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

AI Agent vs Agentic AI

Most people use the terms AI Agent and Agentic AI like they mean the same thing.

They don’t.

The difference isn’t just semantic.
It’s architectural.

Here’s how the tech stack evolves from AI Agent → Agentic AI 👇

1. Intelligence models
- AI Agent typically relies on a single LLM with prompt → response workflows.
- Agentic AI moves toward multi-model reasoning, planner–executor setups, and hybrid inference across systems.

2. Architecture & frameworks
- AI Agent often follows a single-agent, linear execution flow.
- Agentic AI introduces multi-agent systems, goal-driven workflows, and orchestration frameworks like LangGraph, CrewAI, or AutoGen.

3. Memory systems
- AI Agent works with session memory, short-term embeddings, and basic caches.
- Agentic AI adds long-term memory layers, episodic + semantic memory, knowledge graphs, and vector databases.

4. Tool usage & actions
- AI Agent uses predefined tools and function calling triggered by users.
- Agentic AI autonomously selects tools, plans multi-step executions, interacts with environments, and uses structured tool registries.

5. Knowledge & retrieval
- AI Agent typically uses basic RAG pipelines with static retrieval.
- Agentic AI evolves into adaptive RAG, context prioritization, hybrid search, and continuously updated knowledge graphs.

6. Orchestration & workflows
- AI Agent runs sequential flows and simple backend automation.
- Agentic AI uses orchestration engines, planning loops, event-driven workflows, and reflection cycles.

7. Decision making
- AI Agent is reactive and prompt-driven.
- Agentic AI is goal-oriented, with planning, self-evaluation, and iterative reasoning loops.

8. Deployment
- AI Agent is often deployed as chatbots, copilots, or API-based assistants.
- Agentic AI becomes autonomous platforms, digital workforce agents, and persistent execution systems.

9. Monitoring & observability
- Both need logs, monitoring, and error tracking but Agentic AI requires deeper analytics, response monitoring, and system-level feedback loops.

10. Learning & improvement
- AI Agent improves through prompt iteration and occasional fine-tuning.
- Agentic AI evolves through continuous feedback pipelines, performance adaptation, and evaluation frameworks.

AI Agent = intelligent responder.
Agentic AI = autonomous system with goals, memory, tools, and orchestration.

One answers questions.
The other executes objectives.

Are you building smarter responses or autonomous systems?

Anthropic made Learning AI easier.

They’ve released a complete set of FREE courses designed to help you understand Claude, AI agents, APIs, and the Model Context Protocol (MCP) - the foundation behind modern AI applications and agent systems.

This learning path covers everything from beginner concepts to advanced agent development workflows.

Here’s what you can learn:

• Claude 101 - Understand core features and everyday AI workflows
• Claude Code in Action - Use Claude to accelerate development tasks
• AI Fluency: Framework & Foundations - Learn how to work effectively with AI
• Building with the Claude API - Develop real AI-powered applications
• Introduction to Model Context Protocol (MCP) - Connect AI with tools and data
• MCP Advanced Topics - Deep dive into advanced implementations
• Claude with Amazon Bedrock - Deploy Claude in AWS environments
• Claude with Google Cloud Vertex AI - Production AI workflows on GCP
• Introduction to Agent Skills - Build reusable AI agent capabilities

These courses are especially useful if you want to learn:
AI Agents • MCP • LLM Applications • AI Automation • Developer Workflows

No fluff.
No paywall.
Just practical AI education directly from the company building Claude.

Get all the courses here:
https://lnkd.in/gAmrrsBK

If you’re serious about understanding how modern AI systems actually work, this is one of the best free starting points available right now.

Save this for later - you’ll want to come back to it.

Google just quietly released an AI tool for every layer of how humans create things.

Research. Design. Code. Video. Agents. Models.

All of it. At once.
Connected. Intentional. Ecosystem-scale. 

The tools nobody is talking about enough:

Jules - an asynchronous coding assistant that works across entire repositories, not just individual files. It understands the broader structure of a project and helps manage complex codebases.

Google A2A - a multi-agent communication protocol designed to let AI agents collaborate across different frameworks. This is infrastructure for the next generation of agent systems.

Stitch - converts prompts directly into complex UI layouts, turning ideas into interface designs within minutes. A major shift for how design workflows could work.

FileSearch API - a configurable RAG pipeline accessible through a single API call. What previously required weeks or months of engineering can now be integrated almost instantly.

Flow - a cinematic storytelling platform that lets creators generate scenes, narratives, and video content with AI-driven workflows.

Gemini 3 Pro - Google’s most advanced reasoning model so far, designed for deeper analysis, complex tasks, and next-generation AI systems.

This isn't a product launch.

It’s a full-stack AI ecosystem.

From foundational models → to research tools → to design → to development → to autonomous agents → to video creation.

Everything sits on top of the same AI foundation.

That means ideas can move from research → design → code → content faster than ever before.

The real strategy here isn’t individual tools.
It’s ownership of the entire creation pipeline.

And when one company controls every layer of that pipeline, the innovation speed compounds.

The AI race isn’t just about better models anymore.

It’s about who builds the ecosystem developers, creators, and companies build on.

People still use AI like a chatbot.
The real power starts when it becomes your execution layer.

Claude Code isn’t just another AI tool.
It acts like an operating system for building, testing, and shipping real work.

Instead of writing prompts and copying outputs, you can actually run workflows, analyze codebases, connect tools, and automate tasks from one place.

That’s the shift most teams haven’t fully understood yet.

Traditional AI tools help you think faster.
Claude Code helps you execute faster.

It moves through a structured loop:
analyze → plan → build → automate → scale.

You can connect repositories, files, databases, and internal tools.
Run commands.
Generate code.
Debug systems.
Automate repetitive work.
And build reusable skills that keep improving over time.

It also works across environments, which means you’re not jumping between ten different tools to get one workflow done.

The biggest difference?
It behaves less like a chatbot and more like a technical teammate that can actually take action.

When used properly, it becomes a working layer on top of your stack — not just a place to ask questions.

Most people experiment with AI.
Few build repeatable systems with it.

That’s where the real leverage is.

AI becomes valuable when it moves from answering questions to executing workflows.

Start treating it like an operating system for work, not just a prompt tool.

The agent gave a brilliant answer.
Then someone asked, “Where did that data come from?”
Silence. That’s when you realize intelligence needs infrastructure.

Agentic AI isn’t just reasoning.
It’s retrieval, validation, lineage, governance, and monitoring - all working quietly in the background.

Here’s what that foundation looks like:

- Data Ingestion
Pulls in structured and unstructured sources, standardizes formats, and attaches context before anything reaches an agent.

- ETL / ELT Pipelines
Transforms, validates, and loads data into usable systems so downstream workflows aren’t built on chaos.

- Data Versioning
Tracks changes, creates reproducible snapshots, and enables rollback when something breaks or drifts.

- Vector Pipelines
Chunks content, generates embeddings, and indexes it for semantic retrieval in RAG and agent workflows.

- Metadata Management
Registers datasets, records ownership, and enables search and observability across the ecosystem.

- Data Governance
Enforces policies, access controls, and compliance rules to prevent misuse and ensure accountability.

- Data Warehouse / Lakehouse
Stores refined datasets that power analytics, feature engineering, and intelligent querying.

- Streaming Data
Processes real-time events so agents can react, trigger actions, and operate dynamically.

- Data Lineage
Maps where data originated, how it transformed, and who depends on it.

- Data Quality & Reliability
Applies validation checks, detects anomalies, and keeps pipelines healthy under pressure.

At the center is the agent itself -
retrieving context, reasoning over data, planning actions, learning from feedback.

But without the layers beneath it,
even the smartest answer becomes a liability.

Intelligence is impressive.
Traceable, governed intelligence is enterprise-ready.

If someone asks where your agent’s answer came from, can you show them?

If you learned AI even three years ago, you’re already studying history.

The conversation has moved far beyond models and prompts. What we’re witnessing now isn’t just incremental improvement, it’s a fundamental shift in how intelligence is built, deployed, and experienced inside real systems.

AI didn’t suddenly appear with ChatGPT.
It evolved through decades of breakthroughs, each solving the limitations of the era before it - gradually transforming software from rule execution into adaptive intelligence.

Understanding that evolution is what reveals where AI is actually heading next.

From Rules → Learning
AI moved from hard-coded logic toward systems capable of learning patterns directly from data instead of predefined instructions.

From Learning → Representation
Deep learning and transformers introduced contextual understanding, enabling language comprehension, perception, and multimodal intelligence at scale.

From Models → Platforms
Foundation models and Retrieval-Augmented Generation turned AI into reusable platforms connected to dynamic, real-world knowledge sources.

From Assistants → Agents
AI progressed from answering questions to planning actions, reasoning through problems, and executing complex multi-step workflows autonomously.

Where AI Is Heading (2026+)
Intelligence is moving toward edge computing, embodied AI, persistent memory, and systems embedded seamlessly into everyday environments.

Every generation of AI didn’t replace the previous one, it stacked on top of it.

Many organizations struggle today because they attempt to adopt the newest layer without understanding the foundations underneath. Real advantage comes from recognizing how these layers connect:

Rules → Learning → Reasoning → Agents → Autonomous Systems

We’re not reaching the end of AI evolution.

We’re entering the phase where intelligence stops being a feature and becomes a system layer woven into everything we build.

#AI

If you think an AI agent is just an LLM connected to tools, you’re only seeing the surface.

What actually powers a production-grade AI agent is a layered intelligence architecture - where memory, reasoning, governance, and infrastructure work together to transform responses into reliable autonomous behavior.

Modern AI systems are no longer single models.
They are orchestrated systems designed to think, plan, adapt, and operate safely at scale.

This visual breaks down the internal anatomy of a real-world AI agent across three evolving layers:

From Execution → Coordination
The Basic Layer handles core operations — processing inputs, orchestrating prompts, invoking models, executing tools, managing errors, and delivering structured responses.

From Coordination → Reasoning
The Intermediate Layer introduces structured intelligence. Agents decompose tasks, plan execution strategies, validate outputs, manage context, monitor costs, and maintain observability for reliable performance.

From Reasoning → Autonomy
The Advanced Layer enables true production intelligence through long-term memory, multi-agent collaboration, governance guardrails, adaptive learning, self-reflection, and scalable deployment infrastructure.

Each layer doesn’t replace the previous one — it builds on top of it.

Organizations struggle with AI adoption not because models are weak, but because architecture is incomplete. Real AI capability emerges only when these layers operate as a connected system:

Execution → Reasoning → Governance → Autonomy

We’re moving toward a future where AI isn’t an application feature.

It becomes an operational layer embedded into every digital system we build.

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.

“There is no happiness. There is only concentration.” — Al Pacino

This quote resonated with me this morning, following a 3-hour dinner last night at Barons, enjoying peking duck with some incredibly talented friends Yucheng Low, ✨ Prashant Singh Ahluwalia, and Greg Coquillo. 

As we talked, one theme kept coming up — the joy of building and the thrill of solving hard problems. This is flow state/happiness which starts with focus/concentration!

We also ingested data across many topics - Claude’s real coding powers vs. the hype (See Yucheng Low posts on this), the rise of agents, and how AI might reshape the economy through job loss and creation.

We also talked about GenAI’s current limits — context/memory and the joy of building, from tools to a new chess AI app that Prashant created.

It was a fun and memorable night, and excited to co-host another AI leaders dinner next week! DM me if open to joining future events. 

Also excited to co-host the Seattle AI Summit with Pablo Casilimas next Thursday, featuring an incredible panel and speakers. Please see the link to learn more. https://luma.com/48ok1x9q

Missed you for this dinner, Chi Wang!

hosting extraordinary IRL events | ex-scale, amazon | infinite learner, vc & angel investor⚡️

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