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

Debbie Botha

Digital Data Design (D^3) Institute at Harvard

Inaugural member of the Digital Data Design Institute Industry Council at Harvard Business School. Providing guidance and expertise as we move forward cutting-edge research in the space of AI and digital technologies.

Industry Council Member

Ai 101

AI 101 focuses on Data, Analytics and AI coaching to like-minded groups of people to increase business value.  We have given coaching sessions to Boards of Directors, C-Suites, Teachers to date with great practical outcomes.

Co-Founder and CEO, AI 101

Thousand Faces

Founding Board Member

Dalebrook Media

Inspire, Educate and Connect Data, Analytics and AI Leaders in the Middle East

Co-Founder and Managing Director, ME

Women in AI

Fostering a formidable community where there is massive opportunity for the women and men in AI, and the partners, working together.  It will increase the numbers and eminence of women in AI in the educational institutions, and pave the way for successful careers in Governments, Startups, Industry Companies, Vendors and other institutions.

Chief Partnership Officer

Actively involved in the Worldwide Non-Profit Organization Women in AI, with a wonderful team of ladies in the Leadership team representing UAE.

Women in AI Ambassador for UAE

Debbie creates and maintains strong, trusted client relationships at all levels within the client's business. She designs solution recommendations for her clients applying her broad technical skills, industry knowledge and business acumen expertise. These recommendations leverage IBM technologies, architectures, integrated solutions and offerings to solve the client's business challenges and deliver value.

Executive Information Architect, Client Technical Architect, Global Markets, Middle East

Part of the Strategic Client Engagement team, within IBM Analytics Group CTO.

Executive Information Architect, Strategic Client Engagement

As part of the IBM Analytics Group CTO, Strategic Client Engagement, our team works across global geo's to strengthen the AG CTO Office's bond with local clients and IBM teams

Executive Information Architect

Senior Consultant, Global Business Services, Business Analytics & Optimisation

Senior Managing Consultant

Discovery Health

Member of the Technical Steering Committee of Discovery Group, BI Architect of Discovery Health responsible for Maturity, Strategy, Information Architecture, Implementation Methodology and Mentoring the Delivery of a New Data Warehouse and BI Reports and Dashboards.

BI Architect

T-Systems

Senior Business Analyst and Architect at Eskom BICC

Senior Consultant

Business Intelligence Solution Architecture, Data Modeling, ETL design and development, Financial Intelligence, Activity Based Management, Strategic Performance Management, Data Quality, Forensic Audits

Principal Consultant

QINFO Solutions

Solution Architect and Project Manager for SAS projects at eBucks

Solutions Architect and Project Manager

Member of the D^3 Institute Industry Council at Harvard Business School, Dalebrook Media Middle East Managing Director

I have directly managed data and AI teams form small 3 person teams to large 30+ person teams throughout my career.  As a consultant, I managed more than 10 large and complex data, analytics and AI projects from strategy to successful implementation, including assembling multinational teams of experts to ensure success.  As manager in Industry Organizations, I managed 3 large data and AI teams to successfully foster data value driven organizations.  I leverage my deep understanding of the successes and challenges of Data, Analytics and AI Leaders to help elevate the impact of these Leaders in Industry Organizations and help them grow in their careers.  

 I am passionate about producing small and intimate to large and complex events from agenda, speakers, audience,  partners, sponsors, logistics, and promotions to execution.  In the last 2 years I have produced  12 intimate executive luxury experiences, 5 large and complex events and  7 virtual events.

I have extensive experience in giving coaching to executives, large corporate audiences and architects.  I use design thinking and experiential learning techniques and love engaging the audience.  I hold workshops with C-suite executives to develop a Data and AI Strategy to execute the Business Strategy.   I hold roundtables to facilitate executives, and relevant stakeholders discuss hot topics, challenges and successes to help them learn from each other. 

I help others grow in their careers by mentoring, teaching and speaking at Events, and developing communities like Women in AI with 40K members in 150 Countries.  On the Board of Directors I formed a team of 20 executives, driving partnerships for WAI across the globe.  I inspire, educate and connect my community of 130+ CDAOs in the Middle East.

I am a Digital, Data and AI Coach on being data and AI value driven in a very practical way.  I help companies develop and implement Chief Data, Analytics & AI Offices with Data and AI Strategies, operating models, teams and Architectures.  I help Organizations to implement Data and AI Literacy programs across the organization.

While at IBM (2013 to 2021), I had several leadership roles.  I was an Executive Information Architect, a Client Technical Lead in UAE, was in the Worldwide Client Engagement Team reporting to the Global Analytics CTO and before that a Senior Managing Consultant in South Africa.

🔥Wow, just wow! What impressed me most about Datalytics Dubai last week, was that our brilliant busy UAE Data, Analytics and AI Leaders engaged full steam in a packed room from start to finish.

🎯As one of the younger Leaders told me: “The sessions served as a masterclass in everything Data and AI for me”! That is what Datalytics is all about – for the community, by the community and with the community – a platform to grow and shine.

💎Our fantastic Master of Ceremony Shailesh Jain kept us captivated and engaged throughout the day.

❤️‍🔥 Our Table Discussion facilitators discussed absolutely relevant topics: Shehla Tanveer, Omar Aboutaleb, Busisiwe Tshabalala-Phenye  (MBA, CMDP), Rewin Koul, Waseem Akram Syed, Harpreet K. Dev (AI Governance and Risk Management), Mani Abedini, PhD., and Ghanashyama Mahanty, PhD.

🎯Peter Allan analysed the data from the 82+ responses of our “Impact of Data, Analytics and AI Leaders” survey and produced and shared the brilliant Datalytics edition of the insights.

💎Our Platinum sponsors and their speakers truly engaged the audience – Mustafa Aydogdu of Snowflake, Arsalan Sheikh of Capgemini Data and AI, Tony TAWK of MongoDB and Joe Aoun of APGAR. You made your keynotes memorable!

❤️‍🔥 Our esteemed panellists Dirk Jungnickel, Sohail S. Gyan Prakash Srivastava, Dr.Yoonus Ahammed, Gopi Maren and Dhiman D. made sure that the audience, their peers join in the discussions. Karl Fischer 🇺🇦 of our Gold Sponsor DVT group expertly moderated his panel.

🎯 Our Leaders who gave brilliant lightning talks were Phillip Badenhorst, Busisiwe Tshabalala-Phenye  (MBA, CMDP), Kashish Suri, Ezzeddine Jradi and Abd El-Rahman Moawad.

🔥Huge thank you to each and every Leader who shared their thought leadership and engaged throughout the day. You are truly a force to be reckoned with.

❤️‍🔥Proudly Dalebrook Media Middle East, Craig Steward

Thank you for the amazing photos Manna van der Bergh

Member of the D^3 Institute Industry Council at Harvard Business School, Dalebrook Media Middle East Managing Director, Co-Founder and CEO, AI101

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