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

Anthony Alcaraz

Writes on ML/LLM Ops 🎨 | Follow me (4,8 K+) 🔔

I help AI startups go from promising to unstoppable.

I also write about it, my book Agentic Graph RAG (O'Reilly, 2026) is the practical blueprint for building AI systems that combine retrieval, reasoning, and autonomous decision-making at enterprise scale.

https://www.oreilly.com/library/view/agentic-graph-rag/9798341623163/

My career has been a journey across both sides of the table: building AI products as a CTO, transforming legacy systems as a Field CTO for enterprises and government, and now at AWS, helping the most exciting GenAI startups scale across Europe and the US.

I've shipped GenAI platforms, deployed MLOps pipelines serving millions, advised L'Oréal on AI-for-BI transformation, and lectured at Oxford on the intersection of LLMs and graphs. But what gets me out of bed is working with founders who are solving hard problems with bold ideas.

At AWS, I connect high-potential startups with the resources, partnerships, and enterprise pathways they need to grow. Before that, I was in the trenches, building, breaking, and shipping.

If you're a founder, VC, or enterprise leader thinking about GenAI, agentic systems, or knowledge graphs, I'd love to chat.

Chief AI Officer & Decision scientist

Management in AI Projects and Engineering Teams:
In my managerial role, I oversee AI projects and lead teams of engineers in crafting cutting-edge solutions. My focus is on driving strategic initiatives, ensuring projects are aligned with organizational goals, and fostering a culture of innovation and excellence. I take a hands-on approach to problem-solving and mentorship, ensuring that both projects and people reach their fullest potential.

Development in LLM/ML Ops:
My development expertise lies in the specialized domains of Large Language Models Operations (LLM Ops) and Machine Learning Operations (ML Ops). I am responsible for streamlining machine learning workflows from development to deployment. My work enables organizations to not only adopt but excel in leveraging machine learning technologies, facilitating continuous learning and adaptability across the board.

Marketing and Strategic Partnerships:
In the realm of marketing, I focus on building and nurturing strategic partnerships. My efforts are geared towards creating synergies with like-minded organizations and industry leaders. By forming valuable partnerships, I amplify brand visibility, foster innovation, and open doors to new business opportunities. The result is a win-win situation for all stakeholders involved, enhancing both market reach and impact.

The strongest multi-agent architecture has one rule:
No agent calls another agent. Ever. 💢 

Most agent systems work like phone calls. The orchestrator dials Agent A, waits, dials Agent B with the result, waits again. 

If B hangs up, everyone downstream sits in silence.

Queue-based architecture works like a mailroom. Agents subscribe to event streams. They listen for messages matching their capability. They process, drop output into the next queue. No agent knows another agent exists. The queue is the only shared interface.

Agent A publishes to topic:tasks.parsed.
Agent B subscribes to topic:tasks.parsed.
Agent B publishes to topic:tasks.enriched.
Agent C subscribes to topic:tasks.enriched.

No function calls between them. No shared state. No waiting.

A meta-orchestrator reads every topic. It holds the full pipeline schema. When Agent B crashes, messages pile up in tasks.parsed with no consumer.

It restarts B, scales a second instance, or reroutes. Other agents never notice.

Four layers make this work:

Layer 0 — STATE: External stores (database, graph). Events carry references, not payloads.
Layer 1 — EVENT BACKBONE: Kafka or Redis Streams. Durable, ordered, replayable event log.
Layer 2 — AGENTS: Stateless consumers. One capability each. Subscribe input, publish output. Errors to dead letter queue.
Layer 3 — META-ORCHESTRATOR: Reads ALL topics. Tracks events via parentId chains. Detects stuck agents, poison pills, throughput drops.

Confluent documents four patterns on this backbone: Orchestrator-Worker, Hierarchical, Blackboard, Market-Based. Kai Waehner pairs Kafka with Flink for stateful stream processing. Anthropic's MCP and Google's A2A route through the event broker, replacing point-to-point APIs.

The agent contract is minimal: subscribe to T_in, consume event with id/type/payload/metadata, transform, publish to T_out with parentId linking back. On failure, publish to T_dlq with error context.

Every agent becomes a 200-400 line module. Independently deployable. Independently testable. Replaceable without touching anything else.

Ali Pourshahid at Solace calls this the "agentic AI mesh." Alan Nichol at Rasa warns the alternative is a distributed monolith — agents calling each other are monoliths with extra network hops.

The queue turns coordination into infrastructure. Agents stop calling. They start listening.

Scaling Agentic Startups to Enterprise @AWS | Author of Agentic Graph RAG (O’Reilly) | Business Angel

Your systems of record are lying to your AI agents. 🎶 

Ask sales for ARR: one number. 
Ask finance: different number. 
Ask accounting: something else entirely.

Now tell an agent to "calculate ARR for the board deck."

Which number does it use? When systems drift by 3%, which one wins?

The fragility isn't your model. 

It's whether the agent pulled the right value from the right system at the right time.

🔴 The Core Problem

Your warehouse stores state. It doesn't store reasoning, the logic that connects state to action.
Semantic layers define "official_arr." They don't capture that for board decks you exclude trial ARR, or that finance wins when sales disagrees.
That knowledge lives in tribal memory. And agents can't access tribal memory.

🟡 What Agents Actually Need

Three capabilities your stack doesn't have:
Full recursion, traverse ownership chains of arbitrary depth. 
Ontological inference, derive facts not explicitly stored. PTIME complexity, guaranteed reasoning termination.

SQL can't do this. Standard graph databases can't either.

🟢 The Transition That Works

Most enterprises assume agentic AI requires data migration.

It doesn't.

One bank tried the migration path: 4 people, 1.5 years, still couldn't explain credit decisions.

Same bank with a reasoning layer over existing data: delivered in weeks. 17-minute decisions became 5 seconds.

Bring reasoning to your data. Not data to reasoning.

🔵 The Data Strategy That Emerges

You have two distinct problems requiring two distinct solutions.

1️⃣  Problem one: Your agents need real-time memory. Conversation state. Session context. The ability to blend graph traversal with vector similarity in a single atomic transaction. Data that changes millisecond to millisecond.

This is an operational data problem. SurrealDB solves it. One engine handling graph, document, vector, relational, and time-series with unified ACID guarantees. Your agent's working memory lives here. LIVE SELECT pushes changes instantly. No eventual consistency bugs between separate stores.

2️⃣  Problem two: Your agents need to reason over enterprise knowledge. Derive conclusions from facts spread across Snowflake, Databricks, Postgres, and Oracle. Provide proof trees auditors can verify. Handle recursive ownership chains of unknown depth. Infer what must exist even when data is missing.

This is a reasoning problem, not a storage problem. Prometheux solves it. Connects to your existing data infrastructure without migration. Executes Warded Datalog+/- with mathematical guarantees no warehouse can provide. When your compliance agent needs to explain why a credit decision was made, the proof tree traces from conclusion back to source facts automatically.

These layers don't compete. 

They compose.

Your warehouse remains the analytical source of truth. 
Your operational systems remain the transactional front door.

Scaling AI Startups to Enterprise @AWS | Author of Agentic Graph RAG (O’Reilly) | Business Angel

Context graphs exploded in January 2026. 🤯 

I synthesized 6 leading perspectives into one framework.

Here's what the smartest people in enterprise AI are actually saying:

1. Arvind Jain (Glean CEO) - "Capture the How, Not the Why"

Systems capture final states. But work happens in Slack, docs, and meetings.

The "why" is unobservable. The "how" leaves a digital trail.

Over many cycles, process traces approximate the why.

2. Jaya Gupta (Foundation Capital) - "Security Is Existential"

Decision traces are the moat, but only if you make them safe.

Four primitives needed:
→ Stable pseudonymization
→ Synthesis-level conflict checks
→ Real-time policy enforcement
→ Sanitization before indexing

"Agents collapse the security boundary that human judgment provides."

3. Anshul Gupta (Actively AI) - "Salesforce Has Lost the Deal"

Salesforce is a system of record, not a source of truth.

"Ask any sales leader how they get up to speed on a deal. Salesforce is the last place they'd look."

The right unit isn't the opportunity. It's the account.

Per-account agents compound. Workflow agents don't.

4. Justin Johnson (Run Data Run) - "The Hard Part Is the Clock"

Both ontology camps argue about nouns.

Meanwhile, agents fail because they can't tell time.

"Your CRM knows Sarah is a VP. It doesn't know she was a Director until June—which explains why Finance flagged the pricing exception."

Temporal validity is the missing layer.

5. Diego Lomanto (WRITER) - "Enterprise Brain Must Be Stateful"

Most enterprise AI is stateless, each query starts fresh.

Enterprise brains remember, connect, and compound.

"Being adjacent doesn't compound. To capture reasoning, you need to be there when the decision commits."

Marketing is the proving ground: high volume, inherent ambiguity, unique executions.

6. Dharmesh Shah (HubSpot) - "The Three-Car Garage Problem"

"Building context graphs when you haven't deployed agents at scale is like installing a three-car garage when you don't own a car."

Most companies still:
→ Struggling with basic data unification
→ Figuring out if AI can handle tier-1 support
→ Haven't deployed agents at scale

Context graphs are inevitable. The timing is the question.

Six critical gaps remain:

1. Governance: No confidence decay or contradiction detection
2.*Security: Decision traces harder to anonymize than documents
3. Continuity: Workflow agents don't compound
4. Temporality: Systems can't answer "what was true when?"
5. Statefulness: Most AI is stateless
6. Adoption: Prerequisites aren't met for most companies

The architectural truth:

Layer 7: Context Graphs (memory)
Layer 6: Individual Brain (assistants)
Layer 5: Skills (portability)
Layer 4: Tool Orchestration (typed graphs)
Layer 3: Context Layer (access)
Layer 2: Control Layer (correctness)
Layer 1: Structured Output (composability)
Layer 0: Agent Harness (reliability)

If any layer = 0, total value = 0.

Context graphs are Layer 7. Build from the bottom up.

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