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

No alternative text description for this image

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

Clube FII

🔥 Análise automatizada de carteiras de ativos e investimentos, integrando dados atuais do mercado com históricos, usando IA generativa (LLMs) e bancos de dados relacionais (PostgreSQL, SQL Server).

📊 Desenvolvi pipelines de dados com LLM, permitindo análises comparativas entre ativos e avaliação de impactos de comunicados e dados do mercado nas carteiras dos investidores.

📉 Reduzi o tempo de leitura e interpretação de comunicados em 80% com sistemas de resumos automáticos baseados LLM.

🤖 Criei agentes IA para geração de relatórios financeiros utilizando engenharia de prompt avançada.

🌐 Integrei IA com o front-end (React.js) e APIs RESTful, garantindo comunicação fluida entre módulos.

⚡ Otimizei views SQL e consultas complexas, acelerando relatórios em até 85% e evitando custos com upgrades de infraestrutura.

🚀 Automatizei webcrawlers para extração contínua de dados do Fnet, Banco Central e sites financeiros, com orquestração via Azure Pipelines e armazenamento em AWS S3 e Banco de dados.

🔄 Implementei CI/CD para garantir entregas ágeis e seguras.

🗓️ Colaborei com POs e participei ativamente de dailies, refinamentos e plannings, assegurando entregas alinhadas às metas de produto

📈 Atuação próxima ao PO e demais stakeholders para traduzir necessidades de negócio em soluções robustas

Tech stack: Python (FastAPI, Flask, Pandas, NumPy) • React.js • APIs RESTful & GraphQL • Azure, AWS, Docker • CI/CD (GitHub Actions, Azure DevOps) • Hugging Face • LangChain, LangGraph, RAG, vector DBs.

Especialista em IA

- Gereis insights de clientes e oportunidades de negócio aplicando ciência de dados, estatística e inteligência de mercado.

- Apliquei machine learning, deep learning e data mining para análises preditivas e segmentações.

- Implementei pipelines de deploy com Azure Functions, entregando soluções escaláveis.

- Executei web scraping com Scrapy, modelagem de dados e consultas SQL otimizadas.

- Atuei com arquitetura limpa, SOLID e testes unitários, garantindo qualidade contínua.

Data Scientist

- Desenvolvi páginas de dados financeiros e painéis interativos com VB.NET, JavaScript, HTML e CSS.

- Realizei ingestão de dados via APIs JSON e manipulação eficiente com SQL Server, processando ~400 itens em apenas 3 segundos.

- Modelei lógica de extração, transformação e carga (ETL) com alto desempenho.

- Atuei no onboarding técnico de desenvolvedores iniciantes, com mentoria em lógica de programação, Git, boas práticas, resolução de bugs e soft skills.

Software Engineer

self-contained

Civil Engineer

Construtora Nobe

Trainee

Seu Software

Engenheiro de software

Data Science

Especialista em Inteligência Artificial e Automação, com expertise em construir agentes inteligentes, pipelines de dados e integrações corporativas usando LLMs como ChatGPT, Gemini, LLaMA e frameworks como LangChain, LangGraph e Hugging Face.

Minha missão é transformar fluxos de trabalho complexos em automações inteligentes com RAG pipelines, embeddings e vector databases (Pinecone, Weaviate, FAISS).

💻 Tech stack:
Python (Flask, FastAPI, Pandas, NumPy) • SQL (PostgreSQL, SQL Server) • APIs RESTful & GraphQL • Front-end (React.js, JavaScript, TypeScript, HTML5, CSS3) • Azure, AWS, Docker, GitHub Actions, Azure DevOps (CI/CD) • LLMs (ChatGPT, Gemini, LLaMA) • Hugging Face, LangChain, LangGraph, RAG pipelines, embeddings, vector databases (Pinecone, Weaviate, FAISS)

🚀 Projetos recentes com LLM:
🔹 Análise automatizada de carteiras de ativos e investimentos combinando dados atuais do mercado, séries históricas e ativos existentes para gerar insights estratégicos.
🔹 Modelagem de impacto de comunicados oficiais (CVM, B3, Fnet, Banco Central) sobre carteiras, identificando padrões e tendências.
🔹 Construção de comparativos dinâmicos entre ativos, integrando dados históricos e tempo real em bancos de dados SQL Server e PostgreSQL.
🔹 Desenvolvimento de pipelines de dados para análises contextuais ricas e geração de relatórios em linguagem natural.
🔹 Criação de agentes IA para geração de conteúdo, assistentes virtuais e análises preditivas, entregando soluções escaláveis e auditáveis com supervisão humana.


🎯 Objetivo: desenvolver soluções inteligentes que transformam dados complexos em decisões estratégicas, com ética, performance e visão de longo prazo.

taplio

The Battle

Greg Coquillo

Tulio Vargas

Followers

Posts

Engagements

Likes

Performance ScoreGreg Coquillo's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Greg Coquillo

Tulio Vargas

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Greg Coquillo's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreGreg Coquillo's LinkedIn Performance