Multi-Agent Role Definition Prompt
Generates crisp role prompts and handoff contracts for a team of agents.
/ quick answer
Use when splitting one overloaded agent into a small team. Generates crisp role prompts and handoff contracts for a team of agents.
You are designing a multi-agent system. Given the goal below, return:
1. ROLES — for each agent: name, single-sentence mandate, allowed tools, forbidden actions.
2. SYSTEM PROMPT — a ready-to-use prompt for each role (max 150 words each).
3. HANDOFF CONTRACTS — the exact JSON payload each agent passes to the next.
4. SUPERVISOR LOGIC — routing rules, retry limits, stop conditions.
5. OVERLAP CHECK — call out any two roles that could be merged, and recommend merging them if the split adds no value.
Goal: {{GOAL}}
Constraints: {{CONSTRAINTS}}1. ROLES — Researcher: gathers and verifies claims. Tools: web_search, fetch_page. Forbidden: writing prose...
/ frequently asked
What does the Multi-Agent Role Definition Prompt prompt do?
Use when splitting one overloaded agent into a small team.
Which AI models work with this prompt?
It is model-agnostic: it works with any capable general model. Replace the bracketed variables with your own context before running it.
How do I use this prompt?
1) Copy the prompt with the copy button. 2) Replace every bracketed variable with your real details. 3) Paste it into your AI assistant. 4) Compare the answer with the example output and ask for changes if needed.
What output should I expect?
1. ROLES — Researcher: gathers and verifies claims. Tools: web_search, fetch_page. Forbidden: writing prose...
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →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.
- →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.
- →AI Orchestration
Coordinating multiple AI models, tools and steps into a single reliable workflow.
Related workflows
Turn this into a repeatable process.
- →Build a Multi-Agent Content Team
Researcher, writer and editor agents under a supervisor, producing publishable drafts.
- →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.
- →Design an Agent Architecture Before Writing Code
A one-page design process that prevents the most expensive agent rebuilds.
- →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.
- →Multi-Agent Orchestration Stack
Tooling for coordinating several specialised agents with reliable handoffs.
- →Multi-Agent Orchestration Stack (CrewAI)
This stack outlines the essential components for deploying a robust multi-agent system using CrewAI, focusing on agent definition, tool integration, and monitoring for collaborative task execution.
- →Agent Architecture Stack
The minimum tooling to design, run and observe a production agent.
Related prompts
Reusable prompts for this job.
- →Agent Architecture Spec Prompt
Turns a fuzzy agent idea into a reviewable five-layer architecture spec.
- →Autonomy Boundary Prompt
Defines exactly what an agent may do alone, with approval, or never.
- →AI Employee Job Description Prompt
Writes the role spec, KPIs and review cadence for an agent that owns a function.
- →Coding Agent Task Spec Prompt
Writes the task brief that keeps a coding agent inside scope.
Related use cases
How people apply it, and what came out.
- →Ecommerce Team Runs a Multi-Agent Catalog Pipeline
Three agents enrich, write and QA 12,000 product listings.
- →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.
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.