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

No alternative text description for this image

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

University of Pennsylvania

- Develop and deliver courses based on Generative AI (GenAI) and Large Language Models (LLM 's) to graduate students, faculty, and staff.
- Recommend both new and revised curricula on incorporating other aspects of Artificial Intelligence (AI) to those same groups.
- Provide best practices for learning Artificial Intelligence.

Senior AI Curriculum Advisor 

- Developed curriculum for and teach classes on Artificial Intelligence. 
- Deliver to Graduate School of Education.
- Recruited expert guest lecturer on Generative AI for AI.
- Mentored and consulted students on the use of Artificial Intelligence.

Artificial Intelligence Co-Instructor

The Wharton School

- Provide research direction and mentorship to MBA and undergraduate students.
- Offer peer counseling to fellow graduate students.
- Draft strategy options for review by AI Accountability Institute.

#AI Research Mentor & Peer Advocate

Gartner

Advises clients in strategic challenges with Machine Learning, Artificial Intelligence, and Decision Sciences.  Specializes in areas such as:

- AI Talent/ AI Organization
- Upskilling AI Staff/ AI Literacy
- AI Strategy
- AI Ethics/ Responsible AI
- Measuring AI success
- Maximizing AI profitability
- Model ensembles
- Project management
- Simplifying operations
- Feature engineering
- Data storytelling 
- Working with stakeholders

Director, Data & Analytics/ AI Expert

Analytics-Edge, LLC

•	Created Data Science/ AI curriculum for Cisco Networking Academy: 3.1 million current students, 190 countries, and 6 languages.
•	Developed and delivered the first Data Science curriculum for all of 3M Asia.
•	Created the first Artificial Intelligence course for Harvard Innovation Labs' Experfy spin-off.
•	Provided consulting and training for clients like AARP, Biogen Pharmaceuticals, IBM, Los Angeles Times, Royal Bank of Canada, St. Jude's Children's Research Hospital, T. Rowe Price, and Wells Fargo.

VP- Artificial Intelligence Educator

Booz Allen Hamilton

•	Founded the Strategic Artificial Intelligence Lab (formally Emerging Technologies AI team)
•	Increased VBA Fraud Model Performance by 500% in 8 weeks
•	Lifted sales forecasting model accuracy from 85% to over 91%
•	Raised chatbot satisfaction levels from 27% to over 70%
•	Developed firmwide Introduction to AI training for 10K employees with a 98% approval rate

Lead Artificial Intelligence Solutions Architect

Legg Mason (Acquired by Franklin Templeton)

•	Provided 440% lift in product Cross-Sell performance with breakthrough AI model using champion/ challenger Model Risk Management platform.
•	Slashed Feature Engineering timelines by 80% with CN2 rule induction methodology.
•	Built and tested almost 300 production-ready models using Python, TensorFlow, SageMaker, and various AWS/ pipelines and processes.
•	Designed, authored, and implemented Artificial Intelligence Roadmap for Legg Mason, creating of Strategic Artificial Intelligence Labs.
•	Benchmarked AUM performance, revenue growth, and product performance over 800 separate data elements.
•	Trained over 150 Legg Mason employees on AI methodology across 3 continents.

Lead Data Scientist

Morgan Stanley

•	Designed, created, and implemented a fraud detection model that captured 40% of all fraud within a 2% turndown threshold, outperforming a custom model created by a third-party consulting firm of experts.
•	Developed and executed custom data science training programs for a team of 12 Morgan Stanley employees across two continents.
•	Trained 25 professionals on 2 continents in Machine Learning/ Artificial Intelligence.
•	Implemented custom Python tool to automate Exploratory Data Analysis for all future modeling efforts.
•	Managed a junior executive to become a full Data Scientist in under four months.
•	Wrote and executed Request for Proposal for Anomaly Detection for a $40 billion book of business.

Vice President of Data Science

Various roles 

•	Wide variety of predictive analytics assignments for over 25 clients, including developing almost 100 predictive models for industries such as banking, cellular, healthcare, hospitality, and non-profits.
•	Saved St. Jude Children's Research Hospital approximately 45% on mailing costs using an advanced modeling ensemble approach of an initial filter model, a secondary response model, and a final donation model.
•	Raised Sheraton Hotels’ response rates by more than 50% for destination promotions using advanced analytics to predict direct mail conversion.
•	Built web-based fraud detection models with sub-second decisioning for PayPal, evaluating up to 1 million purchases per day.
•	Created and deployed a unique Customer Analysis Protocol for Alltel Cellular’s marketing division, allowing immediate data mining “triage” for the entire marketing staff. 
•	Chief Data Scientist, Analytics-Edge, 2000 – 2021
•	VP Consumer Lending, Bank of Hawaii, staff of 7, 1997 - 1999
•	VP Credit Cards, CoreStates Bank, $7.8 million budget, staff of 4, 1995 – 1997
•	VP Credit Card Acquisition, Citicorp, Service Excellence Award, 1986 - 1995

Contract Consulting: Vice President of Analytics/ Data Science

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

#AI pioneer since 1986, currently working with University of Pennsylvania while studying at Harvard GSE. 🥳

Working to make #ArtificialIntelligence friendlier and more ethical!

Provided ROI results for, among others:

- Audi
- Biogen
- Cisco
- Los Angeles Times
- Mercedes
- Morgan Stanley
- Yale University

How may I help YOU??? 🙂

taplio

The Battle

Greg Coquillo

T. Scott Clendaniel

Followers

Posts

Engagements

Likes

Performance ScoreGreg Coquillo's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Greg Coquillo

T. Scott Clendaniel

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Greg Coquillo's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreGreg Coquillo's LinkedIn Performance