Tell an automation from an agent, name the four parts every agent is built from, and spot the tasks in your own work that each one suits.
Module 01 ~40 min read + lab No codePrerequisites: None. Comfort with email, spreadsheets and chat tools is enough.
Most office work is a chain of small decisions: read this request, decide who handles it, copy the details into the right system, send a reply, chase the follow-up. For twenty years software could only automate the parts that were perfectly regular. Anything that needed reading, judgement or writing stayed with people.
Large language models (LLMs) changed that boundary. A model can now read an email, decide it is a refund request, extract the order number, draft a polite reply and hand it to a person for approval. The parts that were "too messy to automate" are suddenly automatable, which is why the people who understand the process, not the programmers, are the ones who now decide what gets automated and how it is controlled.
You will not write code. You will map the work, design the automation or agent that does it, connect the tools it needs, and decide where a human must stay in the loop. Those four verbs are the whole course.
An automation is a fixed recipe: when X happens, do A, then B, then C. It never surprises you, because someone wrote every step in advance. A form submission that creates a spreadsheet row and sends a confirmation email is an automation.
An agent is given a goal and a set of tools, and a language model decides which steps to take, in which order, and when it is finished. "Find out why this invoice was rejected and prepare a corrected version" is an agent task: the path is not known in advance.
| Automation | Agent | |
|---|---|---|
| Who decides the steps | The designer, in advance | The model, at run time |
| Best for | Regular, high-volume, well-defined work | Variable work that needs reading, judgement or research |
| Predictability | Very high | Lower; needs limits and checks |
| Cost per run | Near zero | Model tokens on every step |
| Failure style | Stops with an error | Can keep going in the wrong direction |
| Typical tools | n8n, Make, Zapier, Power Automate | n8n AI Agent, Copilot Studio, OpenClaw, Hermes |
In practice most useful systems are workflows with AI steps: a fixed automation where one or two steps call a model to classify, summarise, extract or draft. You get most of the benefit with most of the predictability. This course teaches that middle ground first, and full agents after it.
Start with the least autonomous design that solves the problem. Add agency only where the path genuinely cannot be written down in advance.
Whatever product you use, an agent is always the same four things wired together. Learn to see them and every vendor demo becomes readable.
The language model that reads, reasons and writes: GPT, Claude, Gemini, Llama or a smaller local model. You choose it for quality, speed, cost and where your data is allowed to go.
Actions the model is allowed to take: search the web, read a calendar, look up a customer, send an email, create a ticket. No tools means the agent can only talk.
What the agent can recall: the current conversation, past sessions, a knowledge base of your documents, or "skills" it has learned. Memory is what makes an assistant feel like it knows your organisation.
The system prompt: who the agent is, what it must and must not do, the tone it uses, and when it must stop and ask a person. Instructions are your main control surface as an administrator.
Modern personal agents such as OpenClaw and Hermes Agent add two ideas on top: they run all the time as a service you message on WhatsApp or Telegram, and they can save a workflow they figured out as a reusable skill. You will see them demonstrated in Module 9. The four parts are still the same.
An agent runs a short loop, over and over, until it decides the goal is met:
This is called the ReAct pattern (reason, then act). Two consequences matter for administrators. First, every extra loop costs money and time, so agents need an iteration limit. Second, the agent only knows what its tools tell it, so tool quality is answer quality. An agent with a stale price list gives confident, wrong quotes.
A human can sit at any point of the loop: approving a tool call before it runs (for example, "send email"), reviewing the final answer, or being escalated to when the agent is unsure. Designing those points is Module 10.
| Task | Best design | Human's role |
|---|---|---|
| New-hire onboarding checklist: create accounts, send welcome pack, book induction | Automation | Approves the start date once |
| Sort incoming support emails into billing, technical, sales | Workflow with an AI classification step | Reviews the "unsure" bucket |
| Weekly status report compiled from five project trackers | Workflow with an AI summarising step | Edits and sends |
| Research three suppliers and prepare a comparison table | Agent with web search and a document tool | Checks facts, makes the decision |
| Approve a refund over the policy limit | Not automated | Decides; the system only prepares the file |
Notice the pattern in the last column. Automation removes the copying and chasing. It rarely removes the deciding, and where money, people or reputation are at stake, it should not.
No tools to install yet. In this lab you will inventory your own work and produce the list of candidate tasks the rest of the course will build on. You need a blank document or spreadsheet and your AI assistant of choice (ChatGPT, Claude, Copilot or Gemini).
Write down ten things you do at least weekly. Be concrete: not "emails" but "reply to leave requests", "chase late timesheets", "update the vendor tracker".
Add three columns: Frequency (daily, weekly, monthly), Minutes per occurrence, and Judgement needed (none, some, a lot).
For each task write Automation, Workflow with AI step, Agent, or Keep human. Use the table in Section 5 as your guide. A task with no judgement is an automation; reading or writing with clear rules is an AI step; open-ended research is an agent; money, people or legal decisions stay human.
Paste your table into your AI assistant with this prompt and note where it disagrees with you:
Choose one task rated Workflow with AI step that takes at least 30 minutes a week. You will map it in Modules 3 and 4, automate it in Modules 5 and 6, and add controls to it in Module 10.
A one-page task inventory (ten rows, four columns) with your chosen course task highlighted, saved as M1-task-inventory in your course folder.
Pick one answer per question, then check your score. These mirror the style of the final exam.
Answer in your own words first, then open the model answer.
Automation: fixed steps written in advance. Workflow with AI step: fixed steps where one or two steps use a model to read, classify, summarise or draft. Agent: a model given a goal and tools that decides its own steps.
Model, tools, memory, instructions. Instructions (the system prompt) plus the choice of which tools to allow are the administrator's main controls.
Any concrete answer is fine as long as the human-only task involves money, people, legal exposure or reputation, and the agent task is genuinely open-ended.
Further reading: A Visual Guide to LLM Agents · ReAct Agent Architecture · Intelligent Agents (Intro to AI, Module 3)
