Agents 101: model plus tools plus loop
An agent is a model with tools running in a loop until done · know when you need one and when you don't.
::TL;DR · the whole lesson in three lines
- MOVEAn agent is a model with tools running in a loop until done · know when you need one and when you don't.
- DRILLYou will design (not build) an agent on paper for one of your real recurring tasks, then prove to yourself it actually needs the loop.
- WINYou can articulate the agent recipe in one sentence: model + tools + loop.
::concept · what's actually happening
An agent is not a magic upgrade · it is the simplest possible recipe: a language model, a set of tools it can call, and a loop that keeps running until the model says 'done' or hits a stop condition. Strip away the marketing and that is the entire engineering surface.
read full concept · 4 more paragraphs →collapse concept ↑
Single-shot prompting beats agents whenever the task fits in one round-trip and you can verify the output in seconds. Drafting an email, summarizing a doc, rewriting a paragraph · these do not need an agent loop, they need a good prompt.
Agents earn their cost when the work requires uncertain branching · 'I do not know what step three is until I see the result of step two,' or 'I need to call three different tools depending on what the file contains.' That branching is where the loop pays for itself.
Every agent loop is one bad tool description away from infinite spin. The model will happily call list_files() 47 times if list_files() looks like the answer to its current confusion. Budget caps, max-step limits, and explicit stop conditions are not optional.
The blunt heuristic: if you can write the workflow as a checklist a human could follow without making choices, you do not need an agent · you need a script. If choices live inside the workflow, an agent might pay for itself.
::drill · do the thing
You will design (not build) an agent on paper for one of your real recurring tasks, then prove to yourself it actually needs the loop.
::L30 drill · copy-paste into any AI chat
I want to design an AI agent (not a one-shot prompt) for this recurring task I do: [DESCRIBE THE TASK IN 2-3 SENTENCES]. Walk me through: 1) the minimum tools this agent would need (3-5 max, with one-line descriptions of each), 2) the stop condition that tells the loop 'we are done,' 3) the max-step budget you would set as a hard cap, 4) the failure mode you are most worried about, and 5) a brutally honest verdict: does this task actually need an agent loop, or could a single well-crafted prompt plus my own follow-up handle it? Do not flatter the agent framing. If single-shot wins, say so.
::steps
- 01Pick one task you actually do at least weekly that involves uncertainty (research, code review, planning).
- 02Paste the prompt above with your task filled in.
- 03Read the verdict honestly · if the model says single-shot wins, accept it.
- 04If agent wins, write down the 3-5 tools, stop condition, and step cap on a sticky note.
- 05Try the single-shot version anyway as a baseline · time it, save the output.
- 06Decide if the agent overhead is worth it for your weekly cadence.
::outcome · what should be true
- You can articulate the agent recipe in one sentence: model + tools + loop.
- You have an honest verdict on whether one of your tasks actually needs an agent.
- You wrote down a step cap and stop condition · not optional hand-waving.
- You compared agent-overhead against a single-shot baseline you actually ran.
::trap · the most common failure
Operators read 'agent' and reach for it on every task because it sounds advanced · then watch the loop call the same tool eight times and burn $4 doing what one prompt could have done for $0.03. Default to single-shot. Earn the loop.
::end of the curriculum
You're at Pilot level. There's no Level 6.
The next move is doing the work, not another lesson. If you want operator-grade infrastructure, that's /orangebox. If you want the lab's working journal, /founders-view. If you want to collaborate on the curriculum itself, the source is public on GitHub.
::other lessons at Operator level
Local AI · Ollama — privacy, offline, and the limit of free
At Operator level you need an honest opinion about local-only AI. Even if you don't use it daily, you should have run it once.
Model routing — switching between Claude, GPT, Gemini mid-task
Operators don't pick one AI. They route each task to the model that does it best. Knowing the strengths is the skill.
MCP servers — the plug socket that turned AI into a real tool
Model Context Protocol is the standard plug. Knowing what plugs in changes what your AI can actually touch — your files, your inbox, your calendar, your repos.
Agent mode — when AI takes action, not just answers
The frontier of useful AI is agents that DO things — browse, click, file, send. The actual skill is the safety pattern, not the magic.
Computer use — when AI takes the mouse and keyboard
Claude in Chrome, ChatGPT Atlas, computer-use beta — the frontier is AI that drives your browser like a human. Knowing the safety pattern is the actual skill.
What AI cannot replace — taste, judgment, relationships
The operators winning in 2026 are the ones who learned what AI is for and what is theirs. Knowing the line is more valuable than any prompt.
MCP: structured tools for AI
Model Context Protocol is the USB-C of AI tooling · learn the shape before you wire anything.
Skill primers: teach a session your context in 30 seconds
A skill is a reusable file that primes a fresh AI session with your project, voice, and rules · stop re-explaining yourself.
Local models with Ollama
Run Llama, Qwen, or Mistral on your own laptop · no API, no logs, no monthly bill for the work that should stay home.
Vision models: when to use them
Vision lets the model see images · powerful for screenshots and diagrams · weak for precise spatial work · know the line.
Audio and Whisper transcription
Whisper turns audio into text · meetings, voice memos, interviews · the AI-era replacement for note-taking.
RAG vs long context: when to retrieve, when to dump
RAG fetches the right slice of your data at query time · long context stuffs everything in · know which problem you actually have.
Embeddings: meaning as numbers
An embedding is a list of numbers that captures the meaning of text · learn the shape and you unlock semantic search, deduplication, and clustering.
Fine-tuning vs prompt engineering
For individuals, fine-tuning is almost never worth it · know exactly when it actually is.
AI safety in personal use
PII, NDAs, financial data, and other people's secrets · know the rules of what you do not paste.
Multimodal prompting: combining text, image, audio
The strongest prompts use the medium that fits the question · sometimes you describe, sometimes you show, sometimes you do both.
Chain-of-thought: making the model show its work
Asking the model to reason step-by-step before answering raises accuracy on hard problems · know when it earns its cost.
Tool use and structured output
Function calling makes the model return JSON your code can use · know the contract before you build on it.
Cost optimization: tokens, caching, model selection
AI is metered · the operators who stay profitable measure what they spend and choose the model that fits the task.
::part of the AtomEons /learn curriculum · 45 lessons · 5 levels · cc-by 4.0