Building an AI Agent Swarm in n8n Just Got So Easy

How to Automate Tasks with NADN Agent Swarms

Introduction to NADN Agent Swarms

• The speaker introduces the concept of automating tasks using NADN agent swarms, emphasizing that no prior coding experience is required.

• A promise is made to provide live examples, explain how it works, and offer resources for viewers to set up their own systems for free.

Demonstration of Task Automation

• The speaker sends a message via Telegram instructing an agent to find high-performing YouTube videos about N8N and check the weather in Chicago.

• The main orchestrator agent decides which specialized agents to call based on the tasks requested, showcasing the efficiency of having multiple specialized agents.

Execution and Results

• After executing the tasks, the system logs all outputs and responds back through Telegram with results including video details and weather information.

• The response includes confirmation of actions taken: four YouTube videos found and today's weather in Chicago reported as overcast clouds at 81°F.

Workflow Insights

• A Google Sheet is used to log detailed information about each action taken by different agents during execution, providing transparency into the process.

• Each step shows which specific agent was called (e.g., contact agent for email retrieval, web agent for weather), along with token usage statistics.

Advantages of Using Agent Swarms

• The speaker discusses improvements from previous versions where agents were separated into sub-workflows; now they can operate within a single workflow for better visibility.

• Each agent operates independently but cohesively due to shared memory capabilities, allowing them to focus on specific tasks without needing full context.

Flexibility in Agent Functionality

• Different chat models can be assigned per task; cheaper models may be used for simple tasks while more advanced models are reserved for complex ones.

Understanding Agent Memory and Workflow

The Role of Session IDs in Agent Memory

• Each agent can add memory, which is stored based on a session ID, similar to how contacts store different conversations in a phone.

• Agents may need to be linked through a session ID for memory when they are working collaboratively; however, this isn't necessary if agents are performing isolated tasks.

• In the current workflow setup, passing variables like session IDs between agents is simplified as they can reference shared memory directly without separate variable transfers.

Demonstration of Agent Functionality

• A complex task example is presented where an agent retrieves YouTube video ideas, conducts research with another agent (Tavi), sends an email to Michael Scott, and schedules a calendar event.

• The agent successfully calls multiple sub-agents: YouTube for ideas, contact for information on Michael Scott, and web for research purposes.

• Despite minor transcription errors (e.g., using Perplexity instead of Tavi), the agent adapts and completes the task effectively by sending an email with researched video ideas.

Task Completion and Logging

• The sent email includes three YouTube video ideas along with insights from the research conducted by the agent.

• A calendar event titled "Review Research Brief on YouTube Video Ideas" is created at 4 PM with Michael Scott added as an attendee.

• All actions taken by the agents are logged in a Google Sheet detailing inputs, outputs, tools used (five agents), and token costs associated with the run.

Building Your Own Executive Agent Swarm

Initial Setup of AI Agents

• The process begins on a blank canvas where an AI agent is introduced as part of building an executive agent swarm.

• Users are guided to connect a chat model that serves as the brain for their AI agent via OpenRouter.ai; creating an API key is essential for access.

Adding Functionalities to Agents

• After setting up billing information in OpenRouter.ai, users can select from various chat models available to enhance their AI capabilities.

How to Build an Email Agent with a Main Agent

Setting Up the Email Agent

• The main agent is responsible for managing sub-agents, such as the email agent, which is designated to handle email tasks.

• The user message parameter allows the main agent to instruct the email agent on its actions, similar to how it communicates with other sub-agents like the YouTube and contact agents.

Connecting Tools and Testing Functionality

• To enable functionality, a Gmail tool must be connected to the email agent. A tutorial link is provided for those unfamiliar with connecting Google tools.

• The AI will determine recipient details, subject lines, and body messages automatically; options are available to customize these settings further.

Troubleshooting Errors

• Initially, no system prompts were included in either agent. This simplicity demonstrates ease of building agents but may lead to errors in execution.

• An error occurred when attempting to send an email due to an invalid address format; this was traced back to how the main agent processed input.

Refining System Prompts

• To resolve issues with invalid email addresses, a system prompt can be added instructing that valid formats are required for sending emails.

• Alternatively, descriptions can be added directly within tool fields for clearer guidance on input requirements.

Understanding Agent Interactions

• The main issue was identified as being related not just to the email agent but also how the main agent interpreted commands and delegated tasks.

• By refining system prompts for both agents, clarity on their roles improved; now the main agent delegates tasks rather than attempting them itself.

Successful Execution of Tasks

• After implementing changes in system prompts and testing again, successful communication through email was achieved without further errors.

Agent Swarms: Building and Debugging

Understanding Agent Communication

• The email agent processes parameters to create an email, including recipient, subject, and body, before communicating back to the main agent.

Key Components for Building Agent Swarms

1. Mastering Agent Logs

• Reading agent logs is crucial for debugging; it helps identify where errors occur in the command chain.

• Experience with logs enhances understanding of data flow between agents, improving system reliability.

2. Effective Prompting Techniques

• As systems scale with multiple agents and tools, prompting becomes more complex; reactive prompting is essential.

• A structured approach to adding tools one at a time prevents overwhelming the system and aids in pinpointing issues.

3. Tool Calling Mechanism

• AI models determine parameters for tools like sending emails or creating calendar events based on user input.

Implementing Additional Tools

Adding Calendar Functionality

• New tools can be integrated by allowing the main agent to decide how they interact with other agents (e.g., calendar agent).

Debugging Event Creation Issues

• When an event appears incorrectly scheduled (e.g., a year in the past), it's necessary to investigate tool logs for discrepancies.

Analyzing Errors in Real-Time

Identifying Date Misunderstandings

• The calendar event was created incorrectly due to the agent's lack of awareness of the current date.

Problem-Solving Approach

• To resolve issues effectively, consider how a human would manually address similar problems; this perspective aids in troubleshooting.

Fixing Date Parameters

Creating an AI Agent Swarm

Setting Up the Calendar Agent

• The speaker demonstrates how to set up a calendar agent by copying a sentence that includes today's date and saving it as an expression.

• The agent determines which tool to use, leading to the creation of an event in Google Calendar without a title.

• To resolve the missing title issue, the speaker adds a summary field for the event title, allowing the AI agent to generate it automatically.

Building and Scaling Agents

• A mini-agent swarm is created with a main orchestrator, email agent, and calendar agent. The next step involves scaling these agents by integrating various email and calendar tools.

• Users can leverage pre-built system prompts from existing agents to enhance their own workflows easily.

Understanding AI Functions

• The speaker explains that "from AI" functions are similar to previously established setups where users can let models define parameters.

• Recent updates have simplified using these functions, making them more accessible for users.

Distinguishing Between Agents and Workflows

• While discussing automation, the speaker emphasizes that not all processes require agents; many can be effectively managed through structured workflows.

• Workflows are deterministic and follow strict sequences (1, 2, 3...), while agents handle unpredictable processes requiring decision-making assistance from AI.

Accessing Resources for Implementation

• Viewers are directed to access resources via a free school community link provided in the description for duplicating systems discussed in the video.

• A JSON file will be available for download after locating the relevant post within this community resource hub.

Setting Up Your First Agent Swarm

Introduction to Agent Swarm Setup

• The video discusses how to set up your first agent swarm, emphasizing that technical expertise is not a prerequisite for participation.

• Viewers are encouraged to seek free tech support while learning about agent swarms, with an invitation to join a paid community for deeper engagement.

Community and Support Resources

• The speaker highlights the benefits of joining their community, which includes access to members who actively build with NADN daily.

• Members can post questions when they encounter challenges, receiving assistance from both peers and official support specialists.

Educational Offerings

• The community features a classroom section containing two comprehensive courses: "Agent Zero," aimed at beginners in AI automation, and "10 Hours to 10 Seconds," focusing on designing time-saving automations.

Conclusion and Call to Action