The Future of AI Agents Depend on the Data You Don’t See
This week: a free masterclass on data readiness for AI Agents, a challenge to spot invisible data, Bezos’ take on the "AI bubble", and how data engineers are becoming the architects of the AI era.
Hey AgentBuilders,
Thank you for reading this week’s newsletter.
Today We Cover:
MasterClass: Assess your enterprise’s data readiness for AI Agents
Weekly Challenge: Think like an AI system designer
Role Spotlight: How Data Engineers are evolving in the era of AI
AI Watch: Jeff Bezos on the “AI bubble” that might just be good for society
Community Note: Help shape the future of this newsletter
MasterClass: Assessing Data Product Readiness for AI Agents
This week I dropped a masterclass in the Modern Data 101 community. I have been advising Enterprise leaders on how to think about the evolution of the data infrastructure in readiness for the new consumers of data - the AI Agents. I have written a few blogs on this topic (links below) and this masterclass is combination of insights I have been gathering from my reading of where most enterprises are today and where they want to be as their business evolves in the age of AI.
This is a free class, all you need to do is go to the link, pop your email and watch the video.
If you want to dive deeper, here are the links to the few blogs I have written earlier this year:
Chapter 1: Data Products in AI Strategy
Chapter 2: Why Your AI Agents will Struggle?
Chapter 3: Is your Data ready for AI Agents? A Hands-on Framework to find out
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Weekly Challenge: The Data Beneath the Surface
This week, we’ve got something interesting for you - and yes, it’s about data.
We have designed this to make you think like a system designer.
🧠 The Invisible Data Challenge
Every great AI agent depends on data you don’t see.
Not the obvious stuff - but the contextual breadcrumbs that make it actually useful.
Think about an email summarization agent.
Sure, it needs the email text. But what else?
The relationship between sender and recipient (Is this my boss or a spammer?)
The user’s communication style (Formal? Friendly?)
The intent behind the message (Is this asking for action or FYI?)
The urgency level (Is this blocking someone’s work?)
Your task:
Pick any AI agent idea - a travel planner, resume matcher, calendar assistant, anything - and list 5 pieces of invisible data it would quietly need to do its job well.
How to Share Your Response
Post your challenge output in the AgentBuild Slack (or reply to this email if you’re reading on Substack).
Here’s the format you can follow:
Agent Idea: [Name of your agent]
💡 Invisible Data:
1. ...
2. ...
3. ...
4. ...
5. ...👉 Tell me how it made you think about AI Agents differently.
This Week in AI
Jeff Bezos warns about the AI bubble
Jeff Bezos believes AI exists in an “industrial bubble” where overexcited investors fund both good and bad ideas indiscriminately, creating valuations disconnected from business fundamentals. However, he emphasizes that unlike financial bubbles, this industrial bubble will ultimately benefit society. “AI is real and it is going to change every industry,” Bezos stated, predicting “gigantic” societal benefits despite inevitable investment losses. Read the CNBC report or watch the video below.
🟧 My Take: Look, Bezos gets it - we’re basically throwing money at anything with “AI” in the pitch deck right now. Sure, tons of startups will crash and burn, but here’s the thing: all that crazy spending is actually funding the next big breakthroughs we wouldn’t see otherwise.
Job Role Spotlight - The Data Engineer
You know why I love Data Engineers? Because I was one. I’ve spent years knee-deep in ETL jobs, broken pipelines, and when something in production would inevitably break on a Friday afternoon. Lol.
I know something for sure - good AI starts with great data. So when I talk about AI agents today, it’s not theory - it’s built on the same foundations data engineers quietly perfect every single day.
Data Engineer: The Unsung Hero, or the Next AI Architect?
Many data engineers today are insecure, they don’t get the appreciation they deserve as AI takes the spotlight. They are insecure as AI tools can now clean data, build pipelines, even write SQL on their own.
I get this question often,
What’s really happening with Data Engineering role?
To be honest, I think we’re seeing a shift, not a disappearance.
The data engineer of yesterday moved data from one place to another. The data engineer of tomorrow (or today depending on where you work) will make data ready for AI.
They’ll focus on meaning, not just movement.
They’ll make sure data is not just available, but understandable - for both humans and AI Agents.
So if you’re a Data Engineer today, here’s my advice:
Start learning how AI actually uses your data - embeddings, vector databases, feature stores. Because soon, your pipelines won’t just feed dashboards, they’ll feed AI agents that reason, plan, and make decisions.
Now let’s imagine how this role might evolve in the next few years 👇
🟧 The Data Product Engineer.
This person builds reusable, ready-to-consume data products.
Think of it like packaging data so anyone - a human or an AI model - can plug in and use it safely.
🟧 The VectorOps Engineer.
This one’s new. They handle all the behind-the-scenes magic that lets AI “remember” things - managing embeddings, retrieval, and optimization.
It’s like moving from ETL (Extract-Transform-Load) to ETV - Extract, Transform, Vectorize.
🟧 The Data Trust Engineer.
This role is about making sure the data is accurate, explainable, and compliant.
Because in the AI world, bad data doesn’t just break reports - it breaks trust.
Okay, the truth is, we might not see job roles titled that way - those roles would still possibly be called Data Engineers, but you get the idea.
Data Engineering is becoming the control layer of AI.
The people who can move from pipelines to products, from dashboards to agents - they’re the ones who’ll lead the next decade of AI infrastructure.
👉 Are you interested in preparing for this shift?
I am putting together a roadmap to help you bridge the gap. If you are interested, leave a comment “INTERESTED“.
Evolving the Newsletter
This newsletter is more than updates - it is our shared notebook. I want it to reflect what you find most valuable: insights, playbooks, diagrams, or maybe even member spotlights.
👉 Drop me a note, comment, or share your suggestion anytime.
Your feedback will shape how this evolves.
Found this useful? Ask your friends to join.
We have so much planned for the community - can’t wait to share more soon.