I Built J.A.R.V.I.S. from Iron Man with AI (No-Code Tutorial)

Key Concepts

• Orchestration Agent: The main AI agent responsible for routing user queries to the appropriate sub-agent.
• Sub-Agents: Specialized AI agents (e.g., Email Agent, Calendar Agent, Personal Expense Agent) designed to handle specific tasks.
• Tools: Specific functionalities or integrations (e.g., Send Email, Create Event, Vector Database) that sub-agents use to perform their tasks.
• Prompting: The process of providing instructions and context to AI agents to guide their behavior and responses.
• LLM Chain: A sequence of language model operations used to generate text, often for defining the personality or style of an AI assistant.
• N8N: A workflow automation platform used to build and orchestrate the AI agents and their interactions.

Jarvis 3.0: A Personal AI Assistant

The video details the creation of Jarvis 3.0, a personal AI assistant built using N8N. This version improves upon previous iterations (Jarvis 1.0 and 2.0) by leveraging N8N's AI Agent tool for a cleaner and more efficient architecture.

Architecture Overview

Jarvis 3.0 uses a modular design with an orchestration agent and several sub-agents. The orchestration agent acts as the central hub, receiving user queries and delegating them to the appropriate sub-agent based on the query's intent.

1. Telegram Trigger: The workflow is initiated by a Telegram trigger, which captures user input in the form of text or voice messages.
2. Voice Message Handling: If the input is a voice message, it's downloaded, transcribed using OpenAI's transcriber, and then passed to the orchestration agent.
3. Text Message Handling: If the input is text, it's directly passed to the orchestration agent.
4. Orchestration Agent: This agent uses an Anthropic Claude Sonnet model (or similar like GPT-4 or GPT-5) to understand the user's query and determine which sub-agent is best suited to handle it. It also has a calculator tool for mathematical operations.
5. Sub-Agents: Each sub-agent (e.g., Email Agent, Calendar Agent, Personal Expense Agent) is responsible for a specific set of tasks and has access to relevant tools.
6. Output: The orchestration agent receives the results from the sub-agent and generates a response, which is then sent back to the user via Telegram.

Orchestration Agent Details

• Role: Efficiently delegate user queries to the appropriate tool.
• Tools: Email Agent, Calendar Agent, Calculator, Company Knowledge, Personal Expense Agent.
• Prompting: The system prompt is kept simple to reduce hallucinations. It defines the agent's role and provides instructions on when to use each available tool. For example, the prompt specifies that the Email Agent should be used for all email-related tasks.

Sub-Agent Details (Email Agent Example)

• Role: Manage users' emails professionally using the available tools.
• Tools: Send Email, Reply Email, Labels, Create Draft, Get Emails.
• Prompting: The prompt instructs the agent on when to use each tool. For example, it specifies that the "Send Email" tool should be used to compose and send emails. It also includes instructions on how to sign off emails (e.g., "sign off as Zubair").
• Tool Configuration: Each tool within the sub-agent is configured to be defined automatically by the model, leveraging N8N's capabilities.

Sub-Agent Details (Calendar Agent Example)

• Role: Manage users' calendar by creating, retrieving, updating, deleting events using the tools available.
• Tools: Create Event, Get Event, Delete Event, Update Event.
• Prompting: The prompt instructs the agent on when to use each tool. For example, it specifies that the "Delete Event" tool should be used to delete an event, and to get the event ID from the "Get Events" tool first.

Personal Expense Agent Example

This agent handles queries related to personal expenses, such as credit card payments and expense history. It uses a vector database and a CRM to store and retrieve expense information.

Output and Voice Generation

The orchestration agent's output is sent to Telegram as a text message. Additionally, a basic LLM chain is used to generate a quick-witted response in the style of Jarvis from Iron Man. This response is then converted to speech using 11 Labs, using a voice clone of Jarvis. The resulting audio file is sent to the user via Telegram.

• LLM Chain: Defines the personality of Jarvis (sophisticated, quick-witted, British accent).
• 11 Labs: Converts the text response to speech using a cloned voice.

Key Arguments and Perspectives

• Modular Design: The use of sub-agents allows for a more organized and maintainable system.
• Simple Prompting: Keeping prompts simple reduces hallucinations and improves the accuracy of the AI agents.
• Layered Architecture: The orchestration agent delegates tasks to sub-agents, which in turn use specific tools to perform those tasks.

Data and Statistics

• In Q4 2024, the user spent $3,500 on marketing across YouTube, Facebook, and LinkedIn campaigns.

Step-by-Step Processes

1. Receive User Input: Telegram trigger captures text or voice message.
2. Process Voice Message (if applicable): Download, transcribe using OpenAI.
3. Route to Orchestration Agent: Send text to the orchestration agent.
4. Delegate to Sub-Agent: Orchestration agent determines the appropriate sub-agent based on the query.
5. Sub-Agent Executes Task: Sub-agent uses its tools to perform the task and retrieve information.
6. Return Results to Orchestration Agent: Sub-agent sends the results back to the orchestration agent.
7. Generate Response: Orchestration agent generates a response based on the results.
8. Send Text Message to Telegram: Send the response as a text message.
9. Generate Voice Response (optional): Use LLM chain to generate a quick-witted response, convert to speech using 11 Labs.
10. Send Voice Message to Telegram: Send the audio file to the user.

Monetization Opportunities

The video suggests that Jarvis 3.0 or similar AI assistants can be sold to businesses or individuals. The "Earn with N8N" program provides training on how to monetize AI skills and build an AI agency.

Conclusion

Jarvis 3.0 represents a significant improvement over previous versions, thanks to N8N's AI Agent tool and a well-designed modular architecture. The use of sub-agents, simple prompting, and a layered approach allows for a flexible and efficient personal AI assistant. The video provides a detailed overview of the system's architecture, prompting strategies, and potential monetization opportunities. The blueprint for Jarvis 3.0 is available for download, allowing users to customize and adapt it to their own needs.