Build a Multi-Agent Content Team
Researcher, writer and editor agents under a supervisor, producing publishable drafts.
/ quick answer
Split the job into three specialised agents with explicit handoff contracts and an editor that can reject and reloop. Researcher, writer and editor agents under a supervisor, producing publishable drafts.
- 01Researcher agent: search + scrape, returns a structured brief (claims, sources, angles).
- 02Writer agent: consumes only the brief, never raw pages. Outputs draft + heading map.
- 03Editor agent: scores draft against a rubric (accuracy, originality, structure, hook).
- 04Supervisor: if score < threshold, return the rubric notes to the Writer (max 2 loops).
- 05Human approves before publish. Log every rejection reason as future eval data.
- 06Track cost and time per accepted draft; kill any agent that never changes the outcome.
/ frequently asked
What does the Build a Multi-Agent Content Team workflow do?
Split the job into three specialised agents with explicit handoff contracts and an editor that can reject and reloop.
What problem does Build a Multi-Agent Content Team solve?
A single prompt asking for a full article produces generic, unsourced content that still needs a full rewrite.
How many steps does Build a Multi-Agent Content Team take?
6 steps. It starts with researcher agent: search + scrape, returns a structured brief (claims, sources, angles). and ends with track cost and time per accepted draft; kill any agent that never changes the outcome..
How do I set up Build a Multi-Agent Content Team step by step?
1) Researcher agent: search + scrape, returns a structured brief (claims, sources, angles). 2) Writer agent: consumes only the brief, never raw pages. Outputs draft + heading map. 3) Editor agent: scores draft against a rubric (accuracy, originality, structure, hook). 4) Supervisor: if score < threshold, return the rubric notes to the Writer (max 2 loops). 5) Human approves before publish. Log every rejection reason as future eval data. 6) Track cost and time per accepted draft; kill any agent that never changes the outcome.
Which tools does Build a Multi-Agent Content Team need?
It uses multi-agent-orchestration-stack, content-creator-stack — each linked below with its own node.
/ 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 Content Pipeline
An end-to-end system that takes a topic and outputs publish-ready content.
Related workflows
Turn this into a repeatable process.
- →How to Build an AI Content System
A repeatable pipeline that turns one input into publish-ready content across every channel.
- →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.
Related tool stacks
The tools that run it in production.
- →Multi-Agent Orchestration Stack
Tooling for coordinating several specialised agents with reliable handoffs.
- →Solo Content Creator Stack
End-to-end AI stack for one operator running a multi-channel content engine.
- →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.
- →TikTok / Shorts Content Stack
Ship 5-10 short-form videos per week with one operator.
Related prompts
Reusable prompts for this job.
- →Multi-Agent Role Definition Prompt
Generates crisp role prompts and handoff contracts for a team of agents.
- →Content Repurposing Prompt
Atomize one long-form asset into 8 platform-native pieces.
- →Agent Architecture Spec Prompt
Turns a fuzzy agent idea into a reviewable five-layer architecture spec.
- →Crypto Agent Design Prompt
Specifies an AI crypto agent's tools, permissions, guardrails and evaluation before a single line is written.
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.
- →Build An AI Crypto Research Agent
A read-only research agent produced daily briefings on a 30-token watchlist, cutting a 90-minute manual routine to a 10-minute review.
- →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.