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).
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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 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 for AI and automation?
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). It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
/ 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 Handoff
Agent handoff is the controlled transfer of a task — with its context — from one agent to another agent or to a human.
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.
- →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.
- →AI Agent + Web3 Execution
Give an AI agent on-chain capability safely: read freely, propose openly, and sign only inside hard-coded limits.
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.
- →RAG vs Long Context Windows
Is RAG obsolete now that context windows are 1M+ tokens? Not quite — here's when each wins.