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.

Susan Walsh - The Classification Guru

Fixer of dirty data ★ Spend data classification, supplier normalisation, and taxonomies ★ DataIQ100 x 2 ★DataIQ Data Champion 2022★ Author "Between the Spreadsheets" ★ Creator of COAT ★ TEDx - say NO to NO ★ DataVenger

I’m Susan Walsh, also known as The Classification Guru, a globally recognised expert in spend data classification, cleaning, and transformation. For over a decade, I’ve helped organisations fix dirty, unreliable data and turn it into insight they can actually trust.

In 2017, I founded The Classification Guru Ltd, rebuilding my career from the ground up after coming back from bankruptcy after my first business failed. What started as a solo consultancy has grown into a specialist business supporting 100+ organisations globally, particularly in procurement, sales, and marketing, where clean data underpins reporting, automation, and AI.

I work with mid-sized and enterprise organisations, consultancies, and technology providers that rely on complex, high-volume data but struggle with inconsistency, duplication, and poor structure. My clients include procurement, finance, sales, and marketing teams who need data they can stand behind, whether that’s for spend analysis, supplier management, CRM reporting, forecasting, compliance, or AI initiatives.

Keeping a business alive and growing for almost nine years hasn’t been about shortcuts or hype. It’s been built through real-world delivery, practical problem-solving, and a refusal to accept “good enough” data as acceptable. Along the way, I’ve developed frameworks and tools to help organisations embed sustainable data quality, including the COAT framework (Consistent, Organised, Accurate, Trustworthy) and Samification, a self-service supplier normalisation platform launched in 2024 to make clean, structured data accessible at scale.

I’m also an active contributor to the wider data and business community. I’m a TEDx speaker, international conference speaker, and recognised thought leader in data quality and classification. I’ve authored three books, including Between the Spreadsheets 1st and 2nd editions, and the upcoming Optimizing Sales and Marketing. I teach globally via platforms such as Pluralsight, O’Reilly, and the Analytics Institute. Through consistent, practical thought leadership, I’ve grown an engaged LinkedIn audience of 43,000+ professionals.

Alongside my commercial work, I founded the Women Who Speak LinkedIn group, a free initiative connecting women speakers with event organisers to improve visibility and representation. I hope my journey shows that you don’t have to fit a mould to build a credible business, lead in your field, or create meaningful impact.

My mission is simple: help organisations move from spreadsheet chaos to clean, organised, trustworthy data that actually works.

Founder/MD (Mistress of Data)

I love improving the quality of procurement spend data, giving teams clarity so they can make better business decisions and cost savings.

Why not let me take the pain out of laborious data issues and make your life easier?

Whether it's a small or large data set, the accuracy can be improved by identifying classification errors, conflicts or gaps. I can provide a data verification service to efficiently find and correct these in a fraction of the time it would take you or a team member.

And a crucial part of having accurate data is also working with a comprehensive taxonomy, something that can be easily overlooked. I can provide a taxonomy development service to ensure you have all the vital areas covered for the greatest visibility - job done!

If you struggle with the time, resources or willingness to tackle your data, why not get in touch to see how I can make your life easier.

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Susan Walsh - The Classification Guru

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