Foundations of AI Automation for Accounting Agents

Course Title:

Foundations of AI Automation for Accounting Agents

Level: Beginner / No Technical Background
Goal: Teach non-technical learners the digital, automation, and AI concepts needed to build agents confidently in n8n.
Outcome: Students understand automation architecture, APIs, JavaScript logic, AI models, and ethical use of tools.

Module 1: Automation Basics & Ecosystem

Objective: Understand how automation works and where AI fits.
Topics:

• What is automation

• No-code vs low-code vs AI automation

• Core components: Trigger → Process → Output

• Workflow tools (n8n, Zapier, Make, LangFlow)

• Automation in accounting

• Activity: Demo – “From Manual Invoice to Automated Email.”
  Outcome: Learners can describe automation and identify its steps.

Module 2: APIs & Webhooks Explained

Objective: Understand how systems exchange data.
Topics:

• What is an API and endpoint

• API keys and secure usage

• What is a webhook (trigger events)

• Request ↔ Response cycle

• JSON data format

• Testing with Postman/webhook.site

• Hands-On: Send a sample POST request.
  Outcome: Learners can identify API structures and trigger workflows.

Module 3: JavaScript Logic for Automation

Objective: Learn coding basics for n8n expressions.
Topics:

• Variables, If/Else, loops

• Arrays, objects, JSON data

• Math/date functions

• Using expressions in n8n ({{$json.amount * 0.18}})

• Project: Create a GST calculator script.
  Outcome: Learners can read and modify short code snippets.

Module 4: Large Language Models (LLMs) & Generative AI

Objective: Understand how AI models operate.
Topics:

• What is an LLM and text prediction

• Tokens, temperature, and max length

• OpenAI pricing basics

• Function calling and JSON schema

• Context windows and model limits

• Activity: Explore OpenAI Playground.
  Outcome: Learners can explain LLM behavior and cost logic.

Module 5: Prompt Engineering & Context Design

Objective: Learn to communicate precisely with AI.
Topics:

• What is a prompt and why structure matters

• Role/task/instruction-based prompts

• Few-shot examples and JSON formatting

• Context injection and guardrails

• Prompt chaining for multi-step tasks

• Exercise: Extract vendor_name and amount as JSON.
  Outcome: Learners can design structured prompts for automation.

Module 6: Embeddings, Vector Databases & RAG Concepts

Objective: Grasp AI’s knowledge retrieval logic.
Topics:

• What are embeddings

• Vector databases (Pinecone, FAISS, Chroma)

• Semantic search concepts

• RAG overview and use cases
  Outcome: Learners understand semantic search and retrieval mechanisms.

Module 7: Machine Connector Protocol (MCP) Overview

Objective: Learn how agents communicate safely.
Topics:

• What is MCP

• Difference from APIs/webhooks

• Role in multi-agent ecosystems
  Outcome: Awareness of MCP as a standard protocol.

Module 8: AI Ethics & Data Privacy

Objective: Practice responsible AI usage.
Topics:

• Data privacy and anonymization

• OpenAI use policies

• Avoiding bias and unsafe automation

• Confidentiality in finance workflows
  Outcome: Learners commit to ethical and secure AI practices.

Module 9: Navigating n8n (Interface Tour)

Objective: Understand n8n visually.
Topics:

• Nodes, connections, and executions

• Viewing data and logs

• JSON preview panel
  Outcome: Learners can navigate and debug inside n8n.

Module 10: Automation Glossary Mini-Lesson

Objective: Clarify automation and AI terms.
Topics:

• Key terms explained visually (API, Trigger, JSON, Node, Agent, MCP)

• Based on Automation & AI Glossary (PDF handout)
  Outcome: Learners clearly understand technical terms in simple language.