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

Susan Walsh - The Classification Guru

The Classification Guru Ltd

I love improving the quality of procurement spend data, giving teams clarity so they can make better business decisions and cost savings.

Why not let me take the pain out of laborious data issues and make your life easier?

Whether it's a small or large data set, the accuracy can be improved by identifying classification errors, conflicts or gaps. I can provide a data verification service to efficiently find and correct these in a fraction of the time it would take you or a team member.

And a crucial part of having accurate data is also working with a comprehensive taxonomy, something that can be easily overlooked. I can provide a taxonomy development service to ensure you have all the vital areas covered for the greatest visibility - job done!

If you struggle with the time, resources or willingness to tackle your data, why not get in touch to see how I can make your life easier.

Founder/MD (Mistress of Data)

I love improving the quality of procurement spend data, giving teams clarity so they can make better business decisions and cost savings.

Why not let me take the pain out of laborious data issues and make your life easier?

Whether it's a small or large data set, the accuracy can be improved by identifying classification errors, conflicts or gaps.  I can provide a data verification service to efficiently find and correct these in a fraction of the time it would take you or a team member.

And a crucial part of having accurate data is also working with a comprehensive taxonomy, something that can be easily overlooked.  I can provide a taxonomy development service to ensure you have all the vital areas covered for the greatest visibility - job done!

If you struggle with the time, resources or willingness to tackle your data, why not get in touch to see how I can make your life easier.

Founder

Data Collaboration Alliance

I am a member of the Advisory Committee at the Data Collaboration Alliance, a nonprofit organization that’s dedicated to establishing CONTROL as the basis for meaningful Data Ownership and global Collaborative Intelligence. Our approach of eliminating data silos and copy-based data integration is similar to how societies already protect the value of their currency, identities, and intellectual property - and it works for data, too. My fellow advisors include the Executive Director of the Mozilla Foundation and the ex-CIO of Dropbox. I'm also a contributor to the Data Collaboration Community which offers open datasets, free tools, and free training in the data-centric skills of the future. Visit: https://www.datacollaboration.org/community

Advisory Committee / Instructor

Facet Publishing

Dirty data is a problem that costs businesses thousands, if not millions, every year. In organisations large and small across the globe you will hear talk of data quality issues. What you will rarely hear about is the consequences or how to fix it.

Between the Spreadsheets: Classifying and Fixing Dirty Data draws on classification expert Susan Walsh's decade of experience in data classification to present a fool-proof method for cleaning and classifying your data. The book covers everything from the very basics of data classification to normalisation, taxonomies and presents the author's proven COAT methodology, helping ensure an organisation's data is Consistent, Organised, Accurate and Trustworthy. A series of data horror stories outlines what can go wrong in managing data, and if it does, how it can be fixed.

After reading this book, regardless of your level of experience, not only will you be able to work with your data more efficiently, but you will also understand the impact the work you do with it has, and how it affects the rest of the organisation.

Written in an engaging and highly practical manner, Between the Spreadsheets gives readers of all levels a deep understanding of the dangers of dirty data and the confidence and skills to work more efficiently and effectively with it.

Author - Between the Spreadsheets

CONSTELLIA LIMITED

Connecting Professional Services Procurement people to the Consultancy Procurement Council

Membership Manager

Simfoni

+ Lead spend analytics project delivery across Europe & Middle East
+ Project Implementation​
+ Delivery of product demos
+ Development and delivery of training for Analytics team
+ Product development support
+ Spend analytics
+ Taxonomy development​

Senior Analytics Project Lead - Europe & Middle East

Highways England

Project Support Officer

Philips

Data Verification

+	Administration of product database
+	Working with cross functional teams to resolve issues
+	Develop and deliver training for business hubs to improve processes

Data Product Researcher

Coupa Software

+	Project managing multiple projects simultaneously ensuring all were delivered within time and on budget
+	Management of a team of 14 QA staff
+	Developed and implemented training plan and best practice documents
+	Recruit and train new staff
+	Ensuring consistent quality of work  produced by team

QA Manager

Spend360

+	QA, classification and review of data
+	Excellent attention to detail
+	Managing workload to tight deadlines

Data Services -QA

Fixer of dirty data ★ Spend data classification, supplier normalisation, and taxonomies ★ DataIQ100 x 2 ★DataIQ Data Champion 2022★ Author "Between the Spreadsheets" ★ Creator of COAT ★ TEDx - say NO to NO ★ DataVenger

Are you struggling to get reliable insights from your data?  Do you have duplicates, multiple versions of the same supplier or customer, and multiple date and address formats?  And is it saved in multiple different systems?

The Classification Guru can help.

If you’re in procurement, do you find it hard to see how much you are really spending with one supplier, and on what?  Have you got free text descriptions which mean you have multiple versions of the same item, often misspelt and not against the correct GL or categorisation? How can you make the best negotiations with your suppliers, the biggest cost savings, or manage your supplier list if you don’t have the correct information?

Or if you work in sales or marketing, is your CRM full of junk data, poorly formatted and even missing information, and maybe saved in multiple different systems?  Your team could be wasting time contacting redundant leads, or worse you could be sending out expensive print out to the same address twice, or addresses that don’t even exist.

We bring clarity and accuracy to data and help teams work more effectively and efficiently, finding cost savings through spend and time management, supporting better, more informed business decisions, delivering a strong ROI and delivering back to your organisation faster and more cost-effectively than using large consultancies or spend analytics providers.

While most people dread having to classify or cleanse data, my team and I love it! We are hugely passionate about providing clients with accurate and meaningful data, and because I have been doing this for a decade, I can do this much faster and more accurately than people who might look at their data infrequently.

We can help prevent costly mistakes from happening.

There are many solutions available to automate data classification, but these are never 100% accurate. I've developed a method that my team use to efficiently and accurately classify, cleanse and verify data to minimise the errors in the same amount of time.

When working with large volumes of data it's hard to spot these errors even if you know what you're looking for and especially in Excel, let alone if you don't. With my team and I’s knowledge, experience and tools we can do this for you, freeing up your internal resources.

Fix your dirty data now - www.theclassificationguru.com

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Susan Walsh - The Classification Guru

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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

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