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.
32 skills · 5 modules
$1
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.
- Synthesize
- Analyze
- Apply
- Understand
- Remember
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.
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
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.
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.
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 "You already do data engineering"
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
Common questions
- Do I need to know statistics to make my criteria more rigorous?
- No. What matters most when reviewing a result is understanding the question, the group it refers to, and how the data was defined. The check you'll repeat throughout the course is "what was counted, and what was it divided by?" Knowing your data and your team is enough to do that. If you also know statistics, you can use it in your criteria; if you don't, the course doesn't assume you do.
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
- 5 modules and 27 pages, open whenever you want them.
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've worked with data for about ten years, mostly at mission-driven organizations: policy research first, then education, and now early childhood services. 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