Greg Coquillo

Product Leader @AWS | Startup Investor | 2X Linkedin Top Voice for AI, Data Science, Tech, and Innovation | Quantum Computing & Web 3.0 | I build software that scales AI/ML Network infrastructure

Product leader with a passion for solving business use cases such as Growth, Go-to-Market, and Price Optimization. I owned P&L for commodity-based products, and leveraged AI to increase the average customer contract values across business industries. As a Product Leader at Amazon, I own a roadmap that drives the adoption of AI products through the Media Automation charter, which supports Private Brands’ YOY growth. I work with Data Scientists, Software Engineers and Business Intelligence Engineers to leverage Machine Learning and Big Data Analytics to design predictive models that enhance business decision-making and performance to accelerate growth, reduce Time to Market of products, monitor cross-geo compliance and import requirements, and ensure customer safety.

I am always curious and eager to learn how new technology affects our daily lives, and I love to support communities on their quest to adopt Artificial Intelligence across industries. Feel free to contact me if you have Data Science projects that you’d like to be showcased on this platform for visibility.

🌐I am a Global Business Leader with 12 years of experience and skills including Product Management, P&L management, Global Networking, EBITDA Optimization, Pricing Management, Margin Management, Product Management, Program Management, Operations and Supply Chain Management, Lean Manufacturing, Six Sigma, and Continuous Improvement.

🧘🏽‍♂️Soft Skills include: Communication, Collaboration, Lateral Thinking, Adaptability, Inspiration, Innovation, Empathy, Listening, Diplomacy, Organization, Responsibility, Leadership...

🎬🎥🎞 I really enjoy watching movies, and if I could redo it all over, I’d be a Sci-Fi script writer so I can be bounded by the limits of imagination.

(Opinions are my own)

Product Management, Media Automation

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity. Core responsibilities include:

-Building cross-functional teams consisting of Data Scientists, Software Engineers, Data Engineers, Product Managers and Program Managers, to plan and implement sophisticated AI projects
-Manage the New Initiative Intake Process
-Gather Business requirement artifacts
-Communicate roadmap definition and prioritization
-Coordinate User Acceptance Testing
-Manage Launch Announcements
-Prepare and communicate annual planning artifacts

AI Agent vs Agentic AI

Most people use the terms AI Agent and Agentic AI like they mean the same thing.

They don’t.

The difference isn’t just semantic.
It’s architectural.

Here’s how the tech stack evolves from AI Agent → Agentic AI 👇

1. Intelligence models
- AI Agent typically relies on a single LLM with prompt → response workflows.
- Agentic AI moves toward multi-model reasoning, planner–executor setups, and hybrid inference across systems.

2. Architecture & frameworks
- AI Agent often follows a single-agent, linear execution flow.
- Agentic AI introduces multi-agent systems, goal-driven workflows, and orchestration frameworks like LangGraph, CrewAI, or AutoGen.

3. Memory systems
- AI Agent works with session memory, short-term embeddings, and basic caches.
- Agentic AI adds long-term memory layers, episodic + semantic memory, knowledge graphs, and vector databases.

4. Tool usage & actions
- AI Agent uses predefined tools and function calling triggered by users.
- Agentic AI autonomously selects tools, plans multi-step executions, interacts with environments, and uses structured tool registries.

5. Knowledge & retrieval
- AI Agent typically uses basic RAG pipelines with static retrieval.
- Agentic AI evolves into adaptive RAG, context prioritization, hybrid search, and continuously updated knowledge graphs.

6. Orchestration & workflows
- AI Agent runs sequential flows and simple backend automation.
- Agentic AI uses orchestration engines, planning loops, event-driven workflows, and reflection cycles.

7. Decision making
- AI Agent is reactive and prompt-driven.
- Agentic AI is goal-oriented, with planning, self-evaluation, and iterative reasoning loops.

8. Deployment
- AI Agent is often deployed as chatbots, copilots, or API-based assistants.
- Agentic AI becomes autonomous platforms, digital workforce agents, and persistent execution systems.

9. Monitoring & observability
- Both need logs, monitoring, and error tracking but Agentic AI requires deeper analytics, response monitoring, and system-level feedback loops.

10. Learning & improvement
- AI Agent improves through prompt iteration and occasional fine-tuning.
- Agentic AI evolves through continuous feedback pipelines, performance adaptation, and evaluation frameworks.

AI Agent = intelligent responder.
Agentic AI = autonomous system with goals, memory, tools, and orchestration.

One answers questions.
The other executes objectives.

Are you building smarter responses or autonomous systems?

Anthropic made Learning AI easier.

They’ve released a complete set of FREE courses designed to help you understand Claude, AI agents, APIs, and the Model Context Protocol (MCP) - the foundation behind modern AI applications and agent systems.

This learning path covers everything from beginner concepts to advanced agent development workflows.

Here’s what you can learn:

• Claude 101 - Understand core features and everyday AI workflows
• Claude Code in Action - Use Claude to accelerate development tasks
• AI Fluency: Framework & Foundations - Learn how to work effectively with AI
• Building with the Claude API - Develop real AI-powered applications
• Introduction to Model Context Protocol (MCP) - Connect AI with tools and data
• MCP Advanced Topics - Deep dive into advanced implementations
• Claude with Amazon Bedrock - Deploy Claude in AWS environments
• Claude with Google Cloud Vertex AI - Production AI workflows on GCP
• Introduction to Agent Skills - Build reusable AI agent capabilities

These courses are especially useful if you want to learn:
AI Agents • MCP • LLM Applications • AI Automation • Developer Workflows

No fluff.
No paywall.
Just practical AI education directly from the company building Claude.

Get all the courses here:
https://lnkd.in/gAmrrsBK

If you’re serious about understanding how modern AI systems actually work, this is one of the best free starting points available right now.

Save this for later - you’ll want to come back to it.

Google just quietly released an AI tool for every layer of how humans create things.

Research. Design. Code. Video. Agents. Models.

All of it. At once.
Connected. Intentional. Ecosystem-scale. 

The tools nobody is talking about enough:

Jules - an asynchronous coding assistant that works across entire repositories, not just individual files. It understands the broader structure of a project and helps manage complex codebases.

Google A2A - a multi-agent communication protocol designed to let AI agents collaborate across different frameworks. This is infrastructure for the next generation of agent systems.

Stitch - converts prompts directly into complex UI layouts, turning ideas into interface designs within minutes. A major shift for how design workflows could work.

FileSearch API - a configurable RAG pipeline accessible through a single API call. What previously required weeks or months of engineering can now be integrated almost instantly.

Flow - a cinematic storytelling platform that lets creators generate scenes, narratives, and video content with AI-driven workflows.

Gemini 3 Pro - Google’s most advanced reasoning model so far, designed for deeper analysis, complex tasks, and next-generation AI systems.

This isn't a product launch.

It’s a full-stack AI ecosystem.

From foundational models → to research tools → to design → to development → to autonomous agents → to video creation.

Everything sits on top of the same AI foundation.

That means ideas can move from research → design → code → content faster than ever before.

The real strategy here isn’t individual tools.
It’s ownership of the entire creation pipeline.

And when one company controls every layer of that pipeline, the innovation speed compounds.

The AI race isn’t just about better models anymore.

It’s about who builds the ecosystem developers, creators, and companies build on.

People still use AI like a chatbot.
The real power starts when it becomes your execution layer.

Claude Code isn’t just another AI tool.
It acts like an operating system for building, testing, and shipping real work.

Instead of writing prompts and copying outputs, you can actually run workflows, analyze codebases, connect tools, and automate tasks from one place.

That’s the shift most teams haven’t fully understood yet.

Traditional AI tools help you think faster.
Claude Code helps you execute faster.

It moves through a structured loop:
analyze → plan → build → automate → scale.

You can connect repositories, files, databases, and internal tools.
Run commands.
Generate code.
Debug systems.
Automate repetitive work.
And build reusable skills that keep improving over time.

It also works across environments, which means you’re not jumping between ten different tools to get one workflow done.

The biggest difference?
It behaves less like a chatbot and more like a technical teammate that can actually take action.

When used properly, it becomes a working layer on top of your stack — not just a place to ask questions.

Most people experiment with AI.
Few build repeatable systems with it.

That’s where the real leverage is.

AI becomes valuable when it moves from answering questions to executing workflows.

Start treating it like an operating system for work, not just a prompt tool.

The agent gave a brilliant answer.
Then someone asked, “Where did that data come from?”
Silence. That’s when you realize intelligence needs infrastructure.

Agentic AI isn’t just reasoning.
It’s retrieval, validation, lineage, governance, and monitoring - all working quietly in the background.

Here’s what that foundation looks like:

- Data Ingestion
Pulls in structured and unstructured sources, standardizes formats, and attaches context before anything reaches an agent.

- ETL / ELT Pipelines
Transforms, validates, and loads data into usable systems so downstream workflows aren’t built on chaos.

- Data Versioning
Tracks changes, creates reproducible snapshots, and enables rollback when something breaks or drifts.

- Vector Pipelines
Chunks content, generates embeddings, and indexes it for semantic retrieval in RAG and agent workflows.

- Metadata Management
Registers datasets, records ownership, and enables search and observability across the ecosystem.

- Data Governance
Enforces policies, access controls, and compliance rules to prevent misuse and ensure accountability.

- Data Warehouse / Lakehouse
Stores refined datasets that power analytics, feature engineering, and intelligent querying.

- Streaming Data
Processes real-time events so agents can react, trigger actions, and operate dynamically.

- Data Lineage
Maps where data originated, how it transformed, and who depends on it.

- Data Quality & Reliability
Applies validation checks, detects anomalies, and keeps pipelines healthy under pressure.

At the center is the agent itself -
retrieving context, reasoning over data, planning actions, learning from feedback.

But without the layers beneath it,
even the smartest answer becomes a liability.

Intelligence is impressive.
Traceable, governed intelligence is enterprise-ready.

If someone asks where your agent’s answer came from, can you show them?

If you learned AI even three years ago, you’re already studying history.

The conversation has moved far beyond models and prompts. What we’re witnessing now isn’t just incremental improvement, it’s a fundamental shift in how intelligence is built, deployed, and experienced inside real systems.

AI didn’t suddenly appear with ChatGPT.
It evolved through decades of breakthroughs, each solving the limitations of the era before it - gradually transforming software from rule execution into adaptive intelligence.

Understanding that evolution is what reveals where AI is actually heading next.

From Rules → Learning
AI moved from hard-coded logic toward systems capable of learning patterns directly from data instead of predefined instructions.

From Learning → Representation
Deep learning and transformers introduced contextual understanding, enabling language comprehension, perception, and multimodal intelligence at scale.

From Models → Platforms
Foundation models and Retrieval-Augmented Generation turned AI into reusable platforms connected to dynamic, real-world knowledge sources.

From Assistants → Agents
AI progressed from answering questions to planning actions, reasoning through problems, and executing complex multi-step workflows autonomously.

Where AI Is Heading (2026+)
Intelligence is moving toward edge computing, embodied AI, persistent memory, and systems embedded seamlessly into everyday environments.

Every generation of AI didn’t replace the previous one, it stacked on top of it.

Many organizations struggle today because they attempt to adopt the newest layer without understanding the foundations underneath. Real advantage comes from recognizing how these layers connect:

Rules → Learning → Reasoning → Agents → Autonomous Systems

We’re not reaching the end of AI evolution.

We’re entering the phase where intelligence stops being a feature and becomes a system layer woven into everything we build.

#AI

If you think an AI agent is just an LLM connected to tools, you’re only seeing the surface.

What actually powers a production-grade AI agent is a layered intelligence architecture - where memory, reasoning, governance, and infrastructure work together to transform responses into reliable autonomous behavior.

Modern AI systems are no longer single models.
They are orchestrated systems designed to think, plan, adapt, and operate safely at scale.

This visual breaks down the internal anatomy of a real-world AI agent across three evolving layers:

From Execution → Coordination
The Basic Layer handles core operations — processing inputs, orchestrating prompts, invoking models, executing tools, managing errors, and delivering structured responses.

From Coordination → Reasoning
The Intermediate Layer introduces structured intelligence. Agents decompose tasks, plan execution strategies, validate outputs, manage context, monitor costs, and maintain observability for reliable performance.

From Reasoning → Autonomy
The Advanced Layer enables true production intelligence through long-term memory, multi-agent collaboration, governance guardrails, adaptive learning, self-reflection, and scalable deployment infrastructure.

Each layer doesn’t replace the previous one — it builds on top of it.

Organizations struggle with AI adoption not because models are weak, but because architecture is incomplete. Real AI capability emerges only when these layers operate as a connected system:

Execution → Reasoning → Governance → Autonomy

We’re moving toward a future where AI isn’t an application feature.

It becomes an operational layer embedded into every digital system we build.

Joe Reis 🤓

Chief Operating Officer

Best-Selling Author | Data Engineer and Architect | Recovering Data Scientist ™ | Global Keynote Speaker | Professor | Podcaster & Content Creator

I write. I build. And I’m working on something new for the data community.

More soon. ✍️📚⚡️

Ternary Data are veteran data engineers and “recovering data scientists” who have firsthand experience with the challenges of succeeding with data. We offer practical data engineering advice.

"I’ve had conversations this week with data engineers, founders, and investors. The universal vibe is that something changed in the last month or two.

I’m not talking about a gradual improvement. I mean a big change. The models got really good, seemingly overnight. What used to be a decently capable coding assistant that could write passable SQL is now producing production-quality work: pipelines, configurations, strategy documents, the works. Every new point release seems to surprise even people who use these tools daily. And I use and abuse them daily.

Multiple friends have independently told me the same thing: “I don’t know what happened, but something shifted.” The models crossed some threshold where the gap between “neat demo” and “this replaces actual work” effectively closed for a huge swath of tasks.

If you’re not feeling this shift, it’s probably because you’re not using the latest models. And if you’re not using the latest models, I hate to be that guy, but you’re woefully behind and out of step. Use the latest models ASAP."

Read more:

Joe Reis

Open article: The Reckoning Is Already Here by joereis.substack.com

"Data modeling is in trouble. But perhaps not for the reasons you might suspect.

That’s the clearest signal from the Practical Data Community 2026 State of Data Engineering Survey. We asked 1,101 data professionals about their tools, challenges, and outlook. The section on data modeling stopped me cold. 89% report at least one pain point with their modeling approach. Only 11% say modeling is going well.

Is this a technology or skills problem? Not according to the data. The top complaints are organizational: pressure to move fast (59%) and lack of clear ownership (51%). Data modeling is failing because organizations won’t give it the time and attention it requires."

TL;DR - it's your boss's fault ;)
Read more:

Open article: The Organizational Crisis in Data Modeling: Why 89% of Engineers Are Struggling by practicaldatamodeling.substack.com

The Organizational Crisis in Data Modeling: Why 89% of Engineers Are Struggling

"If the data industry has been teaching and preaching the same way for four decades and the vast majority of practitioners are still struggling, clearly the approach hasn’t worked. At this point, we cannot keep blaming the practitioners. Beating people over the head and telling them they’re stupid for not adhering to 40-year-old practices, or dogmatically blaming them for not rigidly following data modeling methodologies is unhelpful at best, and gatekeeping at worst...

Doing the same thing over and over again and expecting different results is the literal definition of insanity. Yet, the data industry keeps insisting that if we just shout louder at people, they’ll finally get it."

This weekend, I get a bit more unhinged than normal. But this needs to be said.

Read more:

Open article: The Insanity of Data Education by joereis.substack.com

When I first started writing my book on data modeling, I felt like the data modeling world was stagnant and tribal. For the past few years, I’ve been giving talks around the world on data modeling, articulating it in various forms. Reflecting on my enthusiasm for combat sports, it finally dawned on me that Mixed Martial Arts already had the template for where the world of data was heading.

This brand new chapter sets the stage for why the old ways of data modeling are failing and officially introduces the concept of Mixed Model Arts (the title of my upcoming book).

Preview here (fully available for paid subs): https://lnkd.in/g6DgrPkr

One of my biggest pet peeves on LinkedIn... I can't believe I have to keep posting about this.

Do you have ODBC Stockholm Syndrome?

We’ve been using ODBC and JDBC for decades. We’ve accepted that moving data from a data warehouse to a dataframe is slow. We’ve accepted the "serialization tax" of converting columnar data into rows for transport, only to convert it back to columns again on the other side.

But why are we still doing this in 2026?

I sat down with Ian Cook (Co-founder of Columnar and core Apache Arrow developer) to discuss ADBC (Arrow Database Connectivity), the new standard poised to finally replace our legacy row-based protocols.

Watch here: https://lnkd.in/gYnv7Pye

Open video: From ODBC to ADBC: Modernizing the Data Stack for AI and Analytics w/ Ian Cook on youtube.com

From ODBC to ADBC: Modernizing the Data Stack for AI and Analytics w/ Ian Cook

My friends at bauplan are giving a lunch and learn about next-generation lakehouses for AI agents, their new Claudio AI project, and more. They're definitely some of the sharpest people in the data biz, so you should check this out if you can.

When: Wednesday, Feb 18 at 12pm MT
Where: https://lnkd.in/g4_qmP6z (Google Meet, also streamed live on YouTube)

Utah AI/ML and data nerds! Have we got an event for YOU!

Kyle Nesbit (CEO of Credible and BigQuery OG) is traveling all the way from Colorado to talk about giving ML and AI systems meaning. What do I mean? Here's the blurb from Meetup:

Modern ML and AI systems are great at generating information — but they’re surprisingly bad at understanding what enterprise data actually means. Business logic, undocumented assumptions, and tribal knowledge are scattered across one-off SQL and people’s heads — and often never make it into models, agents, or applications at all.

This talk dives into Engineering Meaning: a new systems approach for turning business intent into a first-class, versioned, testable asset — and delivering that meaning in context to AI systems, applications, and analytics.

See what I mean? It's pretty cool. You'll learn a lot, and maybe meet some decently cool people too. Only one way to find out 👇 

When: Tuesday, Feb 24 at 6:30pm MT
Where: Weave, Lehi

Signup here:

Open article: Giving Data value to AI, Tue, Feb 24, 2026, 6:30 PM | Meetup by meetup.com

Giving Data value to AI, Tue, Feb 24, 2026, 6:30 PM | Meetup

One thing I love doing is speaking to students. Kishore Aradhya's data engineering class is always a treat.

As part of my graduate class in Data Engineering at Northeastern University this semester, we kicked off the guest lecture series with Joe Reis.
 
Joe consistently inspires my class to think differently and always from first principles. His call to adopt, lean into, and leverage AI coding tools is particularly relevant, as it's transforming the development landscape in a fundamental way and reshaping expectations for entering the workforce. He presented some very real, practical approaches, discussing specific examples of what he is seeing being played out with some very senior developers in this space.
Joe offers a unique perspective that resonates deeply with the students. Over the last six semesters, he has graciously served as a guest lecturer, fostering engaging conversations and enriching their learning experience. 
 
Our second inspiring guest last week was Veronika Durgin, VP of Data at Sack's. She recounted her journey over the years, including how she fell in love with SQL (coincidentally, it was Valentine's Day) and working with data - the continuing fundamental challenges in data governance, data management, and data quality, and how all of these are even more challenging and acute as we race to enable, adopt, and accelerate AI across the enterprise. She offered a unique perspective on how this is playing out in century-old organizations like Sacks with deep legacy footprints. Veronika's unique, down-to-earth style and approach have always resonated with my students, making her message and lessons relatable.
 
Looking forward to more great guests as I continue teaching Data Engineering, realizing how its importance has grown dramatically over just the last couple of years.
 
#Northeastern University MGEN #DataEngineering

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