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

Ken Morimoto

Director, BD & Channel

Innodata Inc.

Leading community for AI leaders and builders in Seattle, Bay, and NYC.

Innodata helps GenAI and agentic AI innovators to build smarter, safer, and more effective models through multimodal, multilingual fine-tuning, context engineering, red teaming, and contingent workforce solutions.

Also, domain specific vertical SaaS with Agility PR and Synodex platform.

Director, AI Builders Collective & Growth

LEV (Leading Edge Ventures) & MVP

LEV & MVP is an advisory and seed fund supporting founders.

Fortunate to back 300+ early-stage ventures (pre-seed/seed) that have collectively raised ~$2B+ on follow on funding with top tier VCs.

Nothing ventured, nothing gained！

Partner

Perplexity

Business Fellow

AI Circle

The expert network you need to build an exceptional career in AI. Join us at ai-circle.org

Founding Member

AI Builders Collective

Hosting AI leaders and builder's events since 2018 with worldclass founders, operators, scientists, and investors. Please DM for invites.

Co-Founder

Centific

Centific is a frontier AI data foundry helps generate  high quality AI data and models. Our Data Studio, RAG Studio, Product Catalogue, AI Workbench and Safe AI Studio are used by “Magnificent 7” - Amazon Rufus, Microsoft Co-Pilot, Tesla, Uber, Apple to produce their models. 

I always enjoy learning from others. Please reach out via LinkedIn DM, 415-948-9098 or book a meeting.

Director, AI Data Foundry (Safe, Multi-Modal & Agentic AI)

Cobalt Robotics

Cobalt Robotics is the largest and only security robotics provider in the world that improves monitoring and judgment while delivering DAY1 ROI (30-50% savings).  Cobalt has raised $75M in venture funding from Sequoia Capital, Coatue Management, Bloomberg Beta, Promus Ventures, Founders Fund, and others.

GTM & BD Executive

Scale AI

Led BD for strategic accounts.  

Companies like Toyota, GM, NVIDIA, Samsung, Lyft, and OpenAI leverage Scale to
turn raw data into high-quality training data that powers their AI applications.

In addition to providing Labeling & Annotation for training data, Scale is developing solutions to meet the broader mission supporting the entire AI development lifecycle. This includes Scale Rapid (Self-Serve platform), Nucleus Data Curation, GIS/mapping solutions, and Models as a Service.

Global BD - Strategic Accounts

Appen

Led strategic accounts.  

Appen together with Figure Eight (CrowdFlower) is a global leader in the development of high-quality, human and ML annotated datasets for machine learning and artificial intelligence.

Client Executive

Algorithmia

Built BD strategy on growth marketing team.  

Enterprises depend on Algorithmia's (acq Datarobot) AI Layer to run their Machine Learning models efficiently and reliably in production, bringing elasticity and GPU acceleration to any framework, cutting deployment time from weeks to minutes.

Global BD

Mission/Purpose: Transformation & wisdom acquisition.

Passion/Interests:  Infinite learner with a passion for AI/ML, spacetech, robotics, data, science, history, biographies, documentaries, philosophy (Stoicism), TOE, quantum mechanics (Schrodinger eq), neuroscience (consciousness/freewill), investing, biohacking, fitness (xfit, krav, boxing), transformation and always looking to learn from others. 

*****
Let's Connect:
I always enjoy learning from others.  
- Book a meeting with me directly at https://calendly.com/iam-kenmorimoto
- Send me an email directly at: iam@kenmorimoto.com
After all, knowledge sharing and collaboration are our species' superpowers!

"You will be the same person in five years as you are today except for the people you meet and the books you read." Charles "Tremendous" Jones

Director & Client Executive - Safe AI, ML & Digital Transformation 

Centific is an “applied” AI & Data engineering services company, employee-owned, global (23 offices in 11 countries) with an extended reach of over 1M + contributors in >130 countries. 

Valued clients & partners include Amazon, Meta, Microsoft, Google Cloud, Databricks, Snowflake, Autodesk, PayPal, Expedia, VMware, Lowes, Home Depot, FedEx, Adobe, and > 100+ Fortune 500. 

Our Solutions: 
- SAFE AI: Red Teaming, RLHF, Prompt Authoring & Response
- Data Collection & labeling (CV, AR/VR, etc)
- Relevance Labeling (e.g. search, ads)
- AI-enabled Localization (up to 50% lower costs/efficiency with Honeybee)
- AI/ML (LLM) model training, fine-tuning, and Deployment
- Pitaya.AI (Retail AI & Analytics)
- Digital Safety (E2E Fraud Protection) 
- Digital Transformation (Custom Software and Engineering)
 
I always enjoy learning from others. Please reach out via ken.morimoto@centific.com, 415-948-9098 or book a meeting below.

First — deep gratitude to the veterans who made it possible for generations of dreamers and builders to pursue their ideas freely. 

Despite the noise and challenges, America remains the place people come to build things.

The week kicked off with a panel at NABE conference alongside Chi Wang (Google DeepMind, Founder of Autogen) and Madison Faulkner (Partner, New Enterprise Associates (NEA)), moderated by Fangfang Tan (Co-founder/CPO, AgentWeb).

We dug into a tough but necessary question:

Is AI truly reshaping enterprise productivity, or are we still chasing hype? Especially with that MIT study circulating — claiming 95% of GenAI pilots fail.

After that, two private AI dinners brought together leaders from Amazon Web Services (AWS), Microsoft, Genentech, Boeing, Innodata Inc., DoorDash, Aurora, Edge Case, and Dropbox. The conversation? Deep, reality-based, and unfiltered.

A few takeaways that stuck with me:
- 90% of enterprise data is still unstructured — the real reason so many GenAI pilots stumble.
- Eval frameworks matter. Teams who measure quality and business impact, not just model performance, are pulling ahead fast.
- The data scientist role is evolving — from training models to orchestrating systems and shaping products.
- The highest ROI is coming from domain experts who deeply understand the problem space — often principles and architects (e.g. 10x + AI code velocity).
- Human + AI collaboration isn’t optional; it’s the multiplier.

The pace of progress feels chaotic, but the signal is clear: the AI builders who combine rigorous evaluation with domain insight are quietly defining the next wave of productivity.

If you’re building in this space or want to join the next AI Builders Roundtable, drop your info here: 👉 https://lnkd.in/efw-TvhG

Tomorrow, we’re co-hosting another session with Pablo Casilimas and Christopher Kennamer from OneSixOne Ventures, Boundary (YC W23), and WAForge — bringing together founders, investors, and operators shaping what’s next. 

RSVP link in comments.

Happy Friday — keep dreaming & building!

Improving AI | ex-Scale, Amazon | Community Builder | Infinite Learner & Investor⚡️

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