Planner–Worker–Reviewer Teams
The most robust agent architecture is also the simplest: a Planner decomposes the goal into tasks, Workers execute one task each with narrow context, and a Reviewer checks results against acceptance criteria before anything ships.
The prompt
PLANNER: Decompose this goal into 3-5 independent tasks with acceptance criteria each. Goal: [GOAL]. WORKER (per task): Complete exactly this task, nothing more: [TASK + CRITERIA]. REVIEWER: Check this result against the criteria. Verdict: PASS or FAIL + one-line reason. Result: [RESULT]
What to replace
Swap these placeholders for your own details before running the prompt:
[GOAL]your own value[TASK + CRITERIA]your own value[RESULT]your own value
Pro tip: The Reviewer must be a separate call with fresh context — models grade their own work far too generously.
How to use this prompt
- Copy the prompt using the button above.
- Replace [GOAL], [TASK + CRITERIA], [RESULT] with your own details — the more specific you are, the better the output.
- Paste it into Any model and run it.
- If the answer feels generic, add constraints: audience, length, tone, and what to avoid. That single change fixes most weak output.
Learn the technique
Planner–Worker–Reviewer Teams
Module 3 — Advanced: Multi-Agent Patterns · Loop & Agentic Engineering
Related prompts
Critique-and-Revise Loops — ChatGPT
A loop is simply feeding a model's own output back to it (or to a second prompt) for review and improvement, repeated until a quality bar is met.
ClaudeCritique-and-Revise Loops — Claude
A loop is simply feeding a model's own output back to it (or to a second prompt) for review and improvement, repeated until a quality bar is met.
ChatGPT / Agent toolsThe ReAct Pattern (Reason + Act)
ReAct loops interleave reasoning ("what should I do next?") with actions (calling a tool, searching, running code), then feed the result back in before reasoning again.
ClaudeGiving Loops Memory
For loops that run over many steps (research agents, multi-turn assistants), summarize prior steps into a short running memory instead of replaying the full history every time — this keeps context small and cheap..
Any modelLoop Budgets and Failure Handling
Production loops need budgets (max iterations, max cost, max time) and explicit failure paths: what happens when the Reviewer fails a result three times? Options: escalate to a human, fall back to a simpler method, or return a partial result flagged as unverified.
Any modelProject: Draft–Critique–Revise Content Machine
Deliverable: a working 3-role loop (writer, critic, editor) that produces a publishable piece of content on any topic you give it. Steps: 1.
Topics
Related across the site
AI Automation with n8n
Connect AI models to real workflows using n8n — the open-source automation tool. Learn the HTTP Request and AI Agent nodes, and build automations that run on a schedule or trigger.
Build a Meeting Notes System
Turn rambling notes into decisions, owners and deadlines — in a format consistent enough that you can search it six months later. The value is in the structure, not the summary.
Build a Personal Study Assistant
Turn a stack of lecture notes into a study partner that quizzes you, explains what you got wrong, and tracks which topics you keep failing. You will finish with a reusable system prompt you can point at any subject.
Build Your Own Prompt Library
Stop rewriting the same prompt. Build a small, organised, tested personal library — and learn the versioning habit that stops it rotting into a folder of near-duplicates.
Human in the loop
Human in the loop means inserting a person at decision points in an automated workflow — typically to approve consequential actions or review low-confidence outputs before they take effect.
n8n and AI: Build Your First Real Automation
A practical walkthrough of wiring an AI model into an n8n workflow — the node structure, the prompt that makes it reliable, and three automations worth building first.