AI Agent Creation Guide: N8N, Trinity, and Claude Code

Understand the key parameters that shape the behavior and personality of your AI agents.

Dive deeper into what makes an agent "intelligent" and how different types of agents function.

Learn the art of prompt engineering to guide your AI's responses with precision.

Explore how memory transforms agents into context-aware assistants.

Integrate your AI agent with messaging platforms to make it conversational and accessible.

Automate scheduling tasks by linking your AI agent to Google Calendar.

Enable your agent to read, write, and send emails using Gmail integration.

Customize your agent’s logic, tone, and task-handling to align with your goals.

Expand your agent’s knowledge base with real-time search capabilities via Perplexity AI.

Master automated email workflows for alerts, summaries, and more.

Implement intermediary steps in chats for a smoother user experience.

Create and manage tasks via your AI agent using Google Task integration.

Build modular workflows to support multi-step and multi-purpose automations.

Explore advanced strategies and tools to elevate your agents' intelligence and usability.

Learn how to use the Code node for custom logic and advanced behavior in N8N.

Enable your agents to handle images, audio, and text for richer interaction.

Understand the Master Control Program (MCP) approach to orchestrating multiple agents.

Introduction to the section.

Discover the core concept of memory in AI agents and why it’s critical for creating more human-like, adaptive, and context-aware systems.

Dive deeper into advanced memory architectures.

Uncover strategies for designing and implementing robust memory systems in AI agents.

In this video, we will discuss the concept of 'context rot' in large language models (LLMs). Instructor explains how LLMs like GPT-4, Claude, and Gemini do not perform uniformly across their entire context window, leading to degradation in performance as the context increases.

Get a clear overview of what this part of the course covers and why autonomous agents are the next step after workflow-based automation.

Understand the limitations of tools like N8N when building agents that need memory, independence, and stateful long-running behavior.

Learn how modern agents operate inside folders, manage files, use command-line tools, and maintain their own working context.

Explore the key components that make Claude Code-style agents useful, including skills, MCP servers, APIs, local commands, and memory.

Watch a practical demo of a local Claude Code agent and see how it uses skills, tools, and terminal-based interaction to perform tasks.

Learn why running agents on your personal computer creates limits around availability, security, scalability, and continuous execution.

Discover how treating each agent as a GitHub repository enables safer development, rollback, branching, collaboration, and long-term maintainability.

Learn how Trinity provides the infrastructure layer for running stateful autonomous agents in secure cloud environments.

See how Trinity runs multiple agents in isolated containers while allowing them to work together as part of a larger system.

Understand how Trinity MCP acts as the central communication layer between agents, users, logs, tasks, and external tools.

Explore the platform features needed for real-world deployments, including sandboxing, guardrails, scheduling, audit trails, and credential management.

Get familiar with the Trinity dashboard, agent containers, GitHub repositories, execution timeline, logs, and the overall platform structure.

Learn how to use the Abilities Marketplace, Trinity Connect, and Trinity Onboard to create and deploy a new agent to the platform.

Discover how to interact with agents through tasks, persistent chats, Claude-style sessions, playbooks, schedules, dashboards, and sync workflows.