81k | Director, Data & Analytics/ AI @ Gartner

Summary

Making state-of-the-art Artificial Intelligence concepts accessible and profitable to professionals from any background for over 35 years!

Highlights
-  Veteran innovator with 3 decades of experience leading state-of-the-art AI and Analytics ventures
-  98% of students surveyed would recommend me to peers
-  Generative AI/ Agentic AI expert-  ChatGPT, Claude, Copilot, Perplexity, Lovable, n8n, more
-  Experienced AI instructor for Cisco, LinkedIn Learning, University of Pennsylvania, & Simplilearn
-  Senior AI Curriculum Advisor for Graduate School of Education at Ivy League university UPenn
-  Winner, International Society of Data Scientists Outstanding Voice Award
-  Social media leader with over 107k LinkedIn followers


Selected Clients
 
-  3M
-  Audi
-  Biogen 
-  Booz Allen Hamilton
-  Bristol-Myers Squibb
-  Cisco
-  Citicorp
-  Franklin Templeton
-  Gartner/ Evanta
-  Harvard Innovation Labs’ Experfy
-  IBM
-  International Olympic Committee
-  Los Angeles Times
-  Mercedes
-  Morgan Stanley
-  Royal Bank of Canada
-  St. Jude’s Children’s Hospital
-  Wells Fargo
-  US Homeland Security
-  US Veterans Administration 
-  Yale University

Partial Skills Inventory
 
-  AIOps
-  Accountable AI/ Ethics
-  Anomaly Detection
-  Anthropic/ Claude
-  Artificial Intelligence
-  AWS
-  Brokerage Firms
-  Client Management
-  Consulting
-  Compelling Presentations
-  Compliance Regs and Frameworks
-  Curriculum Development
-  Dashboards
-  Data Storytelling
-  Data Visualization
-  Education
-  Explainable AI
-  Feature Engineering
-  Financial Services
-  Fraud Detection
-  Gap Analysis
-  Generative AI
-  GitHub Models
-  Google Cloud
-  Google Gemini
-  Insurance
-  KPI's and Metrics
-  Lovable AI
-  Machine Learning
-  Microsoft Azure
-  MLOps
-  Model Ensembles
-  Nano Banana
-  OpenAI/ ChatGPT
-  Orange Data Mining
-  Perplexity
-  Postgres SQL
-  Professional Development
-  Research
-  Retail
-  Retention and Attrition
-  SPSS
-  SQL Server
-  Underwriting

T. Scott Clendaniel

🔬 #ArtificialIntelligence 𝗛𝗶𝗿𝗶𝗻𝗴- 𝗝𝘂𝘀𝘁 𝗪𝗵𝗮𝘁 𝗔𝗥𝗘 𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗿𝘀 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗙𝗼𝗿?

The Terrible Facts:
- Artificial Intelligence needs a lot more experts.
- Tons of qualified professionals aren't getting hired.
- The expectations of hiring managers are just out of control.

If you're a hiring manager, please STOP looking for an entire department in just one employee.  It simply doesn't work that way.  You need a team.

Personally, I find the job search to be truly dreadful. It is exhausting, soul-crushing, depressing, dispiriting- actually, I will run out time before I run of negative emotions on this topic!  

But I hold out hope that one day everyone else will figure this out!

Please follow me for more analysis! 

- T. Scott Clendaniel

#AI Training for Executives | 108K LinkedIn Followers

It’s been a while since I started my role at Nautilus-AI.com as a Chief Learning Officer, but I wanted to share this update with everyone.

🔬 #ArtificialIntelligence: 𝗪𝗵𝗲𝗿𝗲 𝗗𝗼 𝗬𝗢𝗨 "𝗔𝗜"?

Our friends at GiGadgets researched something extraordinarily fun!

AI isn't the only place where innovation occurs.  Sometimes, the very desks we use to work with GenAI, Machine Learning, or any of the rest of AI occurs can show us remarkable creativity.

Take a look and smile!

- T. Scott Clendaniel

🔬 True Wisdom via Michael Tchuindjang!

Cybersecurity Thought Leader | Bridging Academia & Industry | Mentor for Startups & Aspiring Cyber Pros | Board Member Driving Cyber Impact | Associate Lecturer in Cybersecurity

Boardroom AI Strategy Meeting (2026 Edition) 



CEO: Who are we?
Boardroom: CEOs

CEO: What do we want?
Boardroom: AI!

CEO: AI to do what exactly?
Boardroom: We don’t know

CEO: When do we want it?
Boardroom: RIGHT NOW

If you’ve ever sat in a boardroom lately, this probably feels uncomfortably familiar.

AI has become the new magic word:

Replace “digital transformation”

Upgrade from “blockchain”

Stronger than “cloud-first”

Don’t get me wrong, AI is here to stay.

But AI without a clear problem, data readiness, risk assessment, or ethics is just… expensive enthusiasm.

Real innovation starts when we ask better questions:

What problem are we solving?
What risks are we introducing?
Who is accountable when AI fails?
Are humans still in the loop?

Otherwise, we’re not building AI strategies, we’re just speed-running confusion with a GPU 😄

Credits to @Cyber Writes ✍.

#ai #cybersecurity #security #leadership #aihype #responsibleai #jobs #hiringandpromotion #hiring #techhumor #humor #memes #klex

🔬 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜/ 𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗶𝗻𝗴, 𝗦𝘁𝗲𝗽-𝗕𝘆-𝗦𝘁𝗲𝗽:
𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗔 𝗖𝘂𝘀𝘁𝗼𝗺 𝗦𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁 𝗔𝗽𝗽!

What happens when you're building Artificial Intelligence training and a process for execution?  You need to be able to manage all you video assets.  

But what if you want to go further?  What if you want to go beyond Google Sheets and Microsoft Excel and create your own app?

Well it helps if you're a genius like Tianyu Xu!  🤣 

However, I don't have that kind of luck.  So instead, I'm going to share his amazing video.  Take notes.  

Then #SAVE and #SHARE this with your teams and peers!

Happy AI-ing!

T. Scott Clendaniel

🎉 𝗚𝗲𝗻𝗔𝗜 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 𝗪𝗶𝘁𝗵 𝗧𝗵𝗲 𝗣𝗲𝗿𝗶𝗼𝗱𝗶𝗰 𝗧𝗮𝗯𝗹𝗲! 

Here is the magic of video!

Generative AI can build entire new worlds.  This is one I absolutely love.  Mini planets made from elements in the period table:


𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀

▶ 1.  Copper
▶ 2.  Mercury
▶ 3.  Calcium

▶ 4.  Magnesium
▶ 5.  Palladium
▶ 6.  Uranium

▶ 7.  Arsen
▶ 8.  Lead
▶ 9.  Chlorine

Happy Chemistry

Please SAVE and SHARE!  You can follow me: T. Scott Clendaniel

🔬 𝗧𝗲𝗮𝗰𝗵𝗶𝗻𝗴 #AI: 𝗦𝘁𝗲𝗮𝗹𝗶𝗻𝗴 𝗮 𝗩𝗶𝗱𝗲𝗼 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗳𝗿𝗼𝗺 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝘆!

Picture this: you’re holding a cube. It’s got 8 corners, 12 edges, and 6 faces. 

Add the corners and faces, subtract the edges, and you always get 2. Every time. That’s Euler’s Polyhedron Formula—V - E + F = 2. It works for a cube, a pyramid, even a soccer ball.

So why does that matter? Because it’s a reminder that under all complexity, there’s structure. Euler didn’t just find a number—he found a pattern that connects everything.

That’s how I think about explaining AI. The goal isn’t to drown people in details; it’s to show the structure beneath the surface. When you connect data (edges), decisions (vertices), and outcomes (faces) just right, people get it.

The best teachers—whether they’re explaining geometry or generative AI—make the complex feel simple and the simple feel profound. That’s how real understanding sticks.

Happy Learning,

T. Scott Clendaniel

🔬 #ArtificialIntelligence Innovation: Human-Powered Helicopter!  

Human-powered helicopter hovers for more than a minute, defying sceptics' claims it was scientifically impossible!


𝗧𝗵𝗲 𝗖𝗼𝗻𝘁𝗲𝘀𝘁

The Igor I Sikorsky Human Powered Helicopter Competition was launched in 1980 by the American Helicopter Society (AHS). The rules stated that the $250,000 prize could only be won by a human-powered machine capable of hovering for at least 60 seconds, reach at least 3 metres in height, and stay within an area 10 metres by 10 metres.

On Thursday, the award was handed over to the AeroVelo project, led by Todd Reichert and Cameron Robertson. Their winning flying machine, called Atlas, has a set of 20-metre rotors at each corner of a square frame 50 metres on each side, and is powered by a bicycle at the centre of the square.

Thanks to the use of lightweight materials, such as carbon fibre, the whole setup only weighs only 55kg despite its huge size. The team describe the Atlas machine in detail in a claim for a world record to the FAI (Fédération Aéronautique Internationale).

"This isn't something that you're going to commute to work in any time soon, but it's an exercise in really pushing the limits on what's physically possible, and what you can do with lightweight materials and really creative design," Reichert told the Canadian Press. "Winning this competition really is a catalyst to keep doing the things we love. Our goal is to take on projects that really inspire people to follow big dreams."


𝗧𝗵𝗲 𝗪𝗶𝗻𝗻𝗲𝗿𝘀

The AeroVelo team started work on Atlas in January 2012 and flight testing began in August. They refined the design over the following months and, despite two big crashes that destroyed much of their work, Reichert piloted the machine to make the record-breaking flight on 13 June (if you're reading on a mobile device click here to see the flight).

At the presentation of the prize on Thursday, AHS executive director, Mike Hirschberg, said that many in the world's vertical flight community had thought the Sikorsky challenge was impossible. "Several studies 'proved' that it was in fact scientifically impossible," he said. "A human being simply could not generate enough power for long enough to keep a human-powered helicopter in the air for 60 seconds, much less reach 3 metres. And when you add the weight of a control system to keep it hovering over a 10-metre box, it made it even more impossible.

"Well, it took a third of a century to prove those sceptics wrong. It took that long for the state of the art of vertical flight to see significant technological advances in lightweight structures, computer-aided design, aeromechanics, and multidisciplinary design optimisation."

Gotta love innovation!

T. Scott Clendaniel

🔬 #ArtificialIntelligence 𝗶𝗻 𝗗𝗲𝘀𝗶𝗴𝗻: 𝗣𝗿𝗼𝘀𝘁𝗵𝗲𝘁𝗶𝗰 𝗟𝗶𝗺𝗯𝘀!

SoftFoot Pro is a cutting-edge, motorless prosthetic foot developed by Italian researchers, mimicking human foot anatomy with flexible chains and titanium to adapt passively to terrain, offering better stability, natural movement (like bending), and improved energy efficiency for amputees, and also serving as a robotic component. 


---
𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 & 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀
---

▶▶ 𝗛𝘂𝗺𝗮𝗻-𝗜𝗻𝘀𝗽𝗶𝗿𝗲𝗱 𝗗𝗲𝘀𝗶𝗴𝗻
Emulates the shape and structures of the human foot, including the plantar fascia, using flexible components.

▶▶ 𝗣𝗮𝘀𝘀𝗶𝘃𝗲 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻
Its flexible sole and chain system allow it to deform and widen its contact area, naturally adapting to rough or slippery surfaces without motors.

▶▶ 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗠𝗼𝗯𝗶𝗹𝗶𝘁𝘆
Enhances stability, allows for natural actions like bending down, and makes navigating stairs easier for users.

▶▶ 𝗟𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁 & 𝗦𝘁𝗿𝗼𝗻𝗴
Weighs about 450g and supports up to 100kg, using titanium/aluminum and high-strength plastic chains.

Have a great week!

Follow me for more tips: T. Scott Clendaniel

🔬  #GenAI- 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀!

CNN's are the basis for many tools, including most of the computer vision, image, and video generation for Generative AI.  Here's how to teach it to your team.


𝗧𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀

A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio.

Convolution-based networks are the de-facto standard in deep learning-based approaches to computer vision and image processing, and have only recently have been replaced -- in some cases -- by newer deep learning architectures such as the transformer. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections.

For example, for each neuron in the fully-connected layer, 10,000 weights would be required for processing an image sized 100 × 100 pixels. However, applying cascaded convolution (or cross-correlation) kernels, only 25 neurons are required to process 5x5-sized tiles. Higher-layer features are extracted from wider context windows, compared to lower-layer features.


𝗦𝗼𝗺𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗖𝗡𝗡'𝘀

▶▶   image and video recognition
▶▶   recommender systems
▶▶   image classification
▶▶   image segmentation

▶▶   medical image analysis
▶▶   natural language processing
▶▶   brain–computer interfaces
▶▶   financial time series

CNNs are also known as shift invariant or space invariant artificial neural networks, based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps.  Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input.

Via Wikipedia ==> https://bit.ly/4126GDT

Happy Learning,

T. Scott Clendaniel

🔬 "𝗜𝗳 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗜𝘀 𝗕𝗼𝗿𝗶𝗻𝗴, 𝗜𝘁'𝘀 𝗕𝗲𝗶𝗻𝗴 𝗧𝗮𝘂𝗴𝗵𝘁 𝗕𝘆 𝗧𝗵𝗲 𝗪𝗿𝗼𝗻𝗴 𝗣𝗲𝗿𝘀𝗼𝗻!"

Okay, so Richard Feynman wrote the original quote about Science in general, not about Artificial Intelligence specifically.  So I confess.  I took a wee bit of dramatic license!

But UNILAD captured this fantastic video of Tatiana Erukhimova in here element teaching Physics.

And quite frankly, AI has a lot more possibilities for interactive content!

So no excuses!

And if you need help, reach out to me directly!  🤗 

- T. Scott Clendaniel




🎥 credits UNILAD/ Texas A&M University

JLL - Jones Lang LaSalle - Group Manager Top 50 Most Influential Leaders of 2025

“Our similarities bring us to common ground; Our differences allow us to be fascinated by each other.”

~ Tom Robbins ~

🔬 #AI #Education:
Introducing 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 demo- with 𝗣𝗨𝗣𝗣𝗜𝗘𝗦! 🐶🐶🐶

In the world of AI, Reinforcement Learning is truly a star, and the basis of things like self-driving cars/ autonomous vehicles.

It contains a few elements:

▶  Problem to be solved
▶  An agent, who is observed trying to solve the problem
▶  Reward/ penalty functions
▶  Scoring system

Just like these adorable puppies, who serve as our agents, trying to learn how to reach the treats, our agents try to figure out the problem.  Each puppy's success or failure is recorded, and the reinforcement learning system eventually solves the puzzle!

Enjoy!

T. Scott Clendaniel

🤗 𝗦𝗼𝗺𝗲 𝗚𝗼𝗼𝗱 𝗡𝗲𝘄𝘀- 𝗪𝗮𝘀𝘁𝗲 𝗪𝗮𝘁𝗲𝗿 𝗕𝗼𝘁𝘁𝗹𝗲𝘀 𝗕𝗲𝗰𝗼𝗺𝗲 𝗙𝗿𝗲𝗲 𝗟𝗶𝗴𝗵𝘁𝗶𝗻𝗴!


𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄

Liter of Light is an open source design for a low-cost light tube (or deck prism or vault light) that refracts solar light to provide daytime interior lighting for dwellings with thin roofs. 

Daylighting is cheaper than using indoor electric lights during the day. The device is simple: a transparent two-liter bottle is filled with water plus a little bleach to inhibit algal growth and fitted into a hole in a roof. 

The device functions like a deck prism: during daytime the water inside the bottle refracts sunlight, delivering about as much light as a 40–60 watt incandescent bulb to the interior. A properly installed solar bottle can last up to 5 years.


𝗛𝗶𝘀𝘁𝗼𝗿𝘆

The use of plastic bottles in this way to provide indoor lighting from daylight was developed by Alfredo Moser of Brazil. Using the technology as a social enterprise was first launched in the Philippines by Illac Diaz under the My Shelter Foundation in April 2011. In order to help the idea to grow sustainably, Diaz implemented a "local entrepreneur" business model whereby bottle bulbs are assembled and installed by local people, who can earn a small income for their work. 


𝗥𝗲𝘀𝘂𝗹𝘁𝘀

Within months, the organization expanded from one carpenter and one set of tools in one community in San Pedro, Laguna to 15,000 solar bottle bulb installations in twenty cities around the Philippines, and began to inspire local initiatives around the world. MyShelter Foundation also established a training center that conducts workshops with youth, business companies, and other groups who are interested in volunteering their time to build lights in their communities.

In less than a year since inception, over 200,000 bottle bulbs were installed in communities around the world. Liter of Light had a goal to light up 1 million homes by the end of 2015.

Enjoy,

T. Scott Clendaniel

🔬 𝗠𝗮𝗸𝗲 𝗬𝗼𝘂𝗿 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗖𝗼𝗺𝗲 𝗔𝗹𝗶𝘃𝗲!

The art of surprise will help your learners stay intrigued as you go through your training!


𝗖𝗮𝗻 𝗬𝗢𝗨 𝗳𝗶𝗴𝘂𝗿𝗲 𝗼𝘂𝘁 𝘁𝗵𝗲 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗲 𝗕𝗘𝗙𝗢𝗥𝗘 𝘆𝗼𝘂 𝗰𝗹𝗶𝗰𝗸 𝗽𝗹𝗮𝘆?  Tell me in the comments below!

Please make sure to #SAVE and #REPOST so your followers can take advantage of the advice!

- T. Scott Clendaniel

AI Product Builder @ NanoKernel | Generative AI, AI Agents, AIoT, Responsible AI, AI Product Management | Ex-Apple, Accenture, Cognizant, Verizon, AT&T | I help companies build standout Next-Gen AI Solutions

I help visionary companies build standout AI Products. 

I am seeking a Senior IC AI Product Manager role at established AI-focused companies. 

I am a transformative Product and IT Executive, blending innovation with leadership across every role I've embraced. My journey from the technical trenches to the strategic echelons of Product Leadership, Solutions Architecture, and Enterprise Data Strategy has been fueled by an insatiable drive for growth, an unwavering focus on customer success, and a passion for adapting and thriving in diverse organizational landscapes.

From Fortune 50 giants to agile startups, my path has been marked by over 8 groundbreaking product launches and more than 20 transformative project deliveries. At the heart of these achievements lies a deep-seated belief in the power of innovative thinking, strategic execution, and the magic of collaboration across domains and disciplines. 

My mantra? Value creation, operational excellence, and nurturing talent are the cornerstones of my transformative leadership. 

My commitment to thought leadership has led me to author several books on AI and Data, secure several patents, and contribute to the knowledge pool with articles and papers featured in renowned journals, blogs, and media channels. Dive into my insights on LinkedIn and Medium at https://harsha-srivatsa.medium.com

Education is an ongoing journey for me, enriched by formal degrees in Engineering, Math, Computer Science, and Management, alongside continuous learning from prestigious institutions like MIT, Stanford, Kellogg, and IDEOU. This foundation empowers me to lead at the intersection of technology and business, navigating the complexities of AI, IoT, Cloud Platforms, and beyond with ease and insight.

My commitment extends beyond the professional realm. As a High School Educator with Microsoft's TEALS Program, a Master Gardener certified by University of California, a mentor for immigrants through Upwardly Global, and a volunteer with the Coast Guard Auxiliary, I believe in giving back, sharing knowledge, and supporting communities. I am a believer in the transformative power of technology, education, and community service. Let’s connect, collaborate, and create impact together.

Email:  harsha.srivatsa at connectwithharsha.info | Website: https://www.connectwithharsha.me

Harsha Srivatsa

Whoa! I just realized that my network connection crossed 10,000 recently! 
I am elated as well as overwhelmed that the connections count crossed the five figure mark. 

There has been a spurt in connection requests both incoming and sent by me. Many times I have not had the chance to personalize connection requests (LinkedIn on Mobile just sends the request) so I thank those who just accepted a blank connection request. 

Shoutout to my latest connection Ridhima Ranka as I write. 

Thanks you Brian Kalis and Brian Kemler for accepting my recent connection requests. 

I know my network is large but I shall do my best to make our connection meaningful and value added. 

Please DM or reach out if you want to chat with me, I can share what I know and help in any way. I shall surely put my priority and focus on growing our interactions in a meaningful way. 

As I mentioned earlier, following my mentor Ha Nguyen advice, I took the resolution to be a Go-Giver in 2026. I intend sincerely to do that. 

May we all live in interesting times.

Senior AI Product Manager | Generative AI, AI Agents, AIoT, Responsible AI, AI Product Strategy | Ex-Apple, Accenture, Cognizant | I help companies build, debug and launch standout AI products

I just applied to OpenAI Grove 🌱 https://lnkd.in/g2-ED-E8

From what I understand, Grove isn’t a traditional accelerator. It’s a 5-week builder program designed for people at the very start of the company-building journey—bringing together a small, high-signal cohort (~15 people) with workshops, office hours, and mentorship from OpenAI’s technical leaders, plus time to co-build and pressure-test ideas. 

Why I applied: because the next wave of AI isn’t only an algorithm story. It’s a physics story.
Power. Water. Cooling. Land. Community resources.

As AI infrastructure scales, we’re already seeing grid constraints, water stress, and real policy/community pushback. And yet… we’re also moving toward an AI++ world where AI spreads into edge, robotics, and cyber-physical systems. We can’t unlock that future with “more GPUs” alone. We need responsible scaling.

So my application is about what I’m calling Resource-Responsible AI:
 a Resource Ledger + “GreenOps for AI” cockpit that helps AI teams measure, attribute, and reduce energy / carbon / water footprint across training, inference, and agentic workflows—turning sustainability from a slide into an operational KPI.

This idea came straight from my work in AI Micro Data Centers—where resource constraints aren’t abstract… they show up every single day.

Now comes the hopeful part 🙏
 I’m genuinely praying / manifesting that I get to be one of the lucky ~15.

#OpenAIGrove #OpenAI #ResourceResponsibleAI #SustainableAI #AIInfrastructure #AIDatacenters #GreenOps #MLOps #AIOps #AIAgents

Human-in-the-Loop (HITL) vs Human-on-the-Loop (HOTL) in Multi-Agent Systems

While I was thinking, researching and writing out the section on Human involvement in MAS in my recent book - The Illustrated Guide to Multi-Agent Systems, I had an epiphany on how to design Human + AI agent teams that actually work in production.

Most teams pick ONE human oversight model and apply it everywhere. Most agent demos treat “human oversight” like a feature you add later.

That's a mistake.

In production Multi-Agent Systems (MAS), that’s backwards.
Humans aren’t just approvers at the end of a workflow. They’re embedded in:
 ✅ objectives
 ✅ constraints & permissions
 ✅ evaluation criteria
 ✅ escalation paths
 ✅ accountability

There are actually two operating modes:
HITL (Human-In-The-Loop)
 ➡️ the system pauses. No consequential action proceeds without explicit approval.
HOTL (Human-On-The-Loop)
 ➡️ the system runs, but humans supervise with the power to intervene, override, or kill-switch.

Here’s the key: Production MAS needs both — applied selectively, not uniformly.

Here's the framework that actually works:
Map every action on two dimensions:
→ Risk (low to high) → Reversibility (can you undo it?)
Then apply the right model:
🟢 Low risk + Reversible (draft reports, queries) → HOTL. Let agents run. Monitor for anomalies.
🟡 High risk + Reversible (budget proposals, config changes) → HITL with fast approval cycles. Human validates, but keep it tight.
🔴 High risk + Irreversible (fund transfers, data deletion, contracts) → HITL with enhanced guardrails. Multi-party approval. Audit trails. No shortcuts.

The cost of getting it wrong:
→ All-HITL? Approval bottlenecks. Humans become the constraint. You've built a system slower than doing it by hand.
→ All-HOTL? One retry storm cascades across agents. Customer-facing workflows fail before anyone notices.
→ Misaligned incentives? Agents optimize for speed, skip validation steps, and technically "succeed" while violating business intent.

The production-grade approach:
* Classify actions by risk × reversibility
* Set confidence thresholds per workflow
* Layer guardrails: policy gates → confidence scoring → runtime monitoring → escalation routing
* Reserve HITL for the ~2% that's genuinely high-stakes
* Close the feedback loop: every override becomes training data

The mark of production-grade MAS isn't how autonomous it is.
It's how effectively it embeds human judgment at every level—while still moving at machine speed.

It’s finally here. 🚢
After two months of building, tinkering, and turning real lessons into reusable patterns. 

 📘 The Illustrated Guide to Multi-Agent Systems is now live on Amazon.

2025 was the year of AI Agents. The next unlock is multi-agent systems—planner/router/executor/verifier—built to survive tools, evaluation, and production reliability. This is a production-grade approach to AI agent engineering.

What makes this book different:
 ✅ Visual-first (see the system)
 ✅ Production-first (quality, safety, cost, ops)
 ✅ Artifact-driven (plans, evidence, verification)

🎁 Includes a FREE MAS Toolkit + learning artifacts.

If you’re building AI agent products or leading AI teams shipping AI agent systems, this is for you.

Links + reviewer form in the comments 👇

No book gets written in isolation.

While working on The Illustrated Guide to Multi-Agent Systems, I went deep into several books that shaped how I think about agent architectures, orchestration, and what it actually takes to build systems that work beyond demos.

A few that made a real difference:
Victor Dibia's Designing Multi-Agent Systems taught me to think in protocols and system guarantees, not just frameworks. 
Sinan Ozdemir's Building Agentic AI showed me why agents break and how to design around it. 
Thomas R. Caldwell's AI Engineering Bible grounded me in what production-ready actually means. 
Sadie St Lawrence's work on AI orchestration reframed how I think about the role behind the systems. 
And Pascal Bornet, Melissa Pamela, Anjanava Biswas, and Wrick Talukdar each added layers to my understanding of business impact, control loops, and organizational transformation.

My writing is a synthesis of existing thinking and experiential learning. 

These authors did the hard work of documenting their thinking, and I'm grateful I could stand on their shoulders while building my own visual playbook.

If you're serious about multi-agent systems, these books belong on your shelf. See attached document. 

Many thanks to these Authors: 
Pascal BORNET
Rakesh Gohel
Tom Davenport
David De Cremer
Melissa Pamela
Anjanava Biswas
Wrick Talukdar
Victor Dibia, PhD
Thomas R Caldwell
Sinan Ozdemir

Awesome AI Agent Engineering Books

The moment when an epiphany about AI Agent Observability happened, is when I put down some notes for this post. 

Most AI Observability approaches today tell you what happened.
However, AI agents don’t fail like microservices or servers.  AI agent observability must tell you what’s about to break.

AI Agents don’t just crash or timeout. They loop, over-retry, thrash tools, or quietly escalate work to humans while appearing “healthy.”

While there is great guidance from Arize AI / LangChain / Langfuse / Hugging Face, I feel that Observability for AI agents has to move beyond traces, cost, and latency—and focus on agent behavior.

I liked how IBM framed it: 
"Evaluate agent performance by collecting data about actions, decisions and resource usage. It helps answer critical questions, such as:
Is the agent providing accurate and helpful answers?
Is the agent using processing power efficiently?
Is the agent using appropriate tools to fulfill its goals?
What are the root causes of issues with an agent?
Is the agent complying with AI ethics and data protection mandates?

With these insights, organizations can troubleshoot and debug issues more effectively and improve the performance and reliability of AI agents. 
"
Hence I came up with a framework to monitor, measure and report on AI Agent Observability. 

Four operator questions. Eight metrics. One goal: trustable autonomy.

1️⃣ Is it working?
 • Task Success Rate
 • Verifier Pass Rate

2️⃣ Is it struggling?
 • Tool Success Rate
 • Retry Rate
 • Loop Rate

3️⃣ Is it efficient?
 • Time to Result (median, not average)
 • Cost Proxy (tokens, tool calls, retries)

4️⃣ Does it need help?
 • Escalation Rate

What’s different here isn’t the metrics themselves—it’s what they represent.
These aren’t infrastructure metrics.
 They’re behavioral signals:
• Retry & Loop Rates expose reasoning breakdowns
 • Verifier Pass Rate applies truth pressure
 • Escalation Rate defines the autonomy boundary
 • Time & Cost reveal economic viability, not just performance

This is observability as operability.
Not:
“Can I see what the agent did?”
But:
“Can I trust what it will do next—and recover when it doesn’t?”

That’s how AI agents I think can earn trust at scale.

I have written more about it in my upcoming book "The Illustrated Guide to Multi-Agent Systems". 

#AIAgents #AgentObservability #AgentOps #LLMOps #ReliabilityEngineering #EvalOps #AIArchitecture

LLM / Gen AI security is a topic that I am not familiar with although I follow the  awesome writing by my friend Rammohan Thirupasur on this topic. 

So when Dipali M., my fav editor at Packt sent me this book, I read this with a beginner mindset. 

One of the concepts in the book - Trust boundaries - inspired me to think about an equivalent aspect for Multi-Agent systems which I parlayed into my recently published book on Multi-Agent systems. 

AI-Native LLM security is a very important topic with very few credible resources. So this book from Ken Huang / Vaibhav(VB) Malik Ads Dawson
is a seminal work in the field. 

AI-Native LLM Security (Packt, 2025) — a book that lands at exactly the moment many of us are moving from “LLM demos” to “LLM systems in production.”

The big shift is this: LLM security isn’t just AppSec with a new wrapper. Once you add RAG, tools, agents, and enterprise data, the attack surface becomes language-shaped—prompt injection, system prompt leakage, vector store weaknesses, and “agentic” misuse don’t fit neatly into old threat models.

What I liked about this book is its practicality: it treats security as an end-to-end product discipline (architecture → trust boundaries → controls → monitoring → incident response), and it gives teams a shared language through frameworks like OWASP Top 10 for LLM Apps—so security, engineering, and product can align without hand-wavy debates.

The Security Question Has Changed. Is Your LLM Architecture Security Ready? Here's a question I've been sitting with lately: If your LLM made one wron

Open article: Review: AI-Native LLM Security by Vaibhav Malik, Ken Huang, and Ads Dawson by Harsha Srivatsa • 4 min read

Review: AI-Native LLM Security by Vaibhav Malik, Ken Huang, and Ads Dawson

Attending CES 2026 at Las Vegas for the first time. Its exciting, overwhelming, lots to take in.. But its an AI show like no other. Especially given my interest in Physical AI. 

CES 2026 (Day 1 Jan 5 2026) made one thing clear: AI is growing hands, wheels, and sensors.

We’re watching the shift from “AI that talks” → AI that moves, sees, touches, and acts in the real world.

Here are the Physical AI signals I’m tracking from Jan 5, 2026:
1) NVIDIA is pitching the “factory for robots + AVs.”
Rubin + new AV models (Alpamayo) feels like NVIDIA saying:
👉 “If you want autonomy at scale, you’ll train + simulate on our full stack.”
This is less about one chip… and more about owning the pipeline (compute → simulation → deployment).

2) Home robotics is jumping from gadgets → generalist helpers.
SwitchBot’s Onero H1 is a bold move: a wheeled humanoid doing real chores using an on-device VLA brain (Vision + Language + Action).
Translation: it can see the world, understand instructions, and do stuff—without depending on the cloud every second.

3) The AV sensing war is getting spicy.
Teradar’s terahertz sensor is trying to blend “lidar-like detail” with “radar-like all-weather.”
Arbe + NVIDIA are pushing ultra-HD radar as something closer to a primary perception system (not just a backup).
Big theme: “lidar-class perception” without lidar constraints.

4) Drones are turning into flying edge-AI computers.
Ambarella’s CV5 powering an 8K 360 drone concept is a reminder that high-res + onboard AI is becoming the baseline.
But… regulation and supply chain policy may decide who actually wins the market.

5) Samsung is framing the home as a “physical workflow.”
Not just “smart devices.” More like AI companions that sense → decide → act across the home.

My takeaway:
Physical AI is now a platform game.
The winners won’t ship devices. They’ll ship systems: hardware + models + sensor stack + ecosystem + safety.

For Physical AI Product Builders:
Build sensor-agnostic perception (avoid single-modality lock-in).
Go on-device first, cloud assist second (latency + privacy + safety).
Treat policy/compliance as a product requirement, not a footnote.

I wonder which becomes the “default brain” for Physical AI in 2026–2027: on-device VLA, cloud copilots, or hybrid?

#CES2026 #PhysicalAI #EmbodiedAI #Robotics #HumanoidRobots #AutonomousVehicles #EdgeAI #SensorFusion #ComputerVision #AIHardware #SmartHome #AgenticAI

The art of constructing Golden Data Sets (using the 6V framework) 🧪✨

While I was thinking and researching about Golden Sets for Agent Evaluations, the following questions came to my mind: 
- What should be the size of the Golden Data Set for effective evaluations of an Agent application? 
- What should be the variety of the representative entries in the GDS? 
- For an multi-agent system, should be have Evals - GDS for each agent and also for the MAS end to end? 

I didnt find too many authoritative source and definitive guidance. But I was able to find brilliant snippets that helped me a lot. 
* Stella Wenxing Liu substack blog Data Science x AI had an awesome article on how big an evaluation test should be. 
* Talking to an AI architect inspired me to come up with the GDS coverage - Happy Path (50%), Edge cases (20%), Tool failure cases (10%), safety and regressions (15%, 5%). 

Then I had the epiphany for applying the classic Big Data framework to GDS. 
Most teams treat a golden dataset like a spreadsheet they “should probably have.”

In AI Agent world, a golden set is something else:
* It’s your definition of “good”.
* Your release gate.
* Your anti-regression insurance.

The “big data” thinking - the 6V's - is a solid approach to building high quality GDS. 

1) Volume — Do we have enough test cases?
2) Variety — Do we cover different kinds of reality?
3) Velocity — How fast does it go stale?
4) Veracity — Can we trust labels + scoring?
5) Value — Does the dataset protect what matters?
6) Variability — Do we capture ambiguity + distribution shift?

The MAS twist: Golden sets are two-layer
If you’re building multi-agent systems, the 6Vs naturally imply two eval views:
✅ System-level E2E golden set (user journey → final outcome)
✅ Per-agent golden sets (tight contracts → faster debugging + safer iteration)

One last point: you don’t always need massive GDS. Often, a small pilot (~20) tells you the score variance, and you can estimate an effective test size—sometimes ~100 is plenty, not 5,000.

I have covered this in detail in my newly published book "The Illustrated Guide to Multi-Agent Systems" - link in comments. 

#EvalOps #LLMEvaluation #AgenticAI #MultiAgentSystem

Yet another awesome session from SuperInterviews with Kasey Superinterviews and Jeremy Barton. 

Hear Deborah Liu say there is no such thing as true failure in interviews. Every interview is a prep to the next interview.

I am convinced that simulation based learning and exploration should be an integral part of AI agent building. 

I used to learn multi-agent systems like this:
📖 read an article, blog, paper, course material
🧠 seem to get it and nod my understanding
🧩 imagine how I can implement it into my product
🚀 vibe build a MAS prototype
😅 get surprised when I make it work for real 

The uncomfortable truth: I could design and vibe build MAS architecture… but I didn’t have full confidence in my system design until I could see the system behave under real conditions.

So I changed how I learn.
Now I learn MAS architecture like a pilot learns flight:
In a simulator.

Not “animated diagrams.”
A decision lab: inputs → choices → consequences → artifacts.
I pick a simple multi-agent use case (any domain works: support, finance, research, ops) and simulate the build approach and design decisions. 

This is what’s helping me become a Super IC AI PM — because the simulator makes the invisible… visible:

Tradeoffs stop being theoretical
Topologies aren’t “better/worse.” They’re trade spaces.
You can literally watch coordination tax show up, and see where the quality gains stop being worth the latency/spend.

Reliability becomes a systems skill
Not prompt luck.
I inject failures (noisy retrieval, flaky tools, partial context, conflicting agents) and learn what actually stabilizes systems: guardrails, budgets, circuit breakers, fallbacks, escalation paths.

Production thinking becomes muscle memory
EvalOps, observability, and safe rollout aren’t just checklists — they’re behaviors over time.
Shadow → Canary → Full becomes something you practice, not just plan.

My litmus test is simple:
If a simulation helps me make a better build and decision decision and teaches me why, it’s real learning.

Here are three examples of simulation based artifacts that helped in better MAS system design: 

1. The EvalOps & Observability Dashboard is an interactive simulation that implements the complete evaluation lifecycle for multi-agent AI systems. Manage golden test sets, monitoring real-time metrics (latency, accuracy, cost, safety) and debug with distributed traces. 
https://lnkd.in/gvEE67RY

2. The Failure Mode Diagnostic Lab is an interactive simulation that shows the four critical failure modes in multi-agent AI systems: Loops, Tool Thrashing, Cost Explosions, and Security Breaches. Instead of just reading about what goes wrong, you can watch failures cascade through real-time metrics and audit logs—then apply fixes and see them work.
https://lnkd.in/g2j6SbjZ

3. The Cost Explorer is an interactive simulation that makes multi-agent system costs tangible. It visualizes the five root causes of cost explosions—context snowballing, unlimited fan-out, missing caches, memory hoarding, and wasted verifications. 
https://lnkd.in/gBnxx7qt

Simulation Artifacts for Multi-Agent Systems Design

The Evolution of Evaluations

The evolution of evaluations is happening in plain sight:  we’re moving from LLM-as-a-Judge → Agent-as-a-Judge.

This recent paper - A Survey on Agent-as-a-Judge [https://lnkd.in/dh9ThVRR] is an awesome recent survey publication and captures the trend thinking quite well. 

Many people have commented and written about the paper albeit in a static commentary way but not delving into the nuances. With Google Dynamic views in Gemini, I have found a way to really understand an AI paper and understand the principles and working outlined in a paper. Instead of passive reading (or even “paper → code”), I’m building interactive simulation-based learning artifacts: https://lnkd.in/d42i3hgp

In this simulation based learning artifact, I simulate the judge’s workflow (plan → evidence → verify → deliberate → report), stress-test edge cases and failure modes, run “what-if” scenarios to expose hidden assumptions
and walk away with a mechanistic understanding of how this would behave in production. I have used a code generation use case to illustrate the difference between Agent-as-a-Judge and LLM-as-a-Judge. 

Please do check out Google Gemini Dynamic View artifact link above. 

For agentic evaluation specifically, simulation stimulated my thinking about: 
 What tools? What verification steps? What memory? What arbitration? What’s the stopping rule?

LLM-as-a-Judge was a huge leap: scalable, “near-human-ish” scoring, and a practical proxy for preference/reward signals. 

But it’s starting to hit a ceiling as evaluands become multi-step, tool-using, and domain-specialized. 

Where LLM-as-a-Judge breaks down
A single-pass judge tends to struggle with:
* Biases baked into the judge (e.g., rewarding verbosity, style, or its own output patterns). 
* “Looks correct” vs “is correct”: it often can’t verify claims against the real world, so you get hallucinated correctness. 
* Cognitive overload when the rubric has many dimensions → coarse scores that miss nuance. 
* Over-focus on final output, while failing to verify intermediate actions in complex workflows. 

What Agent-as-a-Judge changes
Agent-as-a-Judge turns evaluation into an active process, not a static opinion:
* Plans + decomposes evaluation into subtasks
* Uses tools to ground & verify (search, code execution, environment checks)
* Leverages multi-agent collaboration to reduce single-model blind spots
* Uses memory to preserve intermediate evidence and produce more traceable judgments 

The paper even frames a maturity curve:
Procedural → Reactive → Self-Evolving judge agents (from fixed workflows, to adaptive routing/tool use, to agents that can refine rubrics and learn over time).

If you’re building EvalOps for agentic systems, I think this shift matters:
the Judge is no longer a prompt — it’s an Agent system.

Like many others, I too am on the bandwagon of AI agent / AIoT agents. 
I have delved into the nitty gritty of ~10 multi-agent implementation - 2 in Healthcare, 1 in AI Infra and the rest across different use cases mostly for productivity and product management. 
One of the MAS is currently deployed and running in a Hospital system. 

From these experiences, I’ve learned the hard way that with multi-agent systems, “it works” in a demo doesn’t mean it will survive production.

The biggest gap isn’t tooling. It’s intuition—how agents behave over time when tools fail, handoffs break, loops run, costs spike, or security boundaries get fuzzy.

So I’m sharing a systems-thinking approach I’m excited about: Simulation-First Development. Before we prompt and build, we simulate. We practice the tradeoffs. We inject failures safely. We generate artifacts that become design records.

In the article below, I break this down and outline a 22-simulation roadmap that mirrors the full multi-agent lifecycle—from mental models all the way to EvalOps, scaling, and enterprise constraints.

I have documented the complete set of 22 simulation based learning and exploration artifacts that are enumerated in the roadmap described in the article. 

If you would like to take a look at these simulation artifacts, comment SIM below and I shall send you the link.

A Systems Thinking Approach to Vibe Engineering Multi-Agent Systems 85% of AI agent pilots never reach production. Not because the technology failed. 

Open article: Simulation-Based Multi-Agent Development by Harsha Srivatsa • 4 min read

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