Agentic AI for Data: Your Judgment, at Scale
An agent is a new tool that uses tools. The expertise is yours, and that's what makes you irreplaceable.
You go at your own pace, with your Maestro beside you, 1:1, through the whole course: ask it anything, and it asks you questions too.
61 skills · 10 modules
$1
I'm opening the course one module at a time. When every module is out, the course will be $149. What you pay today covers all of them.
Comes with your Maestro, 1:1, for the whole course.
What will you be able to do?
Each box is a skill. The base is at the bottom, recognizing and explaining, and you work your way up to building something of your own and judging what works and why. Hover over a box to see what it builds on. Click it to see where I teach it and how your Maestro checks it.
- Evaluate
- Synthesize
- Analyze
- Apply
- Understand
- Remember
Evaluate
Assess whether a data source is fit for the question, given its coverage, collection method, and known gaps
Builds on:
- Know what data provenance means: where a table came from, how the data was collected, and what was done to it
- Turn a request at work into a question your data can actually answer
Your Maestro asks you to judge when it fits and why and saves it as evidence when you do.
Answer 'how do you know?' for any number in your work, with evidence
Builds on:
- Trace a number in a deliverable back through the agent's work to the source data
- Verify an agent's result by a path the agent did not use
- State what a result does and does not support
Your Maestro asks you to judge when it fits and why and saves it as evidence when you do.
Synthesize
Write a quality bar for a type of data work, with criteria a person or an agent can check
Where I teach it: Put It in Writing · Your quality bar
Builds on:
- Find your own standards in work you accepted, work you sent back, and the reason for each decision
- Name what makes data work defensible: traceable, reproducible, validated, and honest about its limitations
Your Maestro asks you to build something of your own with it and saves it as evidence when you do.
Write a methods note that lets a reader understand and check how a result was produced
Builds on:
- Record the decisions made during an analysis and the reason for each
- Trace a number back through its transformations to its source data
- State what a result does and does not support
- Write a metric definition precisely enough for two people to calculate the same number
Your Maestro asks you to build something of your own with it and saves it as evidence when you do.
Produce a methods note from the evidence trail with an agent, and verify it against what was done
Builds on:
- Write a methods note that lets a reader understand and check how a result was produced
- Have the agent leave evidence as it works: code it ran, row counts, check results, and decisions it made
Your Maestro asks you to build something of your own with it and saves it as evidence when you do.
Analyze
Review an agent's output against the task specification before accepting it
Where I teach it: Ask Like an Expert, Review Like an Expert · Review the result against your specification
Builds on:
- Specify a task for an agent with a goal, inputs, constraints, and criteria for completion
- Read the record of what an agent did: the commands it ran, the files it changed, and the results it saw
- Know how an agent's response to a data task can miss what you need: decisions you didn't ask for, summaries that claim more than was verified, code that counts something else, and made-up values
- Spot-check a result by recomputing a few rows by hand, spread across the start, middle, and end
Your Maestro asks you to break a problem down with it and saves it as evidence when you do.
Find your own standards in work you accepted, work you sent back, and the reason for each decision
Where I teach it: Put It in Writing · What you accept and what you send back
Builds on:
- Recognize that much of your expertise is tacit: standards and habits you apply without stating them
Your Maestro asks you to break a problem down with it and saves it as evidence when you do.
Decide which data the agent can see, and keep sensitive or regulated data out of places it doesn't belong
Where I teach it: With Your Data · Before opening your data
Builds on:
- Give the agent the least access it needs, and separate read access from write access
Your Maestro asks you to break a problem down with it and saves it as evidence when you do.
When the agent makes a mistake, change the environment so it doesn't happen again
Where I teach it: With Your Data · What went wrong goes in the file
Builds on:
- Review an agent's output against the task specification before accepting it
- Write persistent context that the agent reads at the start of each session
Your Maestro asks you to break a problem down with it and saves it as evidence when you do.
Reconcile a result against an independent source or a previously trusted figure, and explain any difference
Builds on:
- Check a result against what you expect: order of magnitude, totals, ranges, and published references
- Trace a number back through its transformations to its source data
Your Maestro asks you to break a problem down with it and saves it as evidence when you do.
Verify an agent's result by a path the agent did not use
Builds on:
- Reconcile a result against an independent source or a previously trusted figure, and explain any difference
- Review an agent's output against the task specification before accepting it
Your Maestro asks you to break a problem down with it and saves it as evidence when you do.
Apply
Run a first session with an agent on a task using real data and review what it did
Where I teach it: The Agent on Your Computer · Your First Session
Builds on:
- Distinguish the model, harness, and context in an AI agent
- Know how an agent reaches your data: commands, files, connectors, and credentials
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Check a result against what you expect: order of magnitude, totals, ranges, and published references
Where I teach it: What the Agent Decided for You · Three quick checks
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Specify a task for an agent with a goal, inputs, constraints, and criteria for completion
Where I teach it: Ask Like an Expert, Review Like an Expert · Specify the task
Builds on:
- Explain how an agent works: it reads the context, uses tools, observes the results, and repeats
- Understand that an agent only knows about your work what is in its context, that this context is limited, and that it isn't saved from one session to the next
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Write persistent context that the agent reads at the start of each session
Where I teach it: Put It in Writing · A file the agent reads when it starts
Builds on:
- Understand that an agent only knows about your work what is in its context, that this context is limited, and that it isn't saved from one session to the next
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Write down what the agent needs to know about your data: rules, known quirks, and vocabulary
Where I teach it: Put It in Writing · What you know about your data, in writing
Builds on:
- Recognize that much of your expertise is tacit: standards and habits you apply without stating them
- Write persistent context that the agent reads at the start of each session
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Write a metric definition precisely enough for two people to calculate the same number
Where I teach it: With Your Data · What your judgment is made of
Builds on:
- Know the parts of a metric definition: population, numerator, denominator, time window, and exclusions
- Identify a table's grain: what one row represents
Your Maestro asks you to use it on a new case and saves it as evidence when you do.
Understand
Understand AI in data work as an extension of your expertise, with you accountable for the result
Where I teach it: The Agent on Your Computer · Your Judgment, at Scale
Your Maestro asks you to explain it in your own words and saves it as evidence when you do.
Distinguish the model, harness, and context in an AI agent
Where I teach it: The Agent on Your Computer · Model, Harness, and Context
Your Maestro asks you to explain it in your own words and saves it as evidence when you do.
Explain how an agent works: it reads the context, uses tools, observes the results, and repeats
Where I teach it: The Agent on Your Computer · Model, Harness, and Context
Builds on:
- Distinguish the model, harness, and context in an AI agent
Your Maestro asks you to explain it in your own words and saves it as evidence when you do.
Understand that an analysis is an argument: a claim supported by evidence someone else can review
Where I teach it: The Agent on Your Computer · An Analysis Is an Argument
Your Maestro asks you to explain it in your own words and saves it as evidence when you do.
Recognize that decisions made during an analysis change the result, and that each one needs a reason
Where I teach it: What the Agent Decided for You · Two agents, two tables
Builds on:
- Understand that an analysis is an argument: a claim supported by evidence someone else can review
Your Maestro asks you to explain it in your own words and saves it as evidence when you do.
Read the record of what an agent did: the commands it ran, the files it changed, and the results it saw
Where I teach it: What the Agent Decided for You · What did the system do?
Builds on:
- Explain how an agent works: it reads the context, uses tools, observes the results, and repeats
Your Maestro asks you to explain it in your own words and saves it as evidence when you do.
Remember
Know how an agent reaches your data: commands, files, connectors, and credentials
Where I teach it: The Agent on Your Computer · How It Reaches Your Data
Your Maestro asks you to recognize and recall it and saves it as evidence when you do.
Know how an agent's response to a data task can miss what you need: decisions you didn't ask for, summaries that claim more than was verified, code that counts something else, and made-up values
Where I teach it: What the Agent Decided for You · Two agents, two tables
Your Maestro asks you to recognize and recall it and saves it as evidence when you do.
Name what makes data work defensible: traceable, reproducible, validated, and honest about its limitations
Where I teach it: Put It in Writing · Your quality bar
Your Maestro asks you to recognize and recall it and saves it as evidence when you do.
Know the parts of a metric definition: population, numerator, denominator, time window, and exclusions
Where I teach it: With Your Data · What your judgment is made of
Your Maestro asks you to recognize and recall it and saves it as evidence when you do.
Know what data provenance means: where a table came from, how the data was collected, and what was done to it
Where I teach it: With Your Data · Your session, with your data
Your Maestro asks you to recognize and recall it and saves it as evidence when you do.
Who is this course for?
For people who already work with data and want to give an AI agent work in their terminal while remaining accountable for the numbers they sign off on. You don't need to know statistics: knowing your data and your team is enough. You'll practice with the Stack Overflow survey and NASA's exoplanet catalog.
Ask your Maestro
Your Maestro is an AI that knows each course, page by page. Try it here free, in the free course "Check what AI gives you in three steps".
You: Where do I start?
Your Maestro: From what you already know. Tell me what you've done with data, and I'll start this page from there.
Asking about: Answers from the page "When an agent did it"
Only this browser keeps what you ask here. How it works
What we'll cover
The Agent on Your Computer
- Your Judgment, at Scale
- Set Up Your Environmentreference
- What Would the Agent Do?reference
- Model, Harness, and Context
- How It Reaches Your Data
- Your First Session
- An Analysis Is an Argument
What the Agent Decided for You
- Two agents, two tables
- What did the system do?
- What is one row?
- Three quick checks
- You made the decision
Ask Like an Expert, Review Like an Expert
- What the agent cannot know
- Specify the task
- Review the result against your specification
- With a short instruction and with a specificationreference
Put It in Writing
- What you know without saying it
- What you accept and what you send back
- A file the agent reads when it starts
- What you know about your data, in writing
- Your quality bar
With Your Data
- Now, with your datareference
- What your judgment is made of
- The question you can answer
- Before opening your data
- Your session, with your data
- What went wrong goes in the file
Compruébalo por otro camino
6 pages to come
Opens as soon as it's ready.
- Si el agente se revisa solo
- Cinco filas a mano
- Una respuesta que ya sabes
- ¿Qué cuenta este código?
- ¿De dónde sale el número?
- Por un camino que el agente no usó
Cuando se juntan dos tablas
4 pages to come
Opens as soon as it's ready.
- Tres tablas, una tienda
- Perfila antes de contar
- El join que duplica
- Los que faltan
Lo que tu número no dice
6 pages to come
Opens as soon as it's ready.
- ¿Necesito saber estadística?
- ¿Quién contestó?
- Más de cien por ciento
- El método que cabe en la pregunta
- Cambia una decisión
- La frase de los límites
De una sesión a un procedimiento
7 pages to come
Opens as soon as it's ready.
- Lo que se repite
- Misma pregunta, otra respuesta
- El agente escribe, Python calcula
- De sesión a script
- Pruebas para tus datos
- Empaqueta el procedimiento
- Un agente revisa a otro
Defiende tu análisis
5 pages to come
Opens as soon as it's ready.
- "¿Cómo lo sabes?"
- Sigue un número
- La nota de métodos
- La nota con el agente
- Proyecto final: ¿cómo lo sabes?
How long does it take?
3 to 5 hours
A guide: you go at your own pace, and the course waits where you left off.
Common questions
- What does an AI agent do with my data?
- An AI agent is a model that uses tools: it reads files, runs commands in your terminal, looks at the result and repeats. With data, it does the basic analysis well on its own. What the data does not tell you is your call, and so is which number you sign off.
- How do I review what an AI agent did?
- Use the check this course repeats from start to finish: what was counted and what was divided by. Then read the agent log, with the commands it ran and the files it changed, and compare the result with what you expect: order of magnitude, totals and ranges. You decide whether to accept it.
- Why does an agent like Claude Code code well but get data wrong?
- When you program, tests warn you when something breaks. With data, nothing does: the agent assumes what a row is, which rows to drop or what to divide by, and a wrong number still looks fine. So you ask for evidence: what it decided, which rows it used, what code it ran. Then check it against the data.
- Is it a one-time payment, and can I get a refund?
- It is a one-time payment, no subscription. You have 7 days from purchase to ask for a full refund, for any reason, by writing to hola@heymaestro.app. The current price appears next to the buy button. Your Maestro, a 1:1 AI, answers your questions throughout the course.
- Do I need a course, or can I learn this on my own?
- You can learn it on your own. The course gives you an ordered path with real data, exercises that test your judgment, and your Maestro answering questions in every lesson. Unsure? Start with a free course such as Your AI and Your Data. If it is not for you, you have 7 days to ask for a refund.
- Do I need to know statistics?
- No. What matters most when you review a result is understanding the question, the group it refers to and how the data was defined. Knowing your data and your team is enough. If you also know statistics, you use it in your criteria. If not, the course does not assume it.
- Is it worth learning this if AI tools change so fast?
- Yes, because what the course teaches does not depend on one tool: specifying a task, reviewing the result and writing down your judgment so the agent follows it. The tools are the example, and they change. Your judgment about your data is what stays with you.
- Do I need to know how to code or use the terminal?
- You don't need to know how to code. When you set up your environment, the course shows you how to open a terminal and gives you every command, with PowerShell versions for Windows. You don't write Python, you read it, and if a line is unclear you ask the agent. You work with a terminal agent: Claude Code, Codex CLI or Antigravity CLI.
- Does it work with Claude Code?
- Yes. Claude Code is one of the three terminal agents the course gives instructions for, along with Codex CLI and Antigravity CLI. The course works with any of them. If you already pay for a Claude plan, use Claude Code; it needs a Pro plan or higher, or API credits. Your Maestro helps with whichever you choose.
- What data do I practice with?
- You practice with two public tables: the 2024 Stack Overflow survey, which you download when you set up your environment, and NASA's exoplanet catalog. In Module 5, "With Your Data", you use a table from your own work, for example an exported CSV. If you can't take data out of your work, you pick a public table you know well.
How do I check what AI does? I teach it in Your AI and Your Data: Don't Let It Make Up the Number and Check what AI gives you in three steps.
What you get
- 10 modules with 55 pages. I'll open the next modules as each one is ready.
Your Maestro for the whole course, who always knows which page you're on: you open it from there.
It works from this course's skills, the ones above.
Ask it how you're doing, and it tells you what you've already shown and what comes next.
Words you'll meet
In the lessons, tap one to see what it means, or ask your Maestro about it.
Who's teaching you
Sergio Sánchez
I teach this because working with data has changed. Today's models are very good at basic analysis: one table and a clear question. And every version gets better. You no longer do every calculation yourself. You decide what to ask, which of several valid answers your report needs, and how you know it's right. That's where what you already know comes in.
I'm Sergio, chekos online. I'm a data architect, and I've spent more than ten years working with data in the Bay Area, mostly at mission-driven organizations: policy research first, then education, and now early childhood services. I grew up in Tijuana. In 2018 I started tacosdedatos to share what I was learning, and since then it's been a blog, a podcast, courses, guides, and a community. These days I spend a lot of my time building with AI agents and writing about how I organize my work around them. I share what I've learned; you take what's useful and make it yours.
See you in the course
Agentic AI for Data: Your Judgment, at Scale