ReWOO separates planning from observations:** The planner creates a reasoning plan containing tool calls and placeholders for their results. Workers execute those calls and fill in the evidence, while a solver uses the completed plan to produce the answer. Unlike ReAct, the planner does not wait for every tool result before deciding the remaining steps. Xu et al. (2023) reported 5x token efficiency and a 4% accuracy improvement on HotpotQA in the [ReWOO paper](https://arxiv.org/abs/2305.18323). These results are specific to the paper's experimental setup.

* **LLMCompiler parallelizes independent function calls:** A planner creates an execution plan, a task-fetching unit dispatches calls when their dependencies are satisfied, and executors run ready calls in parallel. Kim et al. (2024) reported maximum improvements of 3.7x in latency, 6.7x in cost, and approximately 9% in accuracy compared with ReAct in the [LLMCompiler paper](https://arxiv.org/abs/2312.04511). These are the highest reported improvements, not guaranteed gains for every workload.

* **Re-planning adds feedback to a plan-first loop:** The agent evaluates intermediate results and gives the planner an opportunity to revise the remaining steps. For example, OpenSearch's plan-execute-reflect implementation re-evaluates the plan after every completed step and can add, remove, or change subsequent steps. Other implementations may re-plan only at checkpoints, after failures, or when an assumption is invalidated.

The practical decision is therefore where the workflow should pause for new evidence, which dependencies can run in parallel, and what conditions should invalidate the remaining plan.

These are control-flow designs, not framework categories. [LangGraph](https://atlan.com/know/ai-agent/ai-agent-memory/what-is-langgraph/) can represent loops, conditional branches, shared state, and parallel tool execution, but teams must still define dependencies, validation points, and re-planning rules.

These variants can reduce repeated reasoning or sequential waiting. They still depend on the correctness and freshness of the context used to create, execute, and revise the plan.

---

## Why doesn't choosing an agent architecture fix a context problem?

Choosing an agent architecture does not fix a context problem because architecture determines how an agent proceeds, not whether its assumptions are correct.

Suppose a revenue agent encounters two definitions of ARR. A ReAct agent may use the wrong definition to guide each subsequent decision. A Plan-and-Execute agent may use it once, build a coherent plan around it, and complete every step correctly while still producing the wrong answer.

New observations help only when they expose the faulty assumption or give the agent access to better context. Repeated reasoning cannot reconcile conflicting definitions on its own, and a well-structured plan cannot make stale context current.

Both architectures therefore depend on the same foundation: approved definitions, relationships, ownership, policies, lineage, access rules, and context versions. ReAct needs that context throughout its loop. Plan-and-Execute needs it before planning and whenever the plan is validated or revised.

Atlan's position is that this foundation should not be tied to one architecture. The same governed context should support ReAct, Plan-and-Execute, ReWOO, LLMCompiler, and future variants without becoming framework-specific integration work.

---

## How does Atlan ground ReAct and Plan-and-Execute in governed context?

Atlan provides a shared Context Layer beneath either architecture. It connects enterprise data and business meaning, helps teams prepare and test that context, and makes the approved result available to different agent frameworks and execution environments.

The capabilities map to the needs of each architecture as follows:

| Architecture need | Atlan capability | Practical effect |
| :---- | :---- | :---- |
| Ground each ReAct decision | [Enterprise Data Graph](https://atlan.com/know/enterprise-data-graph/) and Atlan **MCP Server** | The agent can access definitions, lineage, ownership, policies, and other context through a standard interface |
| Ground the initial Plan-and-Execute plan | Enterprise Data Graph and **[Context Agents](https://atlan.com/context-agents-demo/)** | The planner can begin with connected business meaning instead of isolated schemas or documents |
| Test context before deployment | [Context Engineering Studio](https://atlan.com/context-engineering-studio/) and versioned Context Repos | Teams can evaluate a bounded context package against real questions and identify gaps before agents consume it |
| Reuse context across architectures | Context Repos and [Atlan MCP Server](https://atlan.com/mcp-server/) | ReAct, planner-executor, and hybrid workflows can consume the same approved context without rebuilding it for each framework |

Context Agents can help bootstrap context from signals such as query history, lineage, and BI semantics, while human review remains necessary for business logic, exceptions, and approval.

These capabilities do not make one agent architecture universally better. They make the architecture decision easier to revisit because the definitions, evidence, and policies beneath it remain shared.

---

## Why is the architecture decision second-order?

ReAct and Plan-and-Execute differ mainly in when planning happens. ReAct reasons again after each observation, while Plan-and-Execute creates a plan before execution. ReWOO, LLMCompiler, and re-planning variants adjust this balance by separating reasoning, parallelizing work, or revising plans.

Neither architecture automatically guarantees better accuracy, lower token use, or lower latency. Teams should compare them using the same tasks, models, tools, permissions, and context. Whichever pattern they choose, Atlan provides shared definitions, lineage, policies, and evidence through a Context Layer that remains consistent as the control flow changes.

  Context Layer ROI Calculator
  Model what governed context is worth beneath either architecture choice, before deciding how much re-planning or tool overhead is worth paying for.
  Calculate Your ROI

---

## FAQ

### 1. What is the difference between a ReAct agent and a Plan-and-Execute agent?

ReAct chooses one action after the latest observation. Plan-and-Execute creates a multi-step plan first, then executes it. The difference is control flow, not model capability.

### 2. When should you use ReAct instead of Plan-and-Execute?

Use ReAct when results are unpredictable and likely to change the next action. Use Plan-and-Execute when dependencies can be mapped upfront and remain stable enough to review. Test both when a task combines stable planning with uncertain execution.

### 3. Is Plan-and-Execute more token-efficient than ReAct?

Not necessarily. An upfront plan can reduce repeated reasoning, but planning and executor calls consume their own tokens. Re-planning can add more, so compare total tokens per successfully completed task.

### 4. What is ReWOO and how does it relate to Plan-and-Execute?

ReWOO means Reasoning WithOut Observation. Its planner creates a reasoning plan and tool placeholders before workers retrieve the required evidence. This makes it closer to Plan-and-Execute than step-by-step ReAct, although a solver still combines the plan with the retrieved evidence.

### 5. Can an agent combine ReAct and Plan-and-Execute by re-planning after failure?

Yes. A hybrid can plan upfront, execute a step, inspect its result, and revise the remaining plan. Re-planning may be triggered by tool errors, unexpected results, insufficient progress, or scheduled checkpoints.

### 6. Does LangGraph support both ReAct-style and Plan-and-Execute-style agents?

Yes. Stateful graphs can represent ReAct cycles, planner-executor splits, conditional branches, and re-planning. Teams still must design state, permissions, stopping rules, and evaluations.

### 7. Why do Plan-and-Execute agents fail when the environment changes mid-task?

The assumptions used to create the plan may no longer hold during execution. Without validation checkpoints or re-planning, the executor may continue through obsolete steps until a later failure or final review. Freshness checks and explicit invalidation rules can trigger an earlier review, but they do not guarantee correctness.

### 8. How many tool calls does a typical ReAct agent make compared to a Plan-and-Execute agent?

There is no universal count. ReAct follows a stopping condition, while Plan-and-Execute depends on plan length and executor behavior. Compare successful calls, retries, failures, and re-plans on the same tasks.

### 9. What is LLMCompiler and how is it different from ReWOO?

LLMCompiler creates an execution plan and runs independent function calls in parallel. ReWOO separates upfront reasoning from tool observations through a planner-worker-solver design. Both are plan-first alternatives to step-by-step ReAct, but LLMCompiler targets sequential latency while ReWOO targets repeated reasoning and prompt use.

### 10. Is ReAct or Plan-and-Execute the better default for enterprise data agents?

Neither is a universal default. ReAct fits tasks where observations frequently change the next action, while Plan-and-Execute fits stable and reviewable dependencies. Choose the simplest pattern that meets accuracy, latency, cost, context, permission, and recovery requirements on representative tasks.

---

## Sources

1. ReAct: Synergizing Reasoning and Acting in Language Models, arXiv (Yao et al., 2023). https://arxiv.org/abs/2210.03629
2. Plan-and-Execute Agents, LangChain (2023). https://www.langchain.com/blog/plan-and-execute-agents
3. ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models, arXiv (Xu et al., 2023). https://arxiv.org/abs/2305.18323
4. An LLM Compiler for Parallel Function Calling, arXiv (Kim et al., 2024). https://arxiv.org/abs/2312.04511
5. Architecting Resilient LLM Agents: A Guide to Secure Plan-then-Execute Implementations, arXiv (Del Rosario et al., 2025). https://arxiv.org/abs/2509.08646