AI-Powered Research & Content Engine
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AI-Powered Research & Content Engine

Multi-agent automation from research to publication

Multi-agent automation for research, fact-checking, writing, and multi-channel content delivery.

THE PROBLEM

Researching topical AI automation trends, verifying sources, and generating consistent deliverables across channels (blog, email, LinkedIn, newsletter) was manual, inconsistent, and time-intensive. Without structured verification, content risked unverified claims or inaccuracies.

THE SOLUTION

  • Built a multi-agent AI system using n8n with a supervisor agent orchestrating specialist agents
  • Each step outputs structured JSON contracts for governable transitions between agents
  • Agents handle: research, fact-checking, writing, repurposing, and distribution

AGENT ARCHITECTURE

Orchestrator

Manager
  • Reads input (topic, audience, tone, timeframe)
  • Calls specialist agents in sequence
  • Prevents hallucinations by enforcing tool usage
  • Outputs final structured deliverables

Research Scout

Research Agent
  • Finds reputable sources (OpenAI, Anthropic, n8n, industry blogs)
  • Extracts claims with excerpts
  • Tags recency and risk

Fact Checker

Verification Agent
  • Confirms accuracy against sources
  • Removes or flags weak claims
  • Ensures evidence for every fact

Writer

Content Agent
  • Generates skimmable, audience-tailored articles
  • Uses only verified claims
  • Outputs blog draft + TL;DR bullets

Repurposer

Editor Agent
  • Polishes tone and structure
  • Transforms draft into: blog markdown, LinkedIn post, newsletter snippet
  • Generates source table markdown

WORKFLOW FLOW

TriggerOrchestra...ResearchFact CheckWriterRepurposeDistribute

IMPLEMENTATION STEPS

  1. 1.Input parameters (topic, audience, tone, timeframe)
  2. 2.Orchestrator AI assigns tasks
  3. 3.Research agent gathers sources and extracts claims
  4. 4.Fact-checker validates information
  5. 5.Writer composes content based on verified data
  6. 6.Repurposer formats outputs for multiple channels
  7. 7.Automated distribution to Email/Slack/Notion

IMPACT & RESULTS

  • Reduced research-to-publish time from ~3 hours to ~20–30 minutes
  • Standardized content structure across all outputs
  • Improved reliability through fact-checking and source verification
  • Scalable system for future content expansion
  • Created a reusable AI content operations system