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).
2 min readupdated 2026-08-01
/ 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.
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). 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. In practice: A migration agent drafts a 9-step plan, executes steps 1–3, discovers a schema mismatch, replans steps 4–9 and continues. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
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.
Example
A migration agent drafts a 9-step plan, executes steps 1–3, discovers a schema mismatch, replans steps 4–9 and continues.
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.