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

Dataception Ltd

Creator of the Data Product Pyramid solution which enables self-service, collaborative creation, distribution and consumption capability of Data Products for Enterprises. Data Products are Big Data applications, ML models and BI Dashboards etc... it is delivered on hybrid on-demand cloud platform Jon has designed and coded a lot of himself. 

The solution has a platform that employs technologies such as Python/R ,Spark, Tableau and utilises native cloud (AWS, Azure and GCP) and Kubernetes (Openshift). The architecture was used to create a large-scale multi-tenant Big Data / ML platform for retailer and manufacturer, Lego, for their cross business analytics function.

Chief Technology Officer & Founder

Data Science Partnership

At Data Science Partnership I advised CDOs and Data leaders on adopting data strategies including Big Data / Data Science and Artificial Intelligence to embed insight as the foundation for decision making for their business.

Chief Data Officer

Databricks

Lead the EMEA Solution Architecture team for Databricks (the company that founded Apache Spark). Helping customers implement Big Data/ AI solutions with Databricks’ AI/Big Data platform (based on Apache Spark) on AWS & Azure.

Senior Director of Field Engineering, EMEA

PwC UK

Lead PwCs UKs FS Consulting’s Big data and analytics group, specialising in cloud applications, AI / ML and Big Data

Director - FS Consulting Big Data Lead

Lead GFTs Big Data Practice – Building end-to-end client solutions and creating capabilities in Data Architecture/ Data Governance / Data Science and Big Data including white papers.

Head of Data Science and Big Data Specialist

Rule Financial (Part of the GFT Group)

Running a team of specialists that create market leading solutions with capability around the following:
• Industry-leading Data driven architecture and solutions including state of the art in-memory MI and analytics platforms
• Defining big data architectures and technology stacks 
•Sector Specialised technical development expertise leveraging NoSQL databases  
• Data calculation engines – Creation of computing platforms to enable the processing of huge• volumes of data

Responsibilities include:
• Managing a team of Financial Services and BigData technical Specialists.
• Managing the day to day P&L of the practice.
• Creating and selling thought leading practice based propositions
• Creating thought leading white papers around BigData and Analytics

Responsibilities include:
• Managing a team of 30 Financial Services and BigData technical Specialists.
• Managing the day to day P&L of the practice.
• Creating and selling thought leading practice based propositions
• Creating thought leading white papers around BigData and Analytics

Head of Development Practice

Strategic initiatives include:
•	Defining the data and technical approach for BCBS239 - Effective Risk Data Aggregation And Reporting for Rules Risk Practice
•	Implementing a Data Strategy for a Wholesale Risk business 
•	Working with a Tier 1 Banks Enterprise Architecture group to help define the global data distribution mechanism 
•	Developed based Risk MI capability for Rule based on Hadoop , Cassandra and Acunu real-time analytics.
•	Market and Trade Data Fabric platform based on Coherence
•	Delivery on a white papers on Data fabric platforms and strategy 
•	Part of the team delivering a Big Data capability for Collateral Optimization based on real-time Analytics

The specific projects that Jon has been the project Solution Architect on include:  

•	Operations Enterprise BI/MI Data Initiative
Platform providing real-time BI/MI core Operations functions across 25 different business lines (e.g. Settlements, Regulatory Reporting, Confirmations, KYC) and 450 different systems. Jon's role includes: Defining the data strategy (models, data life-cycle taxonomy, on-boarding, data mart, staging and ETL), defining the data quality mechanism and standards, architecting the platform (including: technology selection, system, integration and data architecture) and defining the interfacing standards and approach. 

•	Wholesale Risk Business Data Strategy and Platform
Whole Risk business (including Investment bank, Wealth and Corporate) large scale (200Tb, 800 integration points) data platform for storage and servicing of full Market and Credit Risk Lifecycle – Sanctioning, RWA, DVA,CVA, Reporting,  BASEL III, VaR, PFE/EE/Stressed EE data. 

•	Global golden source Reference Data Platform and Strategy. 
Jon's architecture provided counterparty/legal hierarchy web and messaging interfaces for a portfolio of systems across the whole enterprise. Global Reference Counterparty data including hierarchies, SSIs, with MDM, On-Boarding and KYC business processes.

Principal Consultant

Jon’s has been the Architect on a variety of projects across the Global Markets business and also been part of or driven a number of strategic in-flight initiatives that are helping drive the Global Markets technology strategy for numerous Banks. Jon’s day-to-day functions are very diverse, from defining I.T. strategies to producing the architecture for Multi-million dollar projects to coding components directly.

Strategic initiatives:
•	Global Markets Middleware strategy including: Architecture, SDLC, governance,team structures
•	The Global Markets Technology operating model i.e.  Global Markets delivery, architectural Governance/support existing new platforms.
•	Driving out the Institutional/Enterprise Architecture standards and appropriate technology
•	Tactical/strategic remediation Murex stabilisation

Projects
•	Enterprise Global data fabric for Trade/Market data for the whole Investment bank to use globally
•	Leading the creation and development of an Enterprise data fabric based on GemFire for trade and Market Data for the use of the whole Global Markets business
•	FX Based Margin trading platform (FX/Commodities)
•	Implementation of the Dodd Frank regulations to the OTC Derivatives platform for Swaps in the Fixed Income business.
•	Global Markets Portal (GMP) – Global Markets e-commerce offering 
•	Warrants platform (Horizon Market Maker) used by the Equities Derivatives desk based in Hong Kong

Solutions Architect

Rule Financial

Strategic initiatives:
•	Enterprise Architectural Governance around TOGAF
•	Creation of an Agile based SDLC based on EssUP
•	Delivery on a white paper on Data fabric strategy
•	Assistance of the set-up of a near-shore Support capability for Rule Financial
•	Extensive pre-sales engagement and involvement in the Sales life-cycle
•	Development of propositions and Sales collateral

Specific projects:
•	Architecting a cross Asset Class derivatives market Data System that is used in a Tier 1 bank across the whole of the Businesses.
•	Leading a 27 Million Euro Front-to-Finance business change initiative for a Dutch based bank based.  
•	Architecting an implementation of a new front-office electronic trading system for a joint venture between Morgan Stanley/Goldman Sachs/Merrill Lynch trading in Exchange traded funds (ETFs). 
•	Developing, designing and implementing a FIX 4.2 based DMA capability
•	Managing a large team (including architectural and development responsibilities), that implemented a cross asset derivatives pricing and trading system for the front-office of a tier-one Investment Bank. 
•	Architecting a Counterparty golden source system for an entire Tier 1 Investment bank. 
•	Implementation of two financial payment systems for two tier-one Banks.

Principlal Consultant

Iona

This role involved using Agile methodologies to develop multiple SOA based solutions using a leading Enterprise Service Bus (ESB) for blue chip organizations. In this role I delivered many projects using the above technologies. One project was for a financial services company building a system framework integrating a Web Services stack with Spring/Nano/Pico containers and a Hibernate persistence layer. Another was producing a prototype of an application using Java Server Faces, Tangasol Coherence and AJAX (full SDLC with UML).

Galileo (CendantTDS)

A very high profile regional Lead Systems Architect position. Responsibilities included architectural  accontability for all IT projects carried out by a very large Global Travel Conglomerate ( Cendant TDS ). The role was created to promote good architectural practice in the region and to be a conduit for a Service Orientated Architecture global strategies.

Asia Pacific Systems Architect

Business Data Products | Data Product Storytelling | Data Product Pyramid | Data Product Marketplace

Ask me about delivering the Data Product Pyramid, a solution that uses generative AI and advanced analytics with technology approaches such as Data Mesh and Data Fabric to deliver the “Composable Enterprise”: a low friction, adaptive eco-system of re-configurable data products that runs at the speed of the business.

This includes 
1- Using LLMs/AI to create advanced analytical and Data Products in reconfigurable eco-system
2- Creating a Lingua-Franca that allows business teams to communicate analytics asks with data and tech teams.
3- Creating an Adaptive Business Strategy - The business strategy can start small and evolve and adapt as the business changes.
4- Implementing as a rapid delivery, highly valuable data and analytic use-cases as Data Products
5- Creating A portfolio of actual and specific real world Data Products that can used and re-used across the business.
6- Creating a Data Product Marketplace where internal and external customers can create, prototype, search and use data products, including: Datasets, ML Models, BI Dashboards, Graph analytics
7- Creating hybrid cloud polyglot data infrastructure to deploy Data Products

Jon Cooke

LOL I love designing AI Digital twins but - Oh boy, the first go, that AI has, does sometimes make me laugh.

Me: That's great but waaaayyy too complex, let's break this down.

Ended up with 5 separate twins - 4 business ones and a admin one, all with a bunch of re-usable Data products (from datasets to ML models to sophisticated decisioning components).

It's so cool to be able to visually design e2e workflows as full operating models and then test quickly them out, piece by piece with real data.

This would traditionally take months and a whole design team, but really quick with AI being my design partner. 

We really are in a new age

Preview your AI native business with Nebulyx AI (AI Digital Twins) and A.D.O.P.T. - AI, Data Object Graph and Product thinking | Data Product Workshop Podcast host

Sooo I'm having a crack at the dreaded KYC process with AI and trying to avoid the Parcha problem.

If you haven't read "Agents aren't all you need" then I would definitely suggest you do, as it really hones in on the challenges of using Agents for these types of use cases.
(Link in comments)

Their key findings were:
• Unpredictability: Every run creates a different path, making failures impossible to predict or prevent.

• Complex Interdependencies: One error cascades through the entire system like dominoes falling.

• Artificial Performance Bottlenecks: Parallel tasks wait in line because the agent keeps checking its notes.

• Evaluation Challenges: When everything changes every time, you can't tell what broke or why.

It made great reading, but I didn't really appreciate it until I started building their actual use case.

What I've found is, there are a lot of moving parts - e.g., complex chains of authorizations, complex interactions with 3rd parties, lots of steps and decision points.

But I estimate that approximately 70-80% of the processes are linear and static, so I can totally understand the challenges they surfaced.

But that doesn't mean we can't use AI to massively re-imagine the flows and use models for key parts of them

e.g., All the document extraction, classification and scoring, impact assessment, speech-to-text for customer interaction, SLA forecasting, and so on.

This sooo suits the AI Digital Twins on Nebulyx AI:

No we can use Agents, but for this use-case we need to address Parchas key challenges:

• Predictable: AI generates pyramids as flows (Data Object Graphs - DOGs) and then tweaked by SMEs. Executions are predictable, meaning same path every time, using AI to accelerate the bits it's good at.

• Interdependencies: Each pyramid flow contains data products where errors are measured, controlled and not propagated.

• Bottlenecks: Parallel processing is done through a stable event-driven architecture - i.e., each data product executes in its own process space.

• Evaluation: Every component measurable and tracked independently - you always know what broke.

We are using about 10-20 AI different (small) models but alongside metrics, decision models and other more classic components all deployed as Data Products with the DOG as the business process.

We call this AI-On-Rails! 

This is the essence of AI Process Engineering - working out the right approach for AI for the use-case

Sometimes you don't need to go full Agentic which works 80% of the time (Parcha quote)

All the benefits of AI without the drawbacks of probabilistic systems

My session on Building AI Digital Twins with Data Products from Big Data LDN 2025 is live!!!

So excited, very honoured and grateful to Mike Ferguson and the whole team for hosting me at this excellent conference.

In this session, I go through our whole process to how we answer big business questions and re-imagine business operating models using AI native approaches in non-salesy way.

If you are a senior leader who has big questions about your business, you can't ask today then this shows how we do it with our Nebulyx AI platform and our A.D.O.P.T. (AI Data Object Graph and Product Thinking) approach.

Link in the comments

Building AI Digital Twins with Data Products - Nebulyx AI

AI is not replacing developers, not yet

I was doing an enhancement to the Nebulyx AI platform code base for a customer AI Digital Twin use-case.

Its really cool, optimisation of scenarios - pull levels on their data to give different cashflow yields - more on that later.

With the help of my great AI code co-pilot - I'd made the change to our awesome SQL virtualisation layer (specialised correlated subqueries). 

This layer is the killer feature for Data Products .

Code complete - check
Unit tests passed - check

Then I promoted to the full platform test environment and the code failed.

I asked the AI Co-Pilot what was going on and was confident that it was the new code and kept on insisting on changing it.

But it wasn't.

I did some deep debugging and found it was a bug in the meta-data being passed thru the API of the Data Product Containers in the test platform. 

I.e. nothing to do with the new code.

So I managed to fix it, (the old fashioned way), but this process shows two things,

1- In the hands of a senior Dev, Co-Pilots are a massive accelerator - think jet-pack for a some-one trying to get from A-to-B

2- One still needs expertise if that jet-pack goes of course i.e. Senior dev skills are still required. For simple projects one may be able to get away but for sophisticated solutions we need still them 

Obviously we need to create the next generation of senior devs in an AI world, but that is for another post.

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