ReAct vs Plan-and-Execute: Which Pattern to Use?

Choosing between react vs plan and execute is one of the first real architecture decisions you make once an agent stops being a demo and starts doing work someone depends on. Both patterns are legitimate; the internet's advice is mostly vibes. This guide is grounded in how the two actually behave on capable models, names the specific task shapes where each one wins, and shows you how to settle the question empirically on your own task instead of arguing about it. By the end you'll know which to reach for first — and how to A/B the two in Promptise Foundry by flipping a single argument.

The two patterns, in one paragraph each

ReAct interleaves reasoning and acting in a tight loop: the model thinks, calls a tool, reads the result, thinks again, and repeats until it can answer. There is no upfront plan — the path emerges as the model learns from each tool result. In Promptise this is the default. agent_pattern="react" builds a single reasoning node with your tools attached, and the engine loops LLM → tools → LLM until a final answer. It's the simplest, fastest, and most broadly useful shape, which is exactly why it's the default in reasoning patterns.

Plan-and-Execute front-loads the thinking: the model drafts an explicit multi-step plan first, then executes the steps, ideally revising the plan when a step fails. Promptise's production implementation of this family is PEOATR — Plan → Act → Think → Reflect. agent_pattern="peoatr" builds four specialized nodes: a Plan node that creates and self-evaluates subgoals, an Act node that runs tools, a Think node that analyzes each result, and a Reflect node that decides whether to continue, replan, or answer. The full state machine, including the replan loop, is documented in the prebuilt patterns reference.

React vs plan-and-execute: an agent reasoning pattern comparison

The trade-off is structural, not about which is "smarter." Here's the honest agent reasoning pattern comparison:

Dimension	ReAct (react)	Plan-and-Execute (peoatr)
Control flow	One adaptive loop	Four staged nodes with a replan cycle
Latency	Lower — no separate planning turns	Higher — planning and reflection cost extra LLM calls
Token cost	Leaner on short-to-medium tasks	Heavier — the plan and reflections travel in context
Error recovery	Corrects opportunistically inside the loop	Explicit: a Reflect node can throw out a bad plan and replan
Transparency	The reasoning trace is the plan	The plan is a first-class, inspectable artifact
Sweet spot	Most tool-calling and Q&A tasks	Many interdependent subgoals, replanning under failure

The key thing this table hides is a bias: on a strong model, ReAct's opportunistic loop already does a lightweight form of planning in its head. PEOATR's extra structure only pays off when that implicit planning genuinely isn't enough. That's the case less often than the hype suggests — but when it happens, it matters.

When to use a planning agent

A planning agent earns its extra latency and tokens on tasks with a specific shape. Reach for peoatr when:

• The task has many interdependent subgoals where the order and dependencies matter, and getting step 3 wrong quietly corrupts steps 4 through 8. An explicit plan makes those dependencies visible before execution starts.
• Failure requires re-planning, not just retrying. If a tool returns something that invalidates the current approach, PEOATR's Reflect node can route back to Plan and draft a genuinely different strategy — where a ReAct loop tends to keep nudging the same doomed path.
• Self-correction over a long horizon is the whole point. Multi-step research, reconciliation, and migration-style tasks benefit from a stage whose only job is to evaluate progress and decide "continue, replan, or answer."

If your task doesn't have that structure — most don't — the planning machinery is overhead you'll feel as slower, pricier runs without a matching accuracy gain.

When ReAct (plus code-action) is the better fit

Here's the part most comparison posts skip. On capable, modern models, explicit multi-stage planning usually loses to plain ReAct on latency and token cost while matching it on accuracy. Promptise's own guidance is blunt about this: don't reach for a multi-stage pattern expecting more correctness — on strong models they mostly add latency and tokens. So react should be your default, and you should switch away from it only when you can point at the task shape that justifies it.

There's also a common failure mode people misdiagnose as "I need a planner." When a task is really gather many facts, then compute over them — sum a department's salaries, join records across sources, average a metric across a graph — neither a longer ReAct loop nor a PEOATR plan fixes the real problem, which is that models aggregate unreliably in their heads. The right answer is agent_pattern="code-action": the model writes one Python program over your tools and runs it in a hardened sandbox, so the arithmetic is exact and deterministic. If your "I think I need planning" task is actually a computation task, read the code-action guide before you add planning stages.

So the honest hierarchy is: react for most work, code-action for compute-heavy aggregation, and peoatr for genuinely plan-shaped tasks with replanning. For the full menu and how the wrapper stays constant across all of them, the complete guide to agent reasoning patterns walks through every option.

Switch patterns with one argument

The best part of not knowing the answer in advance is that in Promptise you don't have to guess — you measure. The reasoning pattern is a single agent_pattern argument. Everything around it — your MCP servers, memory, guardrails, semantic cache, and observability — is the same wrapper regardless of which pattern you choose. That means an A/B test is a two-line change, not a rewrite.

import asyncio
from promptise import build_agent
from promptise.memory import ChromaProvider
from promptise.cache import SemanticCache

# One shared configuration. Memory, cache, guardrails, and observability are
# identical for both agents — only `agent_pattern` differs between them.
COMMON = dict(
    model="openai:gpt-5-mini",
    servers={},                       # or your MCP server specs
    instructions="You are an operations assistant. Use the available tools.",
    memory=ChromaProvider(persist_directory="./memory"),
    cache=SemanticCache(),
    guardrails=True,
    observe=True,
)


async def ab_test(task: str) -> None:
    react = await build_agent(**COMMON, agent_pattern="react")
    planner = await build_agent(**COMMON, agent_pattern="peoatr")

    prompt = {"messages": [{"role": "user", "content": task}]}
    react_answer = (await react.ainvoke(prompt))["messages"][-1].content
    planner_answer = (await planner.ainvoke(prompt))["messages"][-1].content

    print("ReAct  →", react_answer)
    print("PEOATR →", planner_answer)

    await react.shutdown()
    await planner.shutdown()


asyncio.run(ab_test(
    "Reconcile March invoices against payments and list every mismatch."
))

Because observe=True records a timeline of every LLM turn and tool call, you can compare the two runs on the things that actually matter — number of turns, tool calls, latency — rather than on which answer reads better. And since both agents share the same SemanticCache, repeated experiments get cheaper as similar queries are served from cache; the framework's published figure for the semantic cache is a 30–50% cost reduction on repeated, semantically similar traffic. Run the test on five or ten representative tasks from your own workload and let the timelines decide.

Frequently asked questions

Is ReAct or Plan-and-Execute better for production agents?

For most production agents, ReAct is the better default: it's lower latency, cheaper in tokens, and simpler to reason about, and on capable models it matches planning on accuracy. Switch to Plan-and-Execute (peoatr) only when your task has many interdependent subgoals or needs to replan after failures. The right move is to A/B both on your real tasks rather than pick from a blog table.

What is the best agent reasoning pattern?

There isn't a single best agent reasoning pattern — it depends on task shape. Use react for general tool-calling and Q&A, code-action when the task is "gather facts then compute," and peoatr for plan-shaped, self-correcting work. Because Promptise exposes all of them behind one agent_pattern argument, the cheapest way to find the best pattern for your task is to test two or three directly.

Does switching patterns change my agent's tools or security?

No. The reasoning pattern only replaces the inner loop. Your MCP servers, memory provider, guardrails, semantic cache, observability, and approval gates stay exactly the same across react, peoatr, and every other prebuilt — which is what makes an apples-to-apples comparison possible in the first place.

Next steps

Stop debating react vs plan and execute in the abstract — A/B the two patterns on your own task by flipping one argument in build_agent(), and let the observability timeline tell you which one earns its cost. New to the framework? Start with the Quick Start, then browse the full menu of prebuilts in the reasoning patterns reference to see where react, peoatr, and code-action each fit.