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One idea for building, checking or operating an AI application. Free, separate from registration, and easy to leave.
Prof Rod · Room 1
Today's lesson
Build AI systems
you can explain.
A free classroom for applied AI engineering. Learn to turn a brief into a working application, test its limits, and explain the decisions behind it.
Define the task → Build the workflow → Check the result
Lesson one · open to everyone
No account neededStart Lesson OneTaught by Rod Rivera — Professor of the Practice at ITAM, teaching at CDTM/TUM, after fifteen years of AI at Alibaba Cloud, Huawei, Samsung and Rasa.
Lesson One · open to everyoneRead the opening lesson before choosing a course.
Claude Code for Business3 min readLesson 1 of 12
Why This Course: The Backlog Nobody Ever Delegated
You have a reconciliation to finish, a contract folder to organize, or a recurring report to prepare. Before handing it to an agent, choose a small example whose inputs and expected result you understand. That gives you a way to tell useful work from a plausible answer.
Your experience helps you describe what matters: which records belong together, what counts as a mismatch, and which action needs approval. This course turns that understanding into a brief, a bounded attempt and a check of the result. It does not assume that a confident answer proves the task was completed.
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Keep a lesson in your week
An optional weekly lesson to put an idea into practice. Courses are free, with registration where required; Lesson One needs no account.
The book · free to read
Build Your Always-On AI Agent From Scratch
Build a self-contained Python agent with tools, memory, messaging, permissions and recovery. Sixteen construction drafts, runnable checkpoints, and no account required.
Open the book
Who's in the room
An explanation. A build. A difficult question.
Bring your experience with data, integrations or AI applications. We work through the mechanism, build an example, and ask what the result actually proves.
Prof Rod
The Teacher
Explains the mechanism, draws it on the board, and works through the example. You get the reasoning as well as the result.
Mator
The Builder — visiting
Does exactly what you asked, immediately, at full speed. Which is how you find out what you actually asked for.
Quackster
The Alchemist — visiting
Asks the question you were too polite to ask. Usually the one that mattered.
The animated episodes are still being drawn. The writing, the courses and the notes are all here now.
What's in the room
The syllabus
The course register
From your first working example to systems you can evaluate
Syllabus
Intermediate
Tools, Memory, and Multi-Agent Systems
Design tools an agent can call safely and memory it can trust past one session.
Method
Subject
AI agents
Lessons
34
Time
319 min read
Open this course
319 min read34 lessons
Syllabus
Live · updated weekly
IntermediateZEO ITAM · Autumn 2026
Run a measured Zero-Employee Organization (ZEO) loop against your own minutes baseline.
Serial
Subject
ITAM
Lessons
18
Time
417 min read
Open this course
417 min read18 lessons
Syllabus
Beginner
The 12-Week Transformation Tracker, Run on Claude Code
Log whether automation is actually getting cheaper, against your own Week 1 baseline.
Outcome
Subject
Claude Code
Lessons
13
Time
91 min read
Open this course
91 min read13 lessons
The journal
Latest from the journal
Practice2026-08-1713 min read
Build a Working Agent with sovereign-agent
Clone it, install it editable, run the offline example, then read the 67-symbol API you'd actually build on. A working agent in about fifteen minutes, no API key required.
Rod Rivera
Practice11 min read
Build Your First ZeoCore Tool and Check Its Failure Boundaries
Turn a small Python function into a typed agent tool. Follow its input and result contracts, reproduce the failure cases, and separate what validation catches from what it cannot prove.
Practice12 min read
Govern a Multi-Agent Project With Zero-Employee
Install the zeo CLI, write a real Statement of Work, and hit the exact schema error a beginner hits first. This is the tool this site's own governance arguments run on.
ZEO Theory13 min read
Why AI Agents Don't Dissolve the Firm
Ronald Coase asked in 1937 why firms exist instead of markets. AI agents revive the question — and the old answer says the firm concentrates, not dissolves.
The corkboard
Pinned up during the week
Short notes from the practice — what broke, what it cost, what turned out to be wrong.
Give a Reasoning Model a Clear Destination
Sep 9 · 2026A difficult task benefits from a clear outcome and constraints. A long prompt full of instructions to “think harder” may add little useful information. For a practice planning task, give the OpenAI model the available hours, required deliverables and…
openaiprompting
Record the Model Alongside the Prompt
Sep 8 · 2026A saved prompt is not a complete experiment record. The model, relevant settings and test inputs can all affect the result. When comparing an OpenAI workflow, record the exact model identifier used, the prompt version and the evaluation set. Keep the response…
openaievaluation
Write the Evaluation Before Tuning the Prompt
Sep 7 · 2026Prompt tuning is hard to assess when the definition of “better” changes after every answer. Freeze a small acceptance set first. For a document-routing task, define the allowed destinations and label a collection of synthetic examples. Include ambiguous…
openaievaluation
A Valid JSON Object Can Still Be a Wrong Answer
Sep 6 · 2026Structured output can make a model response easier to parse. It cannot tell you whether the invoice number or amount was extracted correctly. Try a synthetic invoice with a subtotal, tax and total. Ask an OpenAI model to return a small structured record, then…
openaistructured-output
Test a Model on the Exceptions You Actually See
Sep 5 · 2026A model can handle ordinary support tickets well and still fail on the exceptions that consume most of your team's time. Build a small evaluation set from representative, appropriately sanitized cases. Include a missing order number, conflicting dates, an…
openaievaluation
Keep a Codex Handoff Smaller Than the Session
Sep 4 · 2026A handoff should let someone resume the work without replaying every exploratory turn. Give them the state they need to make the next decision. Ask for the working directory, branch, source revision, changed files, checks run and remaining blockers. Include…
codexworktrees
Thought 1 of 0
Who teaches this
Rod Rivera
Professor of the Practice in AI at ITAM, and fifteen years of production machine learning at Alibaba Cloud, Huawei, Samsung and Rasa. Rod runs these systems on his own work first, and the classroom gets the implementation decisions, the checks and the parts that did not work.
More about RodBefore you go
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