Imagine being able to deploy an AI agent and getting a return of over $100m from that single deployment. My guest today, Nikunj Bajaj, has facilitated that multiple times! Lots to learn from him, enjoy!
Nikunj:
• CEO and co-founder of TrueFoundry, a Bay Area-based startup that has raised over $20m to solve the thorniest problems that enterprises face when deploying agents.
• His clients include demanding organizations like NVIDIA and Siemens.
• Was previously ML tech lead at Facebook.
• Holds a master's in computer science from University of California, Berkeley.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Filtering by Tag: #agenticAI
How to Build AI-First Organizations, with Jacob Miller and Jeremy Mumford
After today's fun episode with Jacob and Jeremy — authors of the brand-new book "Architected Intelligence" — you’ll have all the key info to build successful AI features, AI products and AI-first companies. Enjoy!
Jeremy Mumford and Jacob Miller serve as Lead AI Engineer and Vice President of Platform Intelligence, respectively, at Pattern, a giant Utah-based tech company that IPO’ed on the Nasdaq exchange about six months ago.
Jacob and Jeremy's brand-new "Architected Intelligence" book was published by Wiley and this episode focuses almost exclusively on this invaluable book.
Episode highlights include:
• The "User Agnosticism Tenet", which means designing products and processes so they can be executed equally well by a human, an AI agent, or any hybrid combo.
• The shift in the "define-build-feedback" loop today where "building" is no longer the bottleneck, which means "definition" and "feedback" are where teams win or lose.
• Why workflows are deterministic, predictable, and cheaper than agents, and why the natural progression is skills first, then workflows, and only then agents.
• Why data engineering is the bedrock of AI engineering.
• Why velocity is the only durable moat in a world where everyone has access to the same frontier models.
Thanks to podcast superfan Jonathan Bown for recommending Jeremy and Jacob as guests!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Pair Programming with AI in Your Python Notebook, with Dr. Trevor Manz
Exceptional technical episode today with Dr. Trevor Manz on "marimo Pair", an actually!) game-changing pair-programming A.I.-agent companion that lifts heavy loads within your Python data-science notebook.
More on Trevor:
• 27-time NCAA Swimming All-American & National Champion.
• Master's in Computational Biology from University of Cambridge.
• PhD in Bioinformatics from Harvard University.
• Creator of the popular open-source "anywidget" project (amongst many others, particularly in visualizing bioinformatics data, e.g., genomics data).
• Now a founding engineer at marimo.io, where he is leading the charge on marimo Pair.
Seriously, marimo Pair is unreal. A complete reimagining of what's possible in a Jupyter notebook-style environment in the agentic A.I. era. You will hear (and see) my mind explode in this episode!
We also discuss:
• Agent skills.
• Recursive language models.
• A number of other open-source projects, largely in data viz/analysis.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Web Summit Vancouver 2026
"Collision" has grown and re-branded as "Web Summit Vancouver". I'm looking forward to experiencing the new brand for the first time next week! See you there? Here's where you can catch me:
• Tue May 12 at 11am: Mentor Hours on "scaling your startup"
• Wed May 13 at 1:30pm: Delivering my agentic A.I. talk ("Something Big is Happening") on the "A.I. Summit" stage.
• Wed May 13 at 1:50pm: Emceeing the "A.I. Summit" stage all afternoon.
More on Web Summit Vancouver:
• Taking place May 11–14 at the Vancouver Convention Centre.
• It's the second year in a row the conference, under this new brand, has taken place (the previous "Collision"-branded event was held annually in Toronto and the photo in this post is from a talk I gave there in 2024).
• Connects over 35,000 startup founders, investors and industry leaders to discuss A.I., entrepreneurship and tech trends.
Security for Mythos-Era Agentic Risks, with Rubrik’s Anneka Gupta and Cal Al-Dhubaib
Mythos finds security vulnerabilities at ~100X the rate of publicly available models, and comparable open-weight models are ~6 months away. Scary? Thankfully my guests today, Anneka and Cal, have solutions!
Anneka:
• Chief Product Officer at Rubrik.
• Lecturer in Product Management at Stanford University.
• Climbed the ladder from software engineer to President (!!) during an 11-year tenure at LiveRamp.
• Holds a degree in math and computational sciences from Stanford.
Cal:
• Principal Technologist at Rubrik.
• Formerly founder and CEO of Pandata, which was acquired by Further.
• Highly sought-after keynote speaker.
• Holds a degree in data science from Case Western Reserve University.
This is an exceptional episode with two brilliant, entertaining and highly knowledgeable guests. It can be enjoyed by anyone! In it, they cover:
• How Anthropic's Mythos model can be pointed at a code repository and autonomously surface every vulnerability inside it, and how Anthropic itself estimates Mythos-class capabilities will reach other labs within six to eighteen months, with open-weight versions likely to follow.
• How code-gen models make it easy for attackers by scaling up their capabilities... and by vibe-coders not being aware of vulnerabilities they have!
• How Rubrik's Agent Cloud delivers three pillars of resilience: visibility into every agent in your environment, governance and runtime control through the SAGE small language model, and remediation through Agent Rewind.
• Why the next wave of knowledge work is inherently cross-functional, with A.I. attorneys, security pros, and data scientists all needing shared literacy in A.I. risk.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Building Hardware is Hard but AI Agents Help, with Kishore Subramanian
In software, when something goes wrong, you push a patch. In hardware? Oooph. You're dealing with big headaches and huge costs. Thankfully, my guest today — Kishore Subramanian — is using AI to transform the way physical products get built for the better.
Kishore:
• Is CTO of Propel Software, a Bay Area company that combines product data with agentic AI to make the production of physical hardware (including high tech and medtech devices) as seamless as possible.
• Prior to Propel, held senior engineering roles at Google, where he worked on Google Assistant, so he has particularly rich experience with agent development.
• Holds a degree in electronics, computers and process control… as well as a 200-hour yoga-teaching certificate!
In this episode, Kishore covers:
• How product lifecycle management (PLM) is the system that takes a physical product from concept all the way to the customer and beyond.
• How AI agents can review engineering change orders — the hardware equivalent of pull requests — to flag risks, compliance gaps, and downstream impacts before they become expensive problems.
• How Propel built their AI platform, Propel One, on top of Salesforce's Agentforce 360 Platform, which gave them security, governance, data infrastructure, and a reasoning engine out of the box, allowing them to ship in about six months.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The Four Types of Memory Every AI Agent Needs, with Richmond Alake
To build an effective A.I. agent, getting its memory right is essential. In today's episode, our agent-memory guide is brilliant (and very funny!) machine-learning architect and engineer, Richmond Alake.
More on Richmond:
• Director of A.I. developer experience at Oracle.
• Previously roles include: staff developer advocate for AI/ML at MongoDB, ML architect at Slalom, writer for NVIDIA and computer-vision engineer at Loveshark.
• Holds a master's in ML and robotics from the University of Surrey.
In this episode, Richmond magnificently covers:
• How agent memory is the encapsulation of systems (embedding models, rerankers, databases, and LLMs) that allow AI agents to learn and adapt with new information over time, rather than starting from scratch every session.
• The four types of agent memory (all drawn from human cognition).
• Memory-first agent harnesses.
• Predictions for a flattening of AI engineering roles, where the future developer will need end-to-end understanding of the full agent stack.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Building AI Agents Where 99.9% Accuracy Isn't Good Enough, with Raju Malhotra
The headlines shout “SaaSpocalypse,” but I don’t buy it. Neither does my guest today, Raju Malhotra, who argues that, thanks to humans collaborating with agents on optimized workflows, the SaaS opportunity is now far bigger than ever before.
More on Raju:
Chief Product & Technology Officer (CPTO) at Certinia, an Austin, Texas-based company whose Professional Services Automation software is used by over 1400 organizations around the world.
Was previously CPTO at PAR Technology and Khoros.
Earlier, spent 12 years at Microsoft working on cornerstone products like Visual Studio .NET.
Holds an MBA from The Wharton School and an undergrad in computer engineering.
In this episode, we cover:
Traditional SaaS isn't dead… instead, it's evolving into a hybrid of SaaS plus agentic capabilities, where humans and agents work together in optimized workflows.
By removing the human-skills constraint from professional services delivery, the agentic revolution could expand the addressable market by 7-8X.
The Agentforce 360 platform (by combining probabilistic AI with deterministic logic and guardrails) empowers innovators to turn their ideas into scalable software businesses, allowing businesses like Certinia to bring AI agents securely and reliably to their customers, even in sensitive industries where 0.1% error rates are unacceptable.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.