For people who want to ship the system, not read about it
Getting an answer is easy. Getting it to stay up is the job.
Thirteen classes in the order the school teaches them. You start by getting a model to answer you the way you meant, and you finish running an agent unattended that can account for every action it took. 230.5 hours of it is already filmed, so you can be watching the first class ten minutes from now — and the next one runs live on 7 Sep, if you would rather be in the room for it.
Two ways in, both by the month: $250 for the recordings and the written curriculum; $500 for those plus a seat in every class on this path that runs this month. Either one renews monthly and you cancel it yourself, any time — and a class you finish stays yours after you stop.
tms path build-ai-systems --stats
- classes
- 13
- capabilities
- 41
- recorded
- 179 clips / 230.5 h
- exercises
- 625
- student apps
- 13 (1 fully guided)
- tops out at
- order 13 · Metasystematic
- hours of material
- ~413 h
An autonomous agent with memory.
One that runs without you watching it, remembers what happened last time, and picks up where it left off — behind a review gate, with a bill you can account for.
A laptop and an API key.
That is the whole entry requirement. The first class takes you from there.
The bill nobody models first
What will this thing cost you to run?
The demo is free and the bill is not. Set your traffic and your token mix, put today's prices in the rate fields, and see where a rented GPU stops being the expensive option.
Rates — change any of them
List prices to start with; everything on the right recomputes from whatever you type. Tokens a second means real traffic, not the benchmark chart. A month is 30.44 days.
Your bill, Claude Opus 4.1, 10,000 requests a day
$29,679
$355,875 a year · 9.750¢ per request
Claude Opus 4.1
$29,679 $355,875 a year $15.00 in / $75.00 out per million tokensCheaper model
$1,583 $18,980 a year $0.80 in / $4.00 out per million tokensSelf-hosted
$1,315 $15,768 a year 1 rented GPU at $1.80/hour, running all monthA rented GPU costs the same whether you serve ten requests or ten thousand. At your token mix and your rates, self-hosting overtakes the cheaper hosted model at about 8,300 requests a day, and beats Opus at about 450 requests a day.
You are at 10,000 a day, so self-hosting is the cheaper option here — and you have just taken on a GPU to keep alive.
Halve the prompt and the bill becomes $20,547 a month, $9,132 off, without changing vendor, model, or anything a user can see. That lever is the one nobody reaches for first, and pulling it is what most of this path is about.
Control AI Spending
Segment text into tokens and cost it. Run an open model on your own machine. The class that makes the number above yours instead of somebody else's.
Context Engineering
Design a recursive summarizer for documents several times the size of the context window — 329 exercises aimed squarely at the input-token half of that bill.
Production Agent Engineering
Configure an open model endpoint behind a provider interface, so switching between hosted and self-hosted is a config change rather than a rewrite.
Keep It Running
Produce an itemized bill for what your system costs, and work out what one user costs you to serve — including the user who costs several times the median.
Already on the calendar
The next 6 sessions on this path
The live plan is a seat in every one of them. Turn up with the traceback you are actually stuck on, ask about it out loud, and take the recording home afterward — it lands in the same login as the 230.5 hours that are already there.
tms schedule --path build-ai-systems
- 7Sep Agentic SDLC Monday · 26.5 h of it already filmed · 46 exercises · $500 on its own next up
- 12Sep Control AI Spending Saturday · 3.1 h of it already filmed · 12 exercises · $100 on its own
- 15Sep Intro to Agents Tuesday · 56.7 h of it already filmed · 32 exercises · $350 on its own
- 16Sep Context Engineering Wednesday · 35.7 h of it already filmed · 329 exercises · $400 on its own
- 17Sep Production Agent Engineering Thursday · 22.3 h of it already filmed · $200 on its own
- 25Sep AI Alignment Friday · 1 exercise · $300 on its own
Two ways in
Watch it all, or be in the room
Same curriculum either way. The difference is whether you are asking your questions out loud on the day, starting with Agentic SDLC on 7 September.
$250a month
Watch it all
- 179 clips and 230.5 hours across 13 classes, open the minute you join
- 625 exercises and the written curriculum for all of them
- The 13 tools that come with the classes
- Pause, rewind, and run the exercise with the class on the other screen
$500a month
Be in the room
- Everything on the left, plus a seat in every class on this path that runs this month
- Bring your own traceback and ask about it out loud — 6 sessions are already on the calendar
- Every session is filmed as it runs, so the hour you miss arrives days later
- The archive keeps growing while you are in it
Both renew every month and you cancel either one yourself, any time. A class you finish stays yours — the recordings, the exercises and the written curriculum for it remain in your login after the subscription stops. Classes can also be taken one at a time, at their own prices — this path runs from $100 to $500 a class.
What you walk out able to do
41 things you can prove you can do
Every one of these is a verb you can do afterward, and every one comes with the artifact you hand over to show you can. They stack: the step you are standing on is what lets you reach the next. The last one asks for a week of unattended agent runs and a replay of one of them reconstructed from the trace alone.
- follow a model quickstart model behavior
- locate a tool in an open tool hub system construction
Prove itfollow a model quickstart — A terminal transcript or notebook showing a successful call you ran yourself, including the model's response.
- execute a tool call round trip by hand system construction
- run an open model locally infrastructure
Prove itexecute a tool call round trip by hand — A transcript of all four messages — request, tool call, tool result, final answer — from a script with no agent library in it.
- configure an open model endpoint behind a provider interface infrastructure
- configure inference hyperparameters model behavior
- elicit output from a model model behavior
- operate a model as a first pass editor model behavior
- produce a list of where untrusted input enters a system security
- produce a reusable system prompt model behavior
- produce a rubric a model can apply evidence and verification
- produce an account of what you depend on and who can revoke it infrastructure
- produce an itemised bill for what your running system costs infrastructure
- segment text into tokens and cost it model behavior
- transform a corpus into an embedded index data shaping
Prove itconfigure an open model endpoint behind a provider interface — One client script, unedited, producing comparable output against both the hosted provider and your own endpoint, with only the base URL swapped.
- characterise a models failure modes model behavior
- characterise an applications injection surface security
- characterise what breaks when you cut the connection infrastructure
- characterise what one user costs you to serve infrastructure
- constrain model output to a schema model behavior
- generalise a reasoning prompt pattern model behavior
- generalise a tool interface for model use system construction
- parameterise a prompt template model behavior
Prove itcharacterise a models failure modes — A table of failure modes, each with the input that reproduces it and the output it produced, reproducible by a second person.
- falsify a prompt with a benchmark evidence and verification
- measure whether a fine tune changed behaviour model behavior
- verify a program does what you claimed with a test system construction
- verify a rubric against independent graders evidence and verification
- verify an agents actions with a critic evidence and verification
- verify model written code against its specification evidence and verification
- verify you would know your system broke before a user tells you infrastructure
Prove itfalsify a prompt with a benchmark — A benchmark file with graded cases, a run showing a non-zero failure rate, and a written account of which failures are the prompt's fault.
- construct a multi agent conversation with turn taking system construction
- construct a retrieval system system construction
- construct an agent that uses tools system construction
- design a recursive summariser for oversized documents data shaping
- design a review gate that catches what the doer cannot see organization and delegation
Prove itconstruct a multi agent conversation with turn taking — A transcript where the speaker order differs between two runs of the same task, both terminating, plus the selection rule in source.
- reconcile agents from different toolchains into one run system construction
- select among agent architectures system construction
- synthesise a metalanguage for a problem domain data shaping
- synthesise a self extending agent behind a review gate system construction
- synthesise an accountability regime for an unattended agent system construction
- synthesise an agent that carries notes across its own runs system construction
Prove itreconcile agents from different toolchains into one run — One transcript containing agents from at least two frameworks and one self-hosted backend, plus the script chaining its output into a second task.
Tagged by philosophy, this path is 32 make · 22 think · 14 own · 3 secure. A capability can carry more than one tag, so those add up to more than 41.
The sequence
Thirteen classes, from prompt to deployed system
This order is curated, not alphabetical and not chronological — each class stands on the one above it. The bar under each class fills up as you collect the 41 things you can do by the end: purple is what you walked in with, cyan is what that class hands you.
-
01
Using Large Language Models
- generalise a reasoning prompt pattern
- produce a reusable system prompt
- operate a model as a first pass editor
- +2 more
Prove itThe pattern applied to three tasks with paired outputs against a plain prompt, plus one task where it made the output worse.
- 6 clips · 10.7 h
- 55 exercises
-
02
Control AI Spending
- segment text into tokens and cost it
- run an open model locally
Prove itA token count for a real document, with the resulting cost and the fraction of the model's context window it occupies.
- 2 clips · 3.1 h
- 12 exercises
- Syllabus
- next session 12 Sep
- $100 on its own
-
03
AI Alignment
- verify a rubric against independent graders
- measure whether a fine tune changed behaviour
- falsify a prompt with a benchmark
- +5 more
Prove itTwo rubric versions, the adversarial inputs that forced the revision, and inter-grader agreement measured before and after.
- 1 exercise
- Syllabus
- next session 25 Sep
- $300 on its own
-
04
Context Engineering
- design a recursive summariser for oversized documents
- segment text into tokens and cost it
- configure inference hyperparameters
Prove itA run over a document several times the context window, a fact list written beforehand, and the recall score of the output against it.
- 27 clips · 35.7 h
- 329 exercises
- Guided app
- next session 16 Sep
- $400 on its own
-
05
Claude Model Context Protocol
- generalise a tool interface for model use
- execute a tool call round trip by hand
- locate a tool in an open tool hub
Prove itThe tool definition, a transcript of a model calling it correctly from the description alone, and a transcript of it recovering from a bad call.
- 3 clips · 5.2 h
- Syllabus
-
06
Intro to Agents
- construct an agent that uses tools
- constrain model output to a schema
- produce a reusable system prompt
- +2 more
Prove itA run log showing tool selection the author did not script, a benchmark over held-out tasks, and a case where it correctly declined to continue.
- 42 clips · 56.7 h
- 32 exercises
- Syllabus
- next session 15 Sep
- $350 on its own
-
07
Prompt Engineering
- synthesise a metalanguage for a problem domain
- falsify a prompt with a benchmark
- parameterise a prompt template
- +3 more
Prove itThe notation's grammar, a work produced in it that exceeds the context window, and a consistency check a reader runs to catch contradictions.
- 58 clips · 63.5 h
- 142 exercises
- Syllabus
-
08
RAG & Memory
- synthesise an agent that carries notes across its own runs
- construct a retrieval system
- transform a corpus into an embedded index
Prove itPaired runs of the same multi-step task with the note store kept and cleared, where the cleared run repeats a step the noted run skips, plus the note text the agent wrote and the later turn that cites it.
-
09
Advanced Retrieval Augmented Generation
- construct a retrieval system
- transform a corpus into an embedded index
Prove itA running system, a benchmark of retrieval quality over held-out questions, and citations traceable to source documents.
- 5 clips · 6.8 h
- 7 exercises
-
10
Production Agent Engineering
- synthesise a self extending agent behind a review gate
- select among agent architectures
- reconcile agents from different toolchains into one run
- +3 more
Prove itA diff the agent authored to its own prompt or toolset, the benchmark run that accepted it, and a logged rejection with the reason recorded.
- 19 clips · 22.3 h
- next session 17 Sep
- $200 on its own
-
11
Agentic SDLC
- synthesise an agent that carries notes across its own runs
- synthesise an accountability regime for an unattended agent
- design a review gate that catches what the doer cannot see
- +4 more
Prove itPaired runs of the same multi-step task with the note store kept and cleared, where the cleared run repeats a step the noted run skips, plus the note text the agent wrote and the later turn that cites it.
- 17 clips · 26.5 h
- 46 exercises
- Companion
- next session 7 Sep
- $500 on its own
-
12
Agentic AI Security: Securing What You Build
- verify model written code against its specification
- characterise an applications injection surface
- produce a list of where untrusted input enters a system
Prove itThe diff, a review note per changed hunk, and a test run that is red on the parent commit and green on the child.
- 1 exercise
- Reference
-
13
Keep It Running
- verify you would know your system broke before a user tells you
- characterise what one user costs you to serve
- characterise what breaks when you cut the connection
- +2 more
Prove itA real failure you learned about from your own alerting with the timestamps to prove it, and one you learned about from a user, with what you added so that one cannot happen the same way twice.
Straight answers
What is behind the login
230.5 hours of this path, filmed
Prompt Engineering is 58 clips and 63.5 hours; Intro to Agents is 42 clips and 56.7 hours; Context Engineering is 27 clips and 35.7 hours. 179 clips and 625 exercises in all.
Pause it, rewind the part where the trace does not match the code, and run the exercise with the class still on the other screen. Join on a Tuesday and you can be three classes in by the weekend.
And the room they were filmed in
A recording answers the question the teacher expected. The room answers the one you brought about your own repo. The next one is Agentic SDLC on Monday 7 September, and your pass is a seat in it.
Every session is filmed as it runs, so the hour you could not make arrives in the same login a few days later, and the archive you joined keeps getting longer while you are in it.
Both plans, and what carries on
The recordings, the exercises, the tools and the written curriculum come with either one; the dearer one adds the live room. A class you finish stays yours after you stop paying.
Watch it all $250 Be in the room $500The 13 tools that come with the classes
You open these next to your own work — your prompt, your repo, your bill — with the recording paused on the other screen.
- Context Engineering 1 guided workbench. Nine stations that walk you from a bloated prompt to a context budget you can defend, on your own material.
- Agentic SDLC 1 companion, 9 walkthrough decks. A companion for running the loop, plus decks on agent memory, the complexity ladder, context compression, git under agents, and shipping.
- Agentic AI Security: Securing What You Build 1 reference, 1 walkthrough deck. A frameworks reference and a map of where untrusted input gets into the thing you built.
Read this before you pay
Who this path is not for
- You will write code and live in a terminal. Not "a bit of Python eventually" — from Intro to Agents onward you are running processes, reading stack traces and configuring endpoints yourself.
- You want to own the running system, including the bill, the outage and the injection surface. If you want the output and not the operations, this is the wrong door.
- It is big. 413 hours of material and 625 exercises; Context Engineering alone has 329 of them. Come for the long haul.
- You want to wander, not to climb. Both plans here are 13 classes deep and one track wide, in a curated order. Somebody who wants to sample across all nine paths should start from the paths index and pick the one they mean.
If you want AI doing your work without building the plumbing yourself, two other paths cover the same ground with no terminal in them:
One sequence, worked out already
Press play tonight, be in the room on 7 September
179 clips and 230.5 hours open the moment you join, in the order the school teaches them, and every class above tells you what you will be able to do — and what you hand over to prove it — before you spend an hour on it. That next session is Agentic SDLC, and the live plan is a seat in it.
Both plans renew every month and you cancel yourself, any time. A class you finish stays yours afterward.