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 Duration 14 hours

Course Outline

Day 1: Workflows and the AI step

    Introduction

    • When to automate, when to use an AI step and when to use an agent
    • n8n in context: cloud and self-hosted, and what each means for company data

    n8n essentials

    • Workflows, nodes, triggers and executions
    • Moving data between nodes by dragging fields, without typing syntax
    • Reading an execution to find and fix a problem

    Connecting apps and data

    • Webhooks and form triggers
    • Spreadsheets, email and calendar nodes
    • Credentials and authentication for external services
    • The HTTP Request node, step by step

    The AI step

    • Adding a model to a workflow
    • Writing prompts for reliable output
    • Structured output and automatic correction of malformed answers
    • Classifying and extracting information from emails, forms and documents

    Decisions and actions

    • Branching with If and Switch
    • Approval steps: asking a person before an action is taken
    • Writing results to a spreadsheet and sending notifications

    Hands-on lab: first end-to-end workflow

    • From an incoming request to a classified, recorded and routed result

    Day 2: Agents and operations

      Agents

      • What an agent is: model, memory and tools
      • Fixed workflow or agent: how to choose
      • Giving an agent access to company documents and spreadsheets

      Agents that act

      • Tools that read and tools that write
      • System prompt rules: required fields, no invented data, no duplicate records
      • Testing an agent by trying to break it

      Channels

      • Chat interface for internal use
      • Connecting the agent to one messaging channel

      Reliability

      • Error workflows, retries and notifications
      • Logs and execution history
      • Controlling model and execution costs

      Data and privacy

      • What leaves the company when a model is called
      • Self-hosting and local models: instructor demonstration
      • A checklist to review with the IT or legal team

      Building workflows with AI

      • Asking an AI assistant to draft a workflow
      • Reviewing it: what each node does, where the data comes from and what happens when it is empty

      Final project and next steps

      • Each participant completes and presents a workflow with an AI step and an agent on their own process
      • Maintenance plan and next candidates for automation

      Deliverable: a working workflow with an AI step, an agent connected to company data, error handling and a review checklist.

      Requirements

      • No programming experience required
      • Comfort using web applications and spreadsheets
      • A clear picture of one repetitive process in their own work

      Audience

      • Business, operations and customer-facing professionals
      • Team leads and process owners
      • Non-developers who want to build automations and AI agents on company data

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