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

Building Agentic AI systems beyond connecting APIs or LLMs is complicated, but not impossible.

This architecture lays the foundation for how AI agents think, communicate, and improve, covering everything from testing and observability to deployment and memory management.

Here’s a breakdown of the key layers and components that make up a scalable Agentic AI Architecture :

1.🔸Decomposition
Break down complex systems by domain (e.g., Coding Agent, Data Agent), by cognitive capability (Reasoning, Planning, Execution), or by agent role (Planner, Executor, Memory Manager, Communicator).

2.🔸Communication
Enable message passing between agents using inter-agent protocols or A2A (Agent-to-Agent) orchestration.
Support both single-agent and multi-agent setups for small or distributed workflows.

3.🔸Deployment
Deploy agents in containerized or serverless environments using Docker or Modal.
Support orchestrators like CrewAI or AutoGen for collective intelligence in multi-agent workflows.

4.🔸Data & Discovery
Integrate knowledge bases (like vector databases for RAG), memory stores (FAISS, Redis, Pinecone), and APIs for dynamic data access.
Context is passed using Model Context Protocol (MCP) for structured and real-time reasoning.

5.🔸Testing & Observability
Validate workflows end-to-end, test reasoning logic, and evaluate performance under real conditions.
Monitor using Weights & Biases, LangFuse, and track metrics like latency and task success rate.

6.🔸UI & Style
Provide intuitive feedback loops through visualization layers, dashboards, and self-reflective modes.
Enable collaborative, proactive, and goal-driven reasoning among multiple agents.

7.🔸Security
Protect access with token-based authorization and data encryption.
Include Trust Layers for human-in-the-loop validation and Policy Enforcement for safe execution.

8.🔸Cross-Cutting Concerns
Handle configuration, secrets, and environment management.
Support flexible frameworks like LangChain, AutoGen, or CrewAI for runtime execution and modular design.

Agentic AI is the future of automation - where AI doesn’t just assist but collaborates and learns.

Save this post to understand the architecture that powers the next generation of AI systems

#AgenticAI

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AI agents are great at working alone. However, they can also be effective when operating within networks of collaboration.

The real power of AI lies in how these agents communicate, coordinate, and complement each other’s abilities.

Orchestration patterns define this collaboration which turns isolated agents into intelligent, goal-driven systems capable of solving complex tasks together.

Here are 6 orchestration patterns that power the next generation of AI systems :

1.🔸Sequential Orchestration
Agents work one after another - each passing its result to the next.
Perfect for structured, step-by-step tasks like data pipelines or analysis chains.

2.🔸Concurrent Orchestration
Multiple agents work in parallel, handling different parts of a problem simultaneously.
This boosts speed, scalability, and overall task efficiency.

3.🔸Group Chat Orchestration
Agents communicate in a shared conversation, refining ideas together.
Useful for brainstorming, collaborative decision-making, or generating consensus.

4.🔸Maker–Checker Loops
One agent creates or proposes; another verifies, corrects, or approves.
Ideal for maintaining accuracy, compliance, and quality assurance in workflows.

5.🔸Handoff Orchestration
Agents transfer control dynamically based on context or expertise.
Best suited for adaptive workflows like support systems or multi-step automation.

6.🔸Magnet Orchestration
A central “manager” agent dynamically assigns tasks to the right specialists.
Enables fluid, context-aware collaboration across multiple intelligent agents.

AI Agents are evolving fast and orchestration is what makes them powerful.

Learning how these patterns can help you build smarter, more collaborative AI systems can be a huge advantage.

#AIAgent

AI Agent Orchestration Patterns

📚LLMs in the Enterprise are finally getting the playbook they deserve… LLMs in Enterprise by Ahmed Menshawy and Mahmoud Fahmy provides practical guides for building real AI systems that operate at scale.

Most teams talk about LLMs in theory. This book focuses on execution. It bridges foundational concepts with the hands-on design patterns that matter when you are integrating models into production environments.

Here are the insights that stood out👇

1.🔸Enterprise LLM integration is a data architecture problem
The book breaks down how to design pipelines, tune retrieval, and structure data so models operate with consistency and low latency in real workloads.

2.🔸Scaling LLMs requires pattern-level thinking
They go deep on architectural patterns that reduce complexity, improve efficiency, and streamline deployment. This includes RAG frameworks, fine-tuning strategies, segmentation techniques, and evaluation patterns that teams often overlook.

3.🔸Performance is not just about bigger models
The authors show how to optimize model behavior with advanced inferencing engines, contextual model customization, and monitoring systems that keep applications predictable.

4.🔸Real enterprise value comes from operational rigor
Security, fairness, transparency, and accountability are not afterthoughts. They are part of the design process, especially when LLMs touch business workflows and customer data.

5.🔸AI teams win by mastering both concepts and impact
The flow of the book reflects the real enterprise lifecycle: Concept → Customization → Impact
A clear, structured way to think about turning LLM capabilities into business outcomes.

If you are building production AI systems, leading an LLM program, or preparing for the next wave of enterprise adoption, you should definitely get a copy of this book.

Enterprise AI is evolving fast. Understanding these design patterns early puts at a career advantage which allows you to shape the next generation of intelligent applications.

#LLM

Agentic AI vs Traditional AI: The Shift from Supervision to Autonomy

For years, Traditional AI has been like a skilled worker. It has been efficient, but dependent on human supervision.

Agentic AI, however, takes a different approach. It is a decision-maker that thinks, plans, and adapts without constant direction.

Let’s dive into what truly separates them 👇

Agentic AI: Smarter. Adaptive. Independent.

- Dynamic Learning: Perceives its environment, plans actions, and evaluates outcomes continuously.

🔸Goal-Oriented Thinking: Sets goals, creates sub-goals, and adjusts strategies like a human problem solver.

🔸Reasoning & Reflection: Learns from mistakes, refines decisions, and improves performance over time.

🔸Collaborative Agents: Multiple agents share context, delegate tasks, and synchronize for collective intelligence.

Traditional AI: Powerful but Limited.

🔹Static Learning Models: Train once, deploy, and manually retrain, with no real-time adaptability.

🔹Human-Dependent Execution: Waits for prompts or tasks, performs inference, and stops.

🔹Narrow Focus: Optimized for specific use cases without flexibility.

🔹No Self-Improvement: Requires human supervision for updates and tuning.

In Short:

- Traditional AI executes.

- Agentic AI thinks, plans, and evolves.

It’s the difference between automation and autonomy, a shift that’s redefining how AI systems operate, learn, and collaborate in the real world.

What other diffenreces have you noticed?

#AI

Joe Reis 🤓

Chief Operating Officer

DeepLearning.AI

Instructor

ThinkQuick, LLC

Data engineering and architecture consulting.

Owner

Ternary Data

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.

O'Reilly Media

Author

University of Utah

Adjunct Professor

Big Squid, Inc.

* Consulted with Big Squid clients and sales prospects about how to implement and leverage data science and machine learning within their organization.
* Built and implemented data science curriculum and training for Big Squid's sales and consulting team.
* Advised engineering and product team on data science best practices, infrastructure, and algorithms.
* Engineered data science and predictive analytics solutions for clients.
* Certified Domo technical consultant.

Director of Predictive Analytics

Teem

* Built Teem's (formerly Eventboard, now WeWork) analytics and data platform from the ground up. The insights and data help optimize and empower the workplaces of many smart companies including Uber, LinkedIn, Netflix, Dropbox, Airbnb, Palantir, Pinterest, Twitter, etc, etc, etc.
* Built and managed a team of nearly a dozen brilliant engineers and data scientists.
* Oversaw all data science and data engineering initiatives.

Lead engineer - Analytics and Data Science

JobDash

* Refactored front end of application from Django into Angular
* Enhanced company's analytics capabilities via internal BI 
* Core technologies used: Django, Python, Angular

Software Engineer

Pure Predictive

* Worked on Pure Predictive, a cutting edge machine learning application. Focused heavily on solving the fundamental problem in which data scientists spending 80%+ of their time on preprocessing and munging data, and only a fraction of their time on analysis. 

* Core development and invention of automated data preprocessing and feature selection tools to transform data into machine readable format. End result: reduced the time spent on data science pre-processing and data munging from 20+ hours to around 1 minute.

* Project lead of a team consisting of top notch data scientists, senior software engineers, and Math PhDs.

* Heavily involved in machine learning.

* Technologies used: Python, Java, Hadoop, AWS, Unix

Skullcandy Inc.

* Demand planning, forecasting, and global supply chain management.

* Worked with key Asian vendors.

* Integrated Astro's supply and demand components into Skullcandy.

* Implemented MRP, forecasting processes, and supply chain management processes for their gaming division.

* Supported global product launches into major Consumer Electronics retailers (Best Buy, Target, Gamestop, and key international partners). 

* Key to the success of the hottest gaming headset of 2012 - the Astro A50.

* Experience with SAP, Oracle Demantra.

Operations/Supply Chain

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

Helping data leaders and teams build systems that scale, with clarity, real-world insight, and a healthy dose of skepticism toward hype.

If you’re navigating the challenges of modern data and AI engineering, you’re not alone. Whether it’s designing resilient pipelines, managing organizational chaos, or separating signal from noise in the AI era, the goal is simple: get data from source to value, reliably and efficiently.

That’s where I come in:
- Co-author of the bestselling Fundamentals of Data Engineering (O’Reilly), used by 100,000+ professionals worldwide
- Instructor of Coursera’s top-rated Data Engineering course
- Host of a popular newsletter, podcast, and video series focused on real talk in data and AI
- Advisor and educator to top data and AI leaders across startups, enterprises, and VC firms

Looking to make better decisions with your data stack, your team, or your roadmap? Shoot me a message and let's chat.

Looking to make better decisions with your data stack, your team, or your roadmap? Subscribe to my newsletter for regular updates: https://joereis.substack.com/

AI agents will operate at a scale that humans won't match, and will need new ways of working with data.

In this episode, Ciro Greco (CEO @ bauplan) and I chat about why AI agents need a new lakehouse paradigm, next-gen "Git for data", and much more.

Links in the comments.

Joe Reis

If you’re looking for the world‘s best virtual conference on data observability, reliability, and trust by design, look no further than Sifflet’s upcoming Signals Conference.

I’ll be speaking about how you can build trust by design into your architecture with data modeling. And there are lots of other awesome talks by amazing practitioners and leaders.

When: November 17 to 20
Where: Virtual (link in the comments)

See you there!

People, Process, Technology, and...Data.

Data is the fourth pillar of any organization nowadays. Listen to this awesome chat with Sujay Dutta and Siddharth Rajagopal as we discuss their new book, Data as the Fourth Pillar, the latest trends in AI in the enterprise, and much, much more.

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