Agent Planning (ReAct, Plan-and-Execute)
Agent planning is how an AI agent decides its next step — reactively step-by-step (ReAct) or by drafting a full plan up front (plan-and-execute).
/ quick answer
Planning strategies trade adaptability against predictability. ReAct interleaves reasoning and tool calls one step at a time: flexible, but prone to drift on long tasks. Plan-and-execute writes the whole plan first and then runs it: cheaper, auditable, but brittle when reality differs from the plan. Hybrid agents plan first, then replan whenever an observation invalidates a step.
/ frequently asked
What is Agent Planning (ReAct, Plan-and-Execute)?
Planning strategies trade adaptability against predictability. ReAct interleaves reasoning and tool calls one step at a time: flexible, but prone to drift on long tasks. Plan-and-execute writes the whole plan first and then runs it: cheaper, auditable, but brittle when reality differs from the plan. Hybrid agents plan first, then replan whenever an observation invalidates a step.
What is an example of Agent Planning (ReAct, Plan-and-Execute)?
A migration agent drafts a 9-step plan, executes steps 1–3, discovers a schema mismatch, replans steps 4–9 and continues.
Why does Agent Planning (ReAct, Plan-and-Execute) matter?
Agent planning is how an AI agent decides its next step — reactively step-by-step (ReAct) or by drafting a full plan up front (plan-and-execute).
What should I learn after Agent Planning (ReAct, Plan-and-Execute)?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →CrewAI Framework
CrewAI is a framework for orchestrating role-playing autonomous AI agents, enabling them to collaborate, delegate, and execute tasks as a team. It focuses on clearly defined roles, tasks, and a shared goal to build highly collaborative AI systems.
- →Agent Architecture
Agent architecture is the structural blueprint of an AI agent: the model, the planning loop, the tools, the memory layer and the guardrails that decide how it acts.
- →Multi-Agent System
A multi-agent system splits a job across several specialised AI agents that coordinate through a shared plan, message bus or orchestrator.
- →Agent-to-Agent Protocol (A2A)
An Agent-to-Agent (A2A) protocol defines the communication standards and rules that enable autonomous AI agents to interact and exchange information or services directly with each other without human intervention. This foundational concept is crucial for building complex, collaborative multi-agent systems.
Related workflows
Turn this into a repeatable process.
- →Design an Agent Architecture Before Writing Code
A one-page design process that prevents the most expensive agent rebuilds.
- →Automate Agent Task Delegation
This workflow automates the process of an initiating AI agent delegating a complex task to a specialized recipient AI agent using a standardized Agent-to-Agent (A2A) protocol, ensuring efficient task handoff and execution. It streamlines multi-agent collaboration for defined objectives.
- →Build a Multi-Agent Content Team
Researcher, writer and editor agents under a supervisor, producing publishable drafts.
- →Cut Agent Costs by 60% Without Losing Quality
A measurable cost-reduction pass for any agent already in production.
Related tool stacks
The tools that run it in production.
- →Agent Architecture Stack
The minimum tooling to design, run and observe a production agent.
- →Agent Economics Observability Stack
This stack provides tools to monitor, analyze, and optimize the economic performance of AI agents, focusing on token costs, performance, and ROI.
- →Multi-Agent Orchestration Stack
Tooling for coordinating several specialised agents with reliable handoffs.
- →AI Agent + Web3 Stack
Agent framework, MCP/API tools, blockchain data access and a limited signing layer — with policy enforced in code.
Related prompts
Reusable prompts for this job.
- →Agent Architecture Spec Prompt
Turns a fuzzy agent idea into a reviewable five-layer architecture spec.
- →Multi-Agent Role Definition Prompt
Generates crisp role prompts and handoff contracts for a team of agents.
- →Crypto Agent Design Prompt
Specifies an AI crypto agent's tools, permissions, guardrails and evaluation before a single line is written.
- →Wallet Monitoring Agent Prompt
System prompt for a read-only agent that watches addresses, filters noise and reports only decision-relevant activity.
Related use cases
How people apply it, and what came out.
- →SaaS Cuts First-Response Time from 6h to 4min with an Agent
A 12-person SaaS shipped a tier-1 support agent and kept humans on exceptions only.
- →Ecommerce Team Runs a Multi-Agent Catalog Pipeline
Three agents enrich, write and QA 12,000 product listings.
- →Build An AI Crypto Research Agent
A read-only research agent produced daily briefings on a 30-token watchlist, cutting a 90-minute manual routine to a 10-minute review.
Comparisons & alternatives
Pick between the options.
- →Single Agent vs Multi-Agent System
One well-equipped agent beats a crowd for most jobs; multi-agent wins on genuinely separable, parallel work.
- →AI Agent vs Trading Bot
A trading bot executes fixed rules deterministically; an AI agent interprets context and decides which steps to take — powerful for research, risky for execution.
- →Reasoning Model vs Standard LLM
Reasoning models are more accurate on multi-step problems but slower and pricier; standard calls suit simple, fast tasks.