Byte-Sized n8n AI Series: Multi-Agent Orchestrator

Description:

One brain is good; a team of specialists is better. As AI tasks grow in complexity, a single "generalist" agent often fails. The solution is Orchestration.

In this final 90-minute session, we explore the cutting edge of agentic design: the Model Context Protocol (MCP). You will learn how to standardize agent "skills" so they can be shared across your entire stack. We will dive into Modular Design, teaching you how to build "Sub-workflows" where a lead agent acts as a manager, delegating tasks to specialized "Sub-agents" (e.g., a Researcher, a Writer, and a Fact-Checker). Finally, we cover Production Hardening—ensuring your system is resilient, cost-effective, and includes "Human-in-the-Loop" checkpoints for safety.

Duration:

90 minutes

Course Code: BDT 537

Learning Objectives:

After this course, you will be able to:

1. Implement the Model Context Protocol (MCP) to standardize tool sets
2. Architect a "Manager-Worker" multi-agent system using n8n Sub-workflows

Deploy production-grade error handling and human-approval loops

Advanced automation developers and AI architects who want to scale their AI systems beyond single-task bots into a collaborative ecosystem of specialized agents.

Proficiency in n8n workflow design and a solid understanding of LLM "Tools." Familiarity with the concept of an API is essential

Course Outline:

1. MCP Mastery: The New Standard for Skills
   1. What is MCP? Understanding the Model Context Protocol and why it’s the "USB-C for AI Tools."
   2. Universal Skillsets: Turning local scripts and external APIs into standardized MCP servers
   3. Connect n8n with MCP: How to give any agent instant access to a library of pre-built capabilities
   4. Lab: : Connecting a local MCP server to an n8n Agent node to perform a system-level task

2. Modular Design: Building the Agent Team
   1. The “Manager” Pattern: Using a high-reasoning model (e.g., GPT-4o or Gemini) to orchestrate smaller, faster models
   2. Executing Sub-workflows: How to call an independent n8n workflow as a "Tool."
   3. Inter-Agent Communication: Passing state and context between a "Writer Agent" and a "Fact-Checker Agent."
   4. Lab: Creating a "Reviewer Loop" where a second agent critiques and improves the first agent's output

3. Production Hardening: Reliability & Oversight
   1. Human-in-the-Loop (HITL): Using "Wait" nodes and "Wait for Webhook" to pause for manual approval
   2. Error Handling & Retries: Building "Self-Healing" workflows that retry LLM failure or API timeouts
   3. Observability: Monitoring token usage, costs, and execution latency within n8n
   4. Lab: Adding a Slack/Email approval step to an autonomous publishing pipeline

Training material provided: Yes (Digital format)

Hands-on Lab: Students will be provided with docker compose file and n8n workflow JSON.