In this hands-on course, learn how to design and deploy a context-aware multiagent system using LLMs and Anthropic's MCP. Instructor Lillian Pierson shows you how to build a working system that leverages the power of GPT-4o, Airtable, and n8n to automate structured tasks with persistent context and modular agents. Over the span of just two hours, you'll create two interoperable agents: one for generating content and another for quality assurance—both orchestrated through an MCP-aligned framework. Along the way, learn how to format context layers, structure task prompts, and evaluate outputs to ensure accuracy and brand consistency. Whether you're exploring AI automation, intelligent workflows, or scalable LLM systems, this course equips you with a powerful foundation of business-critical in-demand skills.

Learning objectives

• Describe the core components of Anthropic's MCP and explain how it enables modular, context-aware LLM-based systems.
  
• Design a persistent context layer using tools like Airtable or Google Drive to support context-aware task execution by LLM agents.
  
• Build and orchestrate a dual-agent system using GPT-4o and n8n, including a content generation agent and an evaluation agent.
  
• Implement structured task flows that align with MCP standards, including prompt formatting, task parameterization, and agent chaining.
  
• Evaluate and expand your multiagent system, adding performance feedback loops and planning for future extensibility.
  

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