AI Workflow Basics

Introduction

An AI workflow is a directed graph of steps — some LLM, some tool, some classic code. Frameworks (LangGraph, Inngest, Temporal, Vercel AI SDK) help you compose, persist and observe workflows.

Beginner analogy: a factory assembly line — each station does one thing, products flow through. Workflows are the same for tokens and tool calls.

Understanding the topic

Core concepts:

• Nodes = steps (LLM, tool, code, human).
• Edges = conditional transitions.
• Workflows are durable: pause, resume, retry.
• Use code for things you can express as code.
• Use LLMs only where reasoning is needed.

Syntax reference

Visual workflow / architecture:

 start ─► classify ─► route ─┬─► coding
                                 ├─► support
                                 └─► escalate ─► human

Real-world use

Zapier AI Agents, n8n, Make, LangGraph and Inngest power most production AI workflows.

Best practices

• Draw the graph before writing code.
• Add observability per node.

Common mistakes

• Putting business logic inside LLM prompts that should be code.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Why use a workflow framework?
• Q2. When use code vs LLM for a step?
• Q3. Scenario: which step in your workflow most needs observability?