About this Video

• Video Title: I Built an Entire Marketing Team With One AI Agent (no code with n8n)
• Channel: Riley Brown
• Speakers: Riley Brown, Muhammad
• Duration: 30:57

Introduction

This video demonstrates how the creators built an AI-powered marketing team using a no-code tool called n8n. The system uses a "Chief Marketing Officer" (CMO) agent that delegates tasks to other specialized AI agents for research, content creation (including image and video generation), and social media posting. The goal is to showcase the potential of AI to automate marketing tasks and the process of building such a system.

Key Takeaways

• A no-code platform (n8n) was used to create a network of AI agents to automate various marketing tasks.
• A central "Chief Marketing Officer" (CMO) agent orchestrates the workflow, delegating tasks to specialized agents.
• Individual agents handle specific functions: research, social media posting, content (image and video) generation.
• The system integrates with various APIs (including Amplify's 4000+ APIs) and tools like Buffer and Slack for seamless workflow.
• The creators highlight the potential for AI to automate jobs and improve productivity.

The video demonstrates several automated workflows, all orchestrated by a central "Chief Marketing Officer" (CMO) AI agent. Here's a breakdown:

Workflow 1: Social Media Post Creation and Scheduling:

1. User Input: The user provides a request (e.g., "Do some research on Google's new CLI tool, create three Buffer posts about it; for the third, generate an image of someone using the tool").
2. CMO Agent: This agent analyzes the request and determines which specialized agents are needed. In this case, it uses the Research Agent and the Poster Agent.
3. Research Agent: Gathers information from the internet using various APIs (including Amplify's extensive API collection) about Google's CLI tool. It also gathers relevant tweets.
4. Poster Agent: Takes the research data and creates three social media posts for Buffer. For one post, it generates an image using Stable Diffusion (or a similar image generation tool).
5. Buffer Integration: The posts are automatically scheduled in Buffer.

Workflow 2: Scheduling and Communication:

1. User Input (via VAP): The user, through a custom app (VAP), requests a calendar appointment and a Slack message (e.g., "Schedule an appointment tomorrow at 9:00 a.m. to film short-form content with Muhammad and send him a Slack reminder").
2. CMO Agent: Directs the request to the "Helper 2.0" agent.
3. Helper 2.0 Agent: This agent interacts with the Calendar Agent and Slack Agent.
4. Calendar Agent: Schedules the appointment in Google Calendar, including the invitee.
5. Slack Agent: Sends a message to the specified person on Slack.

Workflow 3: Research and Communication:

1. User Input (via VAP): The user requests information and communication (e.g., "Search the internet and find the latest news regarding Cluey and their recent fundraise. Then message Muhammad and let him know what you found").
2. CMO Agent: Delegates to the Research Agent and Helper 2.0 Agent.
3. Research Agent: Uses APIs (including SERP API and Appify MCP) to find information on Cluey's fundraising.
4. Helper 2.0 Agent: Sends a Slack message to Muhammad with the research findings.

Workflow 4: Image and Video Generation, Social Media Posting:

1. User Input (via VAP): The user requests image and video generation, followed by social media posting (e.g., "Create an image of a man on a unicycle with an orange shirt that says 'Download Vibe Code,' turn it into a video, post to Buffer, and message Muhammad when done").
2. CMO Agent: Delegates to the Media Agent and Poster Agent, utilizing Helper 2.0 for the final Slack message.
3. Media Agent: Generates the image and converts it into a video using FAL workflows.
4. Poster Agent: Posts the video to Buffer.
5. Helper 2.0 Agent: Sends a Slack message to Muhammad confirming completion.

Each workflow shows how the CMO agent acts as a central control point, directing tasks between specialized AI agents. The system's strength lies in its ability to chain together different AI capabilities and integrate with external services, creating complex, multi-step automated processes.