Agents 101 for Normies

What AI agents are, how to set one up, and what's happening behind the scenes.

Who are you?

A normal person not working in the tech industry — or in a non-software role at a tech company.
Not a vibe-coder (yet). Despite the trashy name, this is a useful activity — let me make the pitch.
You feel like a postal worker in 1985 learning about email for the first time.

What this is (and isn't)

This is not another vibe-coding course. The goal is to become a knowledgeable, discerning buyer of the ever-growing stack of "AI" tools — not a developer of them.

We do that by:

Building small applications with AI that improve the efficiency of our day-to-day work
Getting a working understanding of what makes AI systems tick — model capabilities, limitations, memory, eval, guardrails
Learning by constructing, not consuming — I have no interest in rebuilding what exists. I want to spend my money wisely, buy tools I understand deeply, accomplish my goals, and turn them off.

What will we cover?

A use case you can set up in 15–30 minutes, what it reveals about how agents work, and a roadmap to go deeper.

2

Start
My Day

3

Build
a Skill

4

Modes
of Use

5

Under the
Hood

★

Context
Rot

What's out of scope

Building AI for other people (apps, products, platforms)
Building custom tools (custom MCPs, harness development)
Agent-as-product (custom agents, A2A protocols, etc.)

Start My Day

One command. Six tools. Your morning briefing — done.

It's a Skill that an "Agent" applies. At the most fundamental level:

Skill = SOP + Trigger + AI

SOP

How you'd teach a new hire to do a specific task, written in plain English with numbered steps and documented edge cases.

Trigger

So the new hire (or AI) knows when to follow the procedure. A command, a schedule, or an event.

AI

The AI model knows how to execute the SOP and use the tools you specified.

Up next: We're switching to a live Claude session to build this skill from scratch. Follow along.

Let's Build the Skill

You need a clear SOP before you automate anything, with humans or AI.

Your tools

Slack, Gmail, Google Calendar, Jira, Zendesk, My News App.
Every day, for the first 30 minutes, do the following:

1

💬

Sweep Slack

DMs, channels, threads, group chats. Flag anything needing action.

2

📧

Sweep Email

Priority inbox. Identify actionable items.

3

🎫

Create Tickets

Collaboration items become Jira tickets. High-confidence: auto-create. Low: ask first.

4

📋

Check Jira

Read colleague comments. Identify actions needed from me.

5

🎧

Check Zendesk

Complaints, requests, and defects assigned to me.

6

📰

Fetch News

Domain and industry digest.

Set up your Skill in your AI agent tool

Install plugins and connectors for each tool. Most software you use at work already has plugins (or will within 6 months). If not, your IT team can likely build a custom connector.
Write the prompt: tell the AI the trigger (/start-my-day), the goal (summarize what I need to do today), success criteria (all action items identified), tools, and your SOP above.
Test it. Review the output. Refine the SOP. Repeat.

This pattern works everywhere. Claude calls it a Skill. ChatGPT calls it a Custom GPT. Gemini calls it a Gem. The name doesn't matter. The pattern does: write clear instructions with a goal, steps, tools, and what "done" looks like.

Build me a skill called "start-my-day".

Trigger: /start-my-day
Goal: Summarize everything I need to act on today.
Success criteria: Every actionable item across all tools is surfaced. I'd rather see a false positive than miss something.

Tools available (already installed):
- Slack
- Gmail
- Google Calendar
- Jira
- Zendesk
- My news digest app

SOP — run these steps in order:

1. Sweep Slack — DMs, channels, threads, group chats. Identify anything that requires action from me.
2. Sweep emails — focus on priority inbox. Flag anything with an actionable item.
3. Create tickets — items requiring collaboration become Jira tickets. High-confidence items: auto-create. Low-confidence: ask me individually.
4. Check Jira — read comments from colleagues, identify actions I need to take.
5. Check Zendesk — complaints, requests, and defects related to me.
6. Fetch news digest — read up on my domain and industry.

Output format: group by urgency (act now / act today / FYI). Include source and link for every item.

Before you start building, ask me clarifying questions about anything that's unclear or underspecified.

Modes of Use

How much you set up vs. how much AI does on its own

Setup investment vs. AI autonomy by mode

Your effort per task

AI autonomy (how much it does on its own)

Click a mode to explore

When to use

Try this

What you need

Keeping AI focused (context hygiene)

Mode 0

One-off

a search engine that talks back

Mode 1

Chat

a brainstorm partner

Mode 2

Project

a colleague you've already briefed

Mode 3

Agent

an assistant running your playbook

Mode 4

Orchestration

a system that runs without you

The 80/20 trap. AI makes the first 80% look effortless — and it genuinely is. But the last 20% (reliability, edge cases, integration, maintenance) is where the expertise lives. AI didn’t eliminate that work. It made it invisible until you’re already committed.

Beyond this session

Building AI Products

RAG · memory · guardrails · agent orchestration

Building AI Models

eval · fine-tuning · training data · RLHF

Under the Hood

First, how the app wraps the AI. Then, how agents loop.

Phase 1: The Harness Phase 2: The Agent Skills & Agents

Your Computer

AI Company's Server (Anthropic / OpenAI)

You Type a message

THE APP (Claude Desktop / ChatGPT)

Your Files Project files
Uploaded docs
Local folders

Chat Window Text box, buttons, history

Message Packager Bundles conversation + your files

Internet Connection Sends everything to the AI company (API call)

Show the Answer

The AI Brain Reads your conversation and files.
Picks the best next words.
Sends back one response.

Has no memory between calls.
Sees only what the app sends.

⚠

No memory box.
No knowledge base.
Every call starts from zero.

Notice what's missing.

There is no memory box. No knowledge base. The AI starts from zero every single call. The app fakes continuity by re-sending everything — your conversation and your files, every time.

Use the buttons below to walk through each step.

Bonus: Why Long Chats Go Bad (Context Rot Simulator)

Watch a conversation fill the AI's memory. Every message re-sends everything.

This is a toy example with a 3,000-word limit. Real systems handle 100K–1M+ tokens, but the exact same problem shows up at scale.

Simulated Chat

Words in the AI's memory (tokens)

0

of 3,000-word memory (scaled for demo)

AI's memory used

0%

Who's filling the memory?

0

Your messages

0

AI replies

Each message you send transmits

0

words to the AI (tokens)

Context is fresh. Quality is high.

Key insight: AI replies are 2–4x longer than yours. Its own words fill the memory faster than anything you type.