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...
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.
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.
- →AI Orchestration
Coordinating multiple AI models, tools and steps into a single reliable workflow.
- →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.
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.
- →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.
- →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.
- →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.
- →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.
- →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.