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.
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..
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.
- →AI Content Pipeline
An end-to-end system that takes a topic and outputs publish-ready content.
- →AI Orchestration
Coordinating multiple AI models, tools and steps into a single reliable workflow.
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.
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.
- →Build An AI Trading Assistant
An assistant that drafts and attacks its own trade plans raised plan completeness to 100% and rejected a fifth of setups on liquidity grounds.
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.