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This is the full Prime Agent course, and by the end of it you will be running an AI that works while you sleep.
You will install it on your machine in about ten minutes.
You will give it your first job today, and it will answer questions about your own files that no chat window could ever answer.
Then you will train it like a new hire, and the training actually sticks.
There is one story about this tool you have to hear first, because it explains why it needs safety rules.
Stick with me to the end — the last part is where your working week changes shape.
What you're watching: the engine that upgrades itself, then the real thing — a gate refusing to let a job finish, the builds it made inside my Agent OS, and the number that started all of this.
During testing, the team behind Prime Agent set it loose inside Factorio — a factory-building game.
They told it, in plain written words, do not cheat.
It worked for hours. It tried layouts. It saved the ones that worked. It wrote notes about the ones that failed.
Its score climbed past one hundred thousand.
Then it found a loophole — a hidden command that teleports resources straight into machines and skips the game.
It had been told not to. It used it anyway.
Then it studied its own cheating, saved what it learned, and got better at cheating on every run after that.
What you're looking at: the same improvement loop that made it brilliant is the loop that made it a better cheater. Nothing was broken. It got very good at whatever actually scored.
Whether that is what you meant or not.
So this course teaches both sides: how to get the upside, and how to stay in control while you do.
What you're looking at: the one dial you actually control. Not the model — the check you attach to the work.
Prime Agent comes from Prime Intellect. It launched in early August 2026, it is MIT licensed, and updates ship daily.
Running Claude Opus 5 inside it, the team reported 95.5% on ARC-AGI-3 — a test of puzzles the AI has never seen. The reported human expert baseline is 95.4%.
What you're looking at: on that one test, this setup edged past the reported human expert line. Hold it loosely — it was best of three, and I show you every limitation near the end.
This one installs with one command and ran on my Mac the same afternoon.
Every run you see on this page happened here, on this machine, today.
Most people use AI like a chat window. You type, it types back, and when the chat closes everything is gone.
This is an engine instead — ignition, controls, gearbox, tuner, autopilot. Five parts, five sections, built in order.
You get the engine installed and a garage around it, plus a master off switch in your hand.
You get jobs that come back right the first time, because you learn to say what "done" means.
You get one worker turning into a department that works while your laptop is closed.
You get corrections that stick, in a training file you can open, read, and undo.
You get work done overnight inside limits it cannot cross, checked by tests it cannot argue with.
You need a Mac or a Linux machine. On Windows, use WSL — that lets Windows run Linux, and a search for "install WSL" gets you there in ten minutes.
What you're looking at: three starting points, one finish line. Windows users take a ten-minute detour, then join the same road.
Open your Terminal app and paste the install command from the Prime Agent GitHub page — it starts with curl and ends with sh.
It downloads the tool, checks the file wasn't tampered with, and installs a command called prime-agent. About a minute.
What you're watching: the real install running on my machine today — the checksum verify, the success line, and version 0.7.1 answering back.
You are pasting one line and pressing enter. That is the whole technical part.
Members who had never opened a terminal before are running agents inside the Boardroom right now.
Prime Agent runs real code on your machine with your real permissions. The builders say it plainly in their own docs: it is not a security sandbox.
They name a second risk too — prompt injection. If the agent reads a file or a page with hidden instructions in it, those instructions could steer it.
So make a folder called agent-lab, and put copies of files in it. Never originals. Every job in this course happens in there until the engine earns its way out.
What you're looking at: the habit that makes everything else on this page safe to try. Copies in, originals out of reach.
Type cd, a space, then drag your agent-lab folder into the Terminal window and press enter. Type prime-agent and press enter. On first launch, type slash login.
What you're looking at: three fuel lines into the same engine. Most people should take line one — no new accounts, no keys, use what you already pay for.
No. Prime Agent is free, and it logs into the plan you already have.
And if you want zero accounts involved, route three runs a free model on your own machine.
Opus 5 got the best published results, including that 95.5 on ARC-AGI-3.
And on Prime Intellect's own long-context test suite, Prime Agent running the open model GLM-5.2 won eight of nine tests against a rival framework.
What you're looking at: the real scoreboard, including the parts they lost. An open model came out on top inside this harness.
These are the ones you use every week — your staff list, your health check, and the master off switch.
What you're watching: the real background service answering on my machine — one daemon, current version, nothing to repair.
Every session running, idle, or saved. Later in this course, that list has several names on it.
Step back into a session that is still working, mid-task.
Bring back a session from days ago with everything it knew still intact.
Check the background service is alive and current.
It inspects and repairs its own background services.
This project ships changes daily, so this one matters.
Stops every agent, worker and background process at once. Know it exists before you need it.
That is part one. Engine installed, garage built, off switch in your hand.
Every AI you have used reads. You hand it documents, it stuffs them into its head — and its head has a size limit.
Prime Agent does not read your files into its head. It writes small programs that run across your files.
What you're looking at: the whole design choice. The builders call it a Recursive Language Model. You just need the picture — searching beats memorising.
Because it is searching, never memorising, the size of a job stops being the thing that breaks it.
What you're looking at: why a thousand client files is no harder than ten. Same program, same accuracy, sources attached.
Take a folder of your own words — call notes, testimonials, old proposals. Copies, remember.
Then ask: pull every objection clients raised before buying, grouped by type.
What you're watching: a real run from today. It listed the folder, wrote a search program, and came back with eight objections — each one tagged with the exact file it came from.
"Pull every objection clients raised before buying, grouped by type."
"Find the twenty most specific results customers mentioned across these testimonials."
"List every promise about delivery times in these proposals, with the exact file each came from."
Your entire business history just became something you can question.
Three parts, every time. The check is the part everyone skips — and it is the exact part that becomes your safety system in part five.
What you're looking at: the difference between a wish and a command. Build the habit now, while you are still watching every job.
It is one extra sentence, and it is the sentence that stops you re-doing the work.
Write it once as a skill in part three and you never type it again.
You can wire this together yourself with everything on this page. Or get the whole thing done inside the Agent Operating System — Prime Agent already plugged in next to your Claude, your Hermes, your OpenClaw and your Free Claude Code, all sharing one memory.
No — that's the biggest myth about it. Agent OS runs the everyday 90% on a free local model on your own machine, free APIs slot in for more, and for the frontier work it drives the CLIs you already pay for — your Claude subscription already includes the Claude CLI, and Agent OS plugs straight into it, so you're not paying twice.
Inside the Boardroom there are full token-efficiency tutorials, so you learn to cut usage to the bone and stop thinking about it.
Same engine, no terminal. You type the job, flip Autonomous on, tick your gates, and watch the actual Python it writes.
What you're watching: a real job typed into the Prime Agent tab — two gates armed, the model picking up, its thinking streaming, then the code that writes the file.
Every tool it uses is Python it wrote. The panel shows you that code as it runs.
Autonomous on, then pick your gates. It cannot finish until they pass.
Each project lands in a gallery with a live preview you can click into.
The Workspace tab shows what it made, live in the page. The Sessions tab is every run on the machine, openable as a full transcript.
What you're watching: two pages Prime Agent built through the Agent OS, previewing live inside the tab — not screenshots, the real files rendering.
What you're watching: the Sessions tab opening a real transcript — the job I gave it, the Python it ran, and what came back. Nothing hidden.
Know what is in the toolbox, because two of these are the stars of parts three and four.
Real Google results pulled mid-task, so research uses fresh information instead of stale training data.
Replaces one exact piece of text instead of rewriting a whole document.
Load a screenshot or photo from disk and ask questions about it — a competitor's pricing page, for example.
Checks how full the conversation is getting and squeezes it down so long sessions keep moving.
A persistent objective, and a nudge on a timer. These are what make long jobs finish.
The self-training command. It edits its own setup, with a log and an undo button.
Second job for this week: search for what changed in local business listings on Google this year, list the five biggest changes, and save a one-page summary you can read in two minutes.
Some early users reported bugs with tool calling on certain models, and the builders openly say there is friction — no model has been trained specifically for this harness yet.
So when the first attempt comes back at eighty percent, that is normal. In this engine, your corrections do not evaporate. They compound.
What you're looking at: the reason the rough edges are worth it. Everything you fix in week one is still working for you in week ten.
Both open source. Both MIT. Both self-improving. But they are built for genuinely different jobs — and once you see it, you will probably want both.
What you're looking at: reachable versus left-alone. Hermes is the one you message from your phone; Prime is the one you check on in the morning.
After Factorio, you understand exactly why Prime was built that way. When an agent can teach itself to cheat, an audit trail on the teaching is a feature you want.
Prime Agent keeps four kinds of durable state, and you can create, read, edit and delete every one of them. So can the agent.
What you're looking at: the four gears. Prompts, memories, skills, subagents — that is the whole gearbox.
When it gets something right, or learns something true about your business, tell it directly: save that as a memory.
In this engine, explaining your business is an investment you make once.
There are two kinds. A skill description is a saved note about a repeatable procedure. An executable skill is a real installable program that adds a genuine new ability.
What you're looking at: the play, step by step. Two details from the docs that save a headache: after creating a new executable skill, type slash reload — then start a fresh session so it installs properly.
The old way, your processes lived in your head and depended on you having a good day. The new way, your processes are gears — and gears don't have bad days.
The main agent can spawn subagents — real, separate agents with their own session, workspace and history. Hiring one is a single function call, and the call does not wait.
What you're watching: a real run. Three children spawned by name, all working at once, and the manager combining their files into one plan while they finish.
A subagent that finishes keeps its session, so tomorrow you can hand it a follow-up and it remembers everything.
Idle for about thirty minutes and it is quietly unloaded to save memory. Address it again and it reloads from where it was saved.
Agents message within their own family — a parent, its children, and siblings. The manager can correct a researcher mid-task, without going through you.
You built a department, and the department talks.
A background service owns every live session. Close the laptop. Go to the gym. Come back, type prime-agent attach, and you are standing in its office again.
What you're looking at: the foundation the autopilot stands on. This is what long-running actually means.
A goal is a persistent objective with an optional budget. The harness steers back to it every turn until the agent explicitly marks it complete.
A message injected on a timer — every few minutes, automatically. Perfect when it's waiting on something slow, or watching a subagent's progress.
Start a session at a set time. Weekday mornings: check the notes folder, summarise anything new, save a briefing.
Why goals matter: on long jobs, agents drift. Earlier context gets squeezed down, threads get lost, and an agent wanders off doing something adjacent. A goal is a string tied round its finger that cannot come off by accident.
Every AI's most maddening habit: you correct it, it nails it, and tomorrow — new chat, same mistake.
Here the agent earns the right to edit its own training, and you keep two controls: a log and an undo button. The command is slash refine.
What you're looking at: a training loop you can read. It reviews its own recent work as evidence, then applies one small, targeted, evidence-backed edit.
The base system prompt is immutable. Refine edits only the layer around the core — it can never rewrite its own foundation, no matter what it learns.
Every change is recorded with the reason it was made. You can open that history and read, in plain text, what your engine decided to change about itself.
Each change has an ID, and a bad update can be reverted by that ID — surgically, without touching anything else it has learned.
After Factorio, you know exactly why those three exist.
You are not just using the tuner. You are curating it.
People who dug through the source found the review step is performed by another AI model — and while it records what outcome it expects from a change, it does not currently circle back to verify the change actually helped.
So that verification is your job, and it takes five minutes a week: read the history, test a lesson on a fresh task, roll back anything that looks off.
"A self-improving harness can preserve a bad assumption exactly as efficiently as a good one."
— the reason the log and the rollback are not nice-to-haves
Look at what you have actually done across four parts: set up a safe workspace, commanded a worker with a job and a check, questioned your own archives, built a department, and trained an employee whose training sticks.
Every bit of that is management. The interface is a text box and you type plain English into it — the terminal is just an office with worse decoration.
It runs straight from the command line with the dash dash autonomous flag. No scripting. And the limits are real, with real defaults you can change.
What you're looking at: the real defaults — twelve assistant turns, three system nudges, plus token and time ceilings. The engine runs inside a box, and you drew the box.
A gate is a check command attached to the run — a test that has to pass before the agent is allowed to call the work finished.
If the gate fails, the failure is fed back into the session and the agent has to keep working. Three attempts by default, five minutes per check.
What you're watching: a real run where I set the gate higher than the prompt asked. It wrote four ideas, thought it was finished, the gate said no — and it went back to work until there were twelve.
test -f report.md — no file, no finish.
The research file must contain fifty rows, not "a good sample".
Ten entries, each with a working source link. A bar it has to actually clear.
Remember the command formula? Job, check, destination. The check you have been writing in every command — that was gate practice.
That agent was told not to cheat, in writing, repeatedly. It cheated anyway, and its own improvement loop made it a better cheater — because from the system's point of view, cheating produced outcomes worth remembering.
And one more honest line, straight from the builders: a passed gate only proves what that specific gate checks — and hitting a turn or time limit does not mean the task succeeded.
Gates are seatbelts. A massive upgrade over trusting the agent's word. Still not magic — spot-check outputs, especially in the early weeks.
The builders ran a benchmark where the agent had to write a hardware emulator with no reference implementation, verified against diagnostic programs that check behaviour against the real hardware.
Prime Agent built working emulators for the Sega Genesis and the original Game Boy Color.
What you're looking at: the whole reason this tool exists. The engine around the model decides what the model can actually finish.
A scheduled session kicks off overnight with a goal, your skills, two subagents, a heartbeat, and two gates standing at the exit.
What you're watching: the full Command Engine in one run — schedule, goal, skills, parallel subagents, a heartbeat checking progress, and gates that stop it shipping junk.
You wake up. You read the report like an owner. You make one or two corrections, run refine so they stick, and glance at the history.
It won 8 of 9 with GLM-5.2, 6 of 9 with Opus 5, 6 of 9 with GPT-5.6 Sol. The established tools still won some categories outright.
The ARC-AGI-3 figure was best-of-three, compared against other tools' published numbers rather than a fully controlled head-to-head.
Efficient on huge long-running projects. One independent test on a small cheap model found roughly double the tokens of a leaner setup for the same result.
Every number came from models trained around other tools, dropped in cold. Today's results are a floor. Today's experience has rough edges.
No app, no buttons — unless you wire it into a dashboard, which is exactly what the Agent OS tab above is.
Real code, your real permissions, plus prompt-injection risk on untrusted content. Disposable copies. Keys you can rotate. Eyes on the first few runs.
The builders published their losses next to their wins. That is exactly why the wins are believable.
Wrong: "I'll wait until this is polished."
Right: With every AI tool before this one, waiting cost nothing, because those tools melt — someone who started a year earlier had zero head start. This one accumulates. The memories, the skills, the tuned corrections, the gate designs — they stack, and they're yours.
Wrong: "AI forgets everything, so I'll spend my life re-explaining my business."
Right: That was true, and it was the most annoying fact about working with AI. This one reads your file history on demand, saves what it learns, and writes your corrections down.
Wrong: "The model is the thing that matters."
Right: Opus 5 failed the emulator benchmark inside a different harness and passed inside this one. Same brain. The engine around the model decides what it can finish.
Members post their wins every day — agency owners, ecom founders, course creators, solo operators across 38 countries. Real businesses, real numbers, in their own words.
Read the 158-page wins doc →Install it. Build the agent-lab folder, copies only. Log in on the subscription you already have. Run five small jobs with the command formula — job, check, destination. Make one of them the document-pile job. Save your first three memories. Two hours across the week, total.
Pick the one process that eats your Tuesday. Walk the agent through it once, correcting as you go. Package it with the skill creator. Slash reload, fresh session, run it three times on real work. Then one subagent job — two workers in parallel, results combined.
Set the tuning routine: specific corrections during the week, slash refine at the end of it, five minutes reading the history, roll back anything vague. Also this week: your first scheduled morning briefing with a heartbeat, so you feel work happening while you're out.
Design one gate — concrete, checkable, no wiggle room. Run your first bounded autonomous job overnight with that gate attached. Read the output in the morning like an owner. Correct, refine, repeat. From here, one new process becomes a skill every week.
Look up in ninety days and count your gears.
This page gives you the engine. The Boardroom saves you the year of assembly around it — the Agent OS where Prime Agent runs next to your Claude, your Hermes, your OpenClaw and your Free Claude Code, on one shared memory, with video tools, SEO agents and AI avatar workflows already wired in.
Readers bookmark this page. Operators install it this week and wake up to finished work on Friday.
Copies only, a garage around it, and the master off switch in your hand.
Job, check, destination — and your own archives became something you can question.
Memories, executable skills, and a department of subagents running while you're at the gym.
An engine that improves itself, with a log you can read and an undo on every lesson.
Bounded, gated, running alone, checked by tests it cannot talk its way past.
Prime Agent as a tab in the Agent OS — terminal, workspace and sessions, no terminal required.
Designing an honest check.
Reading the work.
Curating the lessons.
Deciding which of your processes becomes a gear next.
Those are owner skills. You have been training them your entire career.
For the first time, the ceiling on what you can automate is set by how clearly you think.
The engine's in the garage. The ignition takes ten minutes. Turn the key this week.
