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

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Head Community Evangelist

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Packt

Senior Content Marketing Editor

As a Content Marketing Editor in the Expert Network team and look after content marketing materials produced by and for Expert Network authors and their titles. I am responsible for a portfolio of content marketing assets for each title, liaising with Producers, Technical Editors, and the wider EN team, and individual authors and technical reviewers as necessary. I am also responsible for liaising with other teams in the business to make appropriate marketing content available, on a title by title basis, as requirements arise, and sourcing further material where there are gaps.

- Create a repository of content marketing assets for each title, visible and available to other stakeholders
- Maintain and develop further content marketing assets as required in support of the individual title marketing for each EN author
- Ensure the completeness, accuracy, and availability of all promotional title information and materials for Expert Network titles.
- Create and maintain the Expert Network forthcoming catalogue to share with internal and external partners.
- Create, source, and curate content for the Outreach team, PTN team, Direct marketing team, and social channels.

Responsibilities:

I work with the Producer and Technical Editor of a title, and other team stakeholders, to pre-emptively produce or curate the following assets for each title across the lifecycle of the project:

- Content articles for the Hub and other partner channels in phases right from pre-pub to post-pub.
- Generate podcasts for authors to talk about the topic and promote the book.
- Providing a suggested social content plan across Expert Network titles, for integration and execution by the Packt social team
- An early pre-release extract, a “sneak preview” in the month before launch, and a highly shareable article immediately post-launch.
- An article based on an author interview with questions provided by the editor.
- The content and images for an Amazon A+ page, based on the author interview.

Content Marketing Editor

- Primary and secondary research on new development in the field of tech
- Getting insights from an industry expert on your projects to give an edge and value to your project making it unique
- Preparing questionnaires for the authors and the clients on our product line
- In-depth market forecasting and analysis of various sectors
- Creating roadmaps while working closely with multiple stakeholders on different projects in accordance with the TAT
- Prepared market engineering sheets at regular intervals that help you visualize complex data via MS Office Tools
- Internal and external stakeholder management including presentation to CXOs
- Understanding the business need, evaluating various processes and controls present in the organization, communicating control deficiencies and other issues encountered during the research process
- Keeping a tap on the commercial viability of the products

Associate Category Manager

S K Patodia & Associates

- Look after different domains of the business with accordance to the clients
- Managing team and preparing questionnaires for the training of research
- Handling accounts and budgets related to the projects
- In-depth primary research related to the projects
- Handling clients PAN India, while closely associating with internal stakeholders
- Forecast new areas in terms of commercial viability

Assistant Manager Research 

Hexa Research Pvt. Ltd.

- Prepare reports on different domains such as media, ICT, Chemical, Tech, FMCG and consumer insights
- Handling client reports while closely working with different internal and external stakeholders 
- To prepare reports with accordance with the client requirements from across the United States, Europe, APAC, Middle East
- Market forecasting considering the factors affecting the industries by Primary Research
- Understand the budget aspects as per the client's requirement
- Training the team and finishing the projects in given turn around time

Research Associate

Reliance Industries Limited

Working for Indirect Taxation on CENVAT credit input.

Finance Intern

Founder & Host of "The Ravit Show". I love to interview Data/AI Leaders on The Ravit Show. Hosted 500+ Data Science/AI leaders on The Ravit Show.

Ravit Jain is well known for --

- Hosting live events
- Data Community Building
- Community Evangelism
- Product Marketer
- Share free resources with Community
- Helping Data Science/AI Community

As the Founder & Host of The Ravit Show, my mission is to illuminate the intersection of media and technology by engaging with Data and AI leaders. With a focus on new business development and compelling communication, I've established a platform that resonates with industry professionals and enthusiasts alike.

I leverage my expertise in B2B influence to foster thought leadership and community building. Our team is dedicated to sharing knowledge, driving conversations, and creating meaningful connections within the tech community, with a special emphasis on the burgeoning fields of Data and AI.

You can reach out to me at Ravitjain0909@gmail.com for The Ravit Show queries :)

Ravit Jain

Trust decides who wins with AI. Signals Summit 2025 shows how to build it. I can’t wait for this one. I have known Salma Bakouk and Sifflet Team for years now!!!! If your AI plans depend on reliable data, this summit is worth your time. Four days. Practical sessions. Fully virtual so your team can join from anywhere.

Why attend
 • Clear playbooks for AI-ready reliability across metadata, lineage, and governance
 • Real use cases and hard lessons from people who have shipped at scale
 • How observability connects to risk, compliance, and business impact
 • A common language leaders and engineers can use to align and move faster

All Star Line up of Speakers –
Sanjeev Mohan, Salma Bakouk, Tristan Handy, Tomas Kratky, Joe Reis, Pete Williams, Michael Thompson, Rosemary Leavy, Maxime Beauchemin, Seth Y., Jane Smith, Michel Tricot, Colin Zima, Alejandro Huertas Martínez, Solmaz Shahalizadeh, Ben Castleton, Maxime Cohen, Callum O'Connor, Mahdi Karabiben, Hannah Rounds, Lindsay King and many more.

Dates
November 17th to 20th

If you care about making AI dependable in production, block your calendar and bring your team. This is about trust by design, not hype.

#data #ai #sifflet #observability #signal2025 #agentic #agents #theravitshow

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