How to Build a Multi-Agent System From Scratch

One agent does one thing. Multiple specialist agents coordinated by an orchestrator can do anything. Here is the architecture, the tools and the exact Make.com implementation.

A multi-agent system is what happens when you stop trying to make one agent do everything and instead give each agent one specific job it does exceptionally well. A research agent that only researches. A writing agent that only writes. A review agent that only checks quality. An orchestrator that coordinates all three.

When One Agent Is Not Enough

Build a multi-agent system when: the task has genuinely distinct subtasks requiring different expertise, quality benefits from one agent reviewing another's work, subtasks can run in parallel for speed, or total context exceeds what fits in a single model call.

Do not build multi-agent systems for simple linear tasks. The coordination overhead is not worth it for anything a 3-prompt chain can handle. Multi-agent systems are for complex, high-stakes workflows where quality justifies the architecture.

The Content Production System — Complete Blueprint

Orchestrator: Main Make.com scenario. Receives content brief via Typeform webhook. Calls each specialist in sequence and passes outputs between them.

Research Agent: Separate Make.com scenario triggered via webhook from orchestrator. Uses Serper API + Perplexity API to gather current facts. Returns structured JSON research brief.

Writing Agent: Receives research JSON from orchestrator. Claude 3.5 Sonnet with detailed brand voice instructions. Returns first draft with section headings and word count.

Review Agent: Receives draft from orchestrator. Checks against: brand voice guide, SEO requirements, factual accuracy against research brief. Returns reviewed version with specific change notes.

Publishing Agent: Receives final content. WordPress REST API creates draft post. DALL-E generates featured image. Buffer schedules social promotion. Google Sheet logs the full pipeline output.

Preventing Error Propagation

The most dangerous aspect of multi-agent systems is error propagation — a hallucination in the research agent becomes a confident false claim in the published article. Add a quality check after each agent call: "Is this output complete, relevant and appropriate? Yes or No." Route No outputs to human review before proceeding. Log every agent output to a Google Sheet for audit trail and debugging.