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Mastering Autonomous AI: Building Intelligent Financial Agents with n8n

Create intelligent systems that think, act, and deliver real-time insights for your business.

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Overview

This course offers a hands-on introduction to agentic AI using the low-code n8n platform, making advanced AI concepts accessible to non-technical professionals. Framed within the context of financial analysis, participants will learn to build autonomous agents equipped with conversational memory, Retrieval-Augmented Generation (RAG), and live API tools—without writing extensive code. Through practical modules and a capstone project, participants gain the skills to design, build, and deploy AI agents capable of complex reasoning, task execution, and real-time insight generation—skills that can be applied across any industry.

Course Syllabus

  • The Paradigm Shift: From Automation to Autonomous Agents
  • Anatomy of an AI Agent: The Core Components (LLM, Memory, Tools, Planning)
  • Why n8n for AI Engineering: A Visual-First, Logic-Capable Platform
  • Tour of the n8n AI Development Environment: Canvas, Workflows, and Credentials
  • Core n8n Concepts: Nodes, JSON Data Flow, and Executions
  • Final Showcase Preview: A Glimpse of the Capstone Agent We Will Build
  • Setting Up the Communication Channel: Creating and Connecting a Telegram Bot
  • Interfacing with the Agent’s Brain: Using the OpenAI/LLM Node
  • Crafting Effective Prompts: System vs. User Messages for Agent Control
  • Building a “Smart Responder” Bot Telegram
  • Visualizing and Manipulating Data: Understanding JSON, the Set Node, and the IF Node
  • Managing API Keys and Credentials Securely for AI Services
  • The Importance of Memory: Short-Term vs. Long-Term Recall
  • Setting Up a Database for Memory: Using Supabase (PostgreSQL)
  • Building the Memory Mechanism: Writing to and Querying Messages via HTTP Nodes
  • Injecting Context: Modifying Prompts to Include Conversation History
  • Upgrading Your Bot to Remember User Interactions Across Messages
  • Beyond Basic Memory: Introduction to Retrieval-Augmented Generation (RAG)
  • Setting Up a Vector Database as the Agent’s Long-Term Memory
  • The Data Ingestion Workflow: Converting Documents into Vector Embeddings
  • Building a Reusable “Knowledge Base Search” Tool as a Sub-Workflow
  • Combining Retrieved Knowledge into LLM Prompts for Context-Rich Answers
  • Building a Q&A Agent that Answers Questions Based on Internal Knowledge
  • The Core of Agentic Action: Delegating Tasks to Specialized Tools
  • Mastering the Execute Workflow Node for Modular Design
  • Passing Data to and Receiving Structured JSON Results from Sub-Workflows
  • Connecting to Live Data: Introduction to the HTTP Request Node
  • Connecting to the Sectors App API and Authenticating
  • Creating a Reusable “Get Market Data” Tool with the Sectors App API
  • Best Practices for Creating Modular, API-driven Agent Tools
  • Introduction to Agentic Frameworks: The Reason and Act (ReAct) Model
  • Using a “Main Agent” LLM to Decompose Tasks and Select the Correct Tool
  • Orchestrating Logic: Using the Switch Node to Route to the Correct Tool Sub-Workflow
  • Designing the Loop: How an Agent Executes a Plan, Reflects, and Continues Step-by-Step
  • Managing State and History Within a Single Task Execution
  • Handling “Task Complete” and “Error” Scenarios within the Loop
  • Objective: Build an agent that researches a company using both internal documents and the live Sectors.app API then emails a summary.
  • Step 1: Integrate the RAG “Knowledge Base Search” Tool.
  • Step 2: Integrate the live “Get Market Data” Tool from the Sectors.app API.
  • Step 3: Build and Integrate a new “Send Email” Tool.
  • Step 4: Construct the Main Agent Loop that uses the LLM brain to orchestrate all tools.
  • Step 5: End-to-End Testing and Live Demonstration of the Agent.
  • Activating Your Agent: Deployment via Webhooks for Real-World Use
  • Human-in-the-Loop: Building an “Approval Step” for Critical Agent Actions
  • Monitoring and Debugging: Logging Agent Decisions, Tool Executions, and Errors
  • Cost Management: Tracking LLM and Database API Usage
  • Versioning Your Agent: How to Safely Update Tools and Prompts
  • Security and Ethical Considerations for Deploying Autonomous AI Systems

Course Receivables

  • Lecturer’s Notes

    Including Course Books (PDF), HTML files, course transcripts (if any).

  • Highly-accelerated Learning

    Learn under the guidance of our expert instructor, who will support you throughout the course with hands-on expertise and real-world insights.

  • Certification of Completion

    Show current employer hat you’ve completed the course with a signed certificate of completion.

  • Quality Learning Environment

    We pay meticulous attention to the logistics of our online courses: high-quality audio and visual delivery, clear and seamless screen-sharing setups, and thoughtfully paced modules for an engaging, interactive learning experience.

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ABOUT THIS SERIES

Courses in this series cater to a diverse audience: from casual learners and working professionals to those venturing into data science and machine learning without a programming background.

We recognize that many students may not have prior expertise in statistics, mathematics, or algebra. Therefore, our courses are designed with a gentle learning curve, placing a strong emphasis on hands-on experience and individualized instruction. Our dedicated team of instructors and teaching assistants ensure personalized guidance every step of the way.

Teaching Methodology

Students work through tons of real-life examples using sample datasets donated by our mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.

YOUR INSTRUCTOR

Muhammad Dzaky Jayalaksono
Business Intelligence Instructor at Algoritma Data Science School

Dzaky has a strong understanding and proficiency in platforms for business analytics, enabling valuable insights for data-driven decision-making. As a detail-oriented professional, He possesses the technical thinking skills to identify innovative solutions for business problems and is deeply committed to the company’s advancement. Orchestrating the end-to-end business intelligence process from defining the business problem to conducting thorough analysis, visualizing insights, and deploying impactful dashboards. Bring a comprehensive skill set to drive the entire lifecycle of business intelligence. He is motivated to continuously learn and grow, keep collaborating, and provide exceptional abilities to drive success.

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Get In Touch

PT. Algoritma Data Indonesia.
RDTX Square, 9th Floor. Setiabudi, DKI Jakarta 12930.
Office: 0816-692-471
Email: community@algorit.ma

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