What is it?
Rather than improvising every step, a planning agent first writes down what it intends to do, then carries it out and updates the plan when something unexpected happens. It is easier to review a plan than a stream of actions.
Also known as: task decomposition, plan and execute, planning agents, plan-and-solve, hierarchical planning agents, reflexion, replanning, agent reflection.
Technical definition
Patterns: plan-and-solve prompting (Wang et al., 2023), plan-and-execute architectures with a planner and executor, hierarchical task decomposition, and re-planning on failure or new information. Plans can be represented as structured lists/graphs (Structured Outputs from LLMs) and checked by programs or humans. Reflection loops (Reflexion, Shinn et al., 2023) feed outcomes back into subsequent attempts. Reliability challenges include plan drift, over-long plans and brittle dependencies between steps.
Entity type: technique · Freshness class: evolving.
When should I use it?
Use for multi-step tasks where oversight or reliability justify the overhead.
Implementation patterns
- Planner emits a JSON plan; executor runs each step with verification.
- Re-plan when a step fails or new information changes assumptions.
- Human approval of the plan before irreversible steps.
- Cap plan length and depth.
Technologies and frameworks
Commonly associated: LangGraph, structured output plans, task queues. Names are examples of what exists, not recommendations, and projects change quickly.
Practical guide
- Define available tools to the planner.
- Emit structured plans.
- Validate against allowed actions.
- Execute with checks.
- Re-plan on failure.
Common failure modes
- Plan drift: execution deviates from the plan unnoticed.
- Overly rigid plans.
- Hallucinated steps or unavailable tools.
- Plans that exceed context or budget.
Troubleshooting
- Steps depend on missing info — Add information-gathering steps and allow re-planning.
- Plan too long — Plan at a coarser level; expand just-in-time.
Contrasted with
- The ReAct Pattern (Reason + Act) — ReAct is incremental; planning commits to a structure first.
Questions this page answers
- How do AI agents plan?
- What is plan-and-execute?
- How do I break a goal into sub-tasks for an agent?
- What is Reflexion?
Typical goals: improve reliability on long tasks; add human review of plans.
Sources and verification
Status: not independently verified. This entry was authored on 2026-10-06 from general technical knowledge, without live checking of the sources below. Treat every source as a lead to confirm (identifier, title, authorship, current version) before publication or citation.
Freshness class: evolving — Established techniques whose best practice shifts as research and tooling move. Re-review every 6 months.
Canonical sources (unverified leads):
- Wang et al., Plan-and-Solve Prompting (2023) — arXiv:2305.04091
- Shinn et al., Reflexion (2023) — arXiv:2303.11366