Dare to Data

I founded Dare to Data to formalize and structure the relationships I have with some of the world's leading companies to consult, support and empower them in their data and AI journey.

Current and past clients include PepsiCo, PUMA, Whirlpool, Nitto, Amazon Web Services, AtScale, EM360, O'Reilly Media, nexxworks, Aporia, Colquímica, DATAcated Plus, The Ravit Show, Ontotext, Growth Tribe, IBM / Databand, CoRise, SingleStore, Equals, Einblick, Impetus, Nexla, Starburst, ClearML, Nvidia, Dubai Future Foundation, ThriveDX, Global DIY-Summit, Afera, Prophecy, Listly, Dremio, amongst others.

Founder

LinkedIn

Over 65,000 learners to date! Building and teaching content as a part of the data course library, currently focused on empowering others to take full control of their data through the use of SQL as well as how to tap into the power of analytical thinking.

LinkedIn Learning Instructor

IE Business School

Delivering keynotes for the US, Spain, Canada, Portugal, Russia, Armenia, Georgia, Turkey, Azerbaijan, Jordan, Belgium, Luxembourg, Norway, Switzerland, Chile, Greece, Japan, South Korea, Nigeria, Egypt, Lebanon, China, Indonesia, Saudi Arabia, France, Cyprus & more.

International Speaker

Graduate-level courses taught primarily within the MBA programs covering data and artificial intelligence topics, as well as personal branding workshops.

Adjunct Professor, Department of Information Systems and Technology

Leading the Corporate Masters program for Saudi Aramco employees.

Academic Director, Saudi Aramco Master in Business Analytics and Big Data

Leading the online Executive program which explores the range of possibilities offered by using new technologies and aligning digital strategy with general strategy in a company.

Academic Director, Digital Strategy and New Technologies Executive Program

Porto Business School

Graduate and executive level courses taught in business strategy for data and artificial intelligence.

Adjunct Professor

ISDI

Launched, continue to coordinate and teach the Data & AI module within one of the flagship programs at ISDI - the Master in Internet Business in Madrid, Barcelona and online.

Data & AI Module Program Manager

Regularly lecturing in advanced analytics topics at the Masters level and Executive programs in ISDI, an international institution focused on digital transformation.

Lecturer

VN Campaign Partners

Partner

Google

Developing insights and tools to empower our teams to work smarter and our clients to get the most out of Waze Ads. Leading analytical studies and strategy across North America for some of our largest advertising verticals (Fuel, Auto Parts & Services) as well as non-endemic verticals such as Entertainment. Creating scalable data solutions via daily use of SQL, BigQuery, Looker, CARTO, GSheets.

Analytical Lead, Waze

As a part-time facilitator, teaching engineers & analysts how to apply statistical methods to big data problems (Hypothesis Testing, T-Tests, Multiple Linear Regression, Logistic Regression, Centered Moving Average, Causal Impact, K-Means Clustering).

Master Faculty - Data Science I

Leading complex data projects, automation and optimization with our most advanced international clients cross-office.

Data Specialist

#IamRemarkable is an initiative empowering women & underrepresented groups to speak openly about their accomplishments in the workplace & beyond, thereby breaking modesty norms & glass ceilings. Workshops hosted for the UN World Tourism Organization, Amadeus, CARTO, IE Business School, LastMinute, Teleperformance, Google Campus - Digitalizadas.

#IamRemarkable Facilitator

Combining Google Search data with advanced analytics and Cloud tools to drive business impact. Working with a $xxx-million client portfolio.

Analytical Consultant

Nielsen

Maintaining data integrity and leading projects in data process optimization, as well as supporting a migration to Big Data technologies (Google Suite). Delivering both internal and external seminars and training courses around data topics.

Expert Engineer in Project Development and Big Data

Graduate Intern: Associate Systems Engineer

Graduate Fellow

Data Whisperer ♦ Analytics Specialist & Tech Enthusiast ♦ Join My #bookaweekchallenge

📚 Follow & join my #bookaweekchallenge
📍 Current resident of Málaga, Spain & native of Charlotte, North Carolina

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Greek-American currently living in Spain, helping companies harness the power of data. Driven analytics professional & strong supporter of lifelong learning. I hope to bring advanced data technologies to the masses & exploit the potentials of emerging technologies. I am bilingual & passionate about international relations. Avid reader (bibliophile) & traveler. With trips to 60+ countries & first-hand experience working in a variety of cultures & languages, I enjoy & thrive when working in an international environment.

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Alongside my corporate work, I am a regular international speaker supporting data analytics, emerging technologies & women in STEM. Some of my latest involvements include:
● Guest appearances on more than 30 podcasts such as SuperDataScience, Ken's Nearest Neighbors, How to Get An Analytics Job, Driven by Data, Humans of Data Science, Present Beyond Measure, LightsOnData & more.
● Interview features, guest lecturer, panelist and event host!
● Keynote speeches delivered regularly since 2018 across 15+ countries in-person, virtually spanning the globe, with audiences surpassing 1,000+. Top involvements include:
→ → The WPO Annual Conference 2022 in Montreal CA, "The Information Age: Digitized & Datafied"
→ → Bloom Tech Forum 2021, "Harnessing the Power of Data for a Better Tomorrow"
→ → The WPO Annual Conference 2021 in Nashville TN, “Data Science: A Double-Edged Sword"
→ → The World Knowledge Forum 2021, “The Information Age: Digitized & Datafied"
→ → The Global DIY-Summit 2021, "The Future of Retail: Data as a Differentiator"
→ → Africa Communications Week 2021, “The Shift toward Data-Driven Marketing & Communications” 
→ → DATAcated Conference 2021, “From zero to hero: a simple explanation of ML"
→ → DATAcated Conference 2020, “SQL isn't going anywhere"
→ → Quantic EMBA Conference 2020, “The Truth behind the Buzz- Emerging Data Technologies for the 21st Century"
→ → The Smart Future World Expo 2019 in Istanbul Turkey, "Harnessing the Power of Big Data"
→ → TechIE 2019 in Madrid Spain, "Big Data & ML Intro Workshop"
→ →  IE-UCOM Big Data Day 2018 in Yerevan Armenia, "Harnessing the Power of Big Data"

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For further opportunities (podcasts, events, etc.) in my interested fields, feel free to contact me directly.

The opinions stated here are my own, not those of my employers.

*Note that I may earn a small commission for sales through my affiliate links shared here.

Christina Stathopoulos, MSc

Greg Coquillo

Amazon Web Services (AWS)

As a member of the Data Center Systems & Automation (DCSA) Critical Operations Leadership team, I lead a cross-functional groups comprised of 100+ Network Engineers, Software Engineers, Technical Infrastructure Program Managers, and Infrastructure Delivery to build robust distributed systems (using Native AWS services) that accelerate network scaling capacity for AWS customers to support global AI/ML workloads.

Product Management Technical - Data Center Automation

Amazon

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.

While at Amazon Retail, I led cross-functional teams of Product, Engineering Program, and Risk to innovate on behalf of customers, while ensuring product compliance and safety. The collective effort reduced time-to-market by xx% and operational costs by $xxM annually.

Deliverables include: 
-A Transformer-based document orchestrator and processor handling xxxM data points per month.

- An automated purchase order gate that increased Gross Merchandise Sales by $xxxM.

 -A successful migration of xx petabytes of data to a new, secure, and private data lake with Redshift-enabled architecture to ensure our business complies with global regulations.

Product Management, Gates & Media Automation

CharlieDAO

CharlieDAO is a collective of analysts, product folks, crypto natives, and web developers making Web 3.0 tools and systems. Areas of Focus: NFT Analytics & DeFi primitives. 

We're growing on Discord, please DM if you would like to learn more and possibly join! All we expect is a willingness to learn & participate 2-5 hours / month. Sharing articles, meeting others, and supporting projects as you see fit.

Advisor - Product Management & Strategy

Lonza

Harvesting the power of information through global data analysis, professional networking, cross-team collaborations and strategic business decisions to maximize profits for the $1.2B Microbial Control Solutions Portfolio.

Lead Product Manager, Global Pricing Optimization

Maximize Profit (EBITDA) for a global portfolio of products achieving $350 Million in annual sales.
•	Improve MRP system via SAP, BW and IBP for global sites’ Raw Materials and Finished Goods to enable automated replenishment, production planning and service delivery. 
•	Implement and lead Value Chain and S&OP meetings globally and set KPIs for all sites.  
•	Manage product’s life cycles through proper segmentation and value category.
•	Analyze value chain and submit Capex for Key Projects to increase market share and improve operational efficiency
•	Post-Merger integration and portfolio optimization between Lonza and Diacon.  Reduce portfolio by 25% by eliminating custom blends and re-branding under Lonza umbrella. 
•	Partner with Sourcing, Supply Chain, Operation, Marketing, Sales and IT globally to bring value to the organization by organic market share gain and operational excellence.

Lead Product Manager, Global Value Chain

Avery Dennison

Lead a team of 60 employees and drove profitability for an $80 Million business.
- P&L Management and Owner of key KPI's including Safety, Quality, Productivity, Cost and Delivery for the Finishing Department.
-Lead deployment of continuous improvement activities centered on data-driven root cause analysis, Corrective Actions and Counter Measures. 
-Managed the department's R&O (Risks and Opportunities), while aligning with Divisional S&OP and reducing Labor Hour per BSI by 30 % in 6 months.

Value Stream Manager

Supporting key plant metrics to contribute to the organization’s OGSM:
			Safety - Zero Recordable (Zero is Achievable Program)
			Quality - CPMO (Complaints per Million Opportunities) target of 1.37
			Delivery - Stock Flex target 97.3%, Custom Coat target 97%
			Productivity - Coater UEE (Uptime and Equipment Effectiveness 70%)
			Losses (% of TVP) - Target of 2.22%
•	Manage the Safety Participation Program to achieve 100% completion rate
•	Support the plant’s ISO 9001 standard through the management of the Quality Inspection System (Technical Analysis, Certificate of Analysis, Process Control Standards, etc…)
•	Manage the Corrective and Prevention Action Program for customer satisfaction
•	Establish rapport with vendors and partner with them during Continuous Improvement projects
•	Support customers claims with short response time while providing root cause analysis and corrective action
•	File Vendor Claims exceeding $400,000 per year to reduce Losses
•	Lead the Coater Wrinkle Project to reduce wrinkles claims by 25%
•	Lead the Finishing Machine Cleaning Standard Project to reduce contamination claim by 50%
•	Perform data analysis of adhesive coat weight profiles using an ROQ Roll Hardness Tester to predict  die change (Total Preventive Maintenance) and avoid High/Low Coat Weight claims

Quality and Safety Process Engineer

Global Operations Leadership Development Associate
Worked with plant leaders and cross-functional team members of Finishing, Coating, Shipping and Supply Chain to achieve a stock delivery flexibility of 97.6% and a custom coat shipped flexibility of 98% while leading a team of 28 employees in Finishing and driving operational excellence through safety, quality, delivery and cost. 
•	Zero safety injuries in 2014 through daily improvements and communications using our “Zero is Achievable” approach. 
•	Reduced transactional quality complaints by 30% with the B-Shift Finishing team.
•	Increased productivity by 15% through SMED and flexible staffing.
•	Increased training completion rates and overall skills of B-shift Finishing (Operators, Assistants).
•	Consolidated small rolls and reduced by 25% in the facility and increased machine up time by 10%

Finishing Manager

Global Operations Leadership Development Associate
Collaborated with plant leaders including the materials manager, the master production scheduler and coating manager to improve and maintain a service level of 99.3%, a custom coat shipped flexibility of more than 90%, and a stock delivered flexibility of 94%, while saving at least $400,000 in operational costs across LPM plants and promoting safety through the “Zero is Achievable” campaign.

Supply Chain Planner

Publix Super Markets

Teamed up with manufacturing leaders like plant managers, production and quality control managers, plant engineers and production line associates to execute continuous improvement projects that included waste reduction, quality assurance, safety training, transfer of knowledge and leadership development, expecting to save upwards of $3,000,000 for 2 Bakeries, 2 Fresh Foods, 1 Deli plants by the 4th quarter of 2011.

Industrial Process Engineer

AI Product @Amazon | Startup Investor | 2X Linkedin Top Voice - 2021 Tech & Innovation, 2020 Data Science & AI | Quantum Computing & Web 3.0 | I use technology to scale and improve compliance processes | Content Creator

Book a session with at Topmate: https://topmate.io/greg_coquillo

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

🗺️ Here’s a quick mindmap to learn Agentic AI

Agentic AI is so far the biggest shift from traditional, static AI models to autonomous, goal-driven systems capable of reasoning, planning, and acting on their own. 

Instead of just generating text or images, these agents can now make decisions, interact with tools, and even collaborate with other agents. And they do so all while learning continuously.

If you’ve ever wondered how ChatGPT plugins, AI copilots, or workflow automation tools “think,” that’s Agentic AI in action. Learning it means understanding how to design agents that plan, execute, and optimize tasks dynamically, bringing human-like adaptability into automation.

1. Agentic AI Fundamentals
Start with the basics, which is “what Agentic AI is”, how it differs from traditional AI, and the types of agents (reactive, proactive, goal-driven). Learn how agents communicate with humans or other agents to complete complex workflows.

2. Querying and Interaction
Master prompt engineering for agents, context management, and natural language interfaces. Learn how agents handle multi-turn conversations and execute actions using APIs or databases.

3. Core Components & Architectures
Understand the building blocks of Agentic AI which include frameworks (LangChain, AutoGen, CrewAI), reasoning methods like Chain-of-Thought and Tree-of-Thought, and memory systems that retain and use information efficiently.

4. Optimization and Scaling
Explore how agents coordinate in multi-agent systems (agent swarms), decompose complex tasks, and optimize workflows through feedback loops and continuous learning.

5. Tools and Ecosystem
Get hands-on with popular frameworks like LangChain, AutoGen, Haystack, and CrewAI, and connect them to LLMs such as GPT-4 or Gemini. Learn how to use APIs, plugins, and orchestration tools like Zapier or Make for real-world deployment.

6. Security, Safety, and Reliability
Building responsibly involves implementing role-based permissions, policy enforcement, human-in-the-loop supervision, and strategies for error recovery and reliability in agent systems.

7. Use Cases and Applications
Apply what you’ve learned to real-world solutions: customer support agents, workflow automation assistants, data analysis copilots, or team collaboration bots.

🔹Here’s a tip for you: Start small by building a single-task agent, then expand into multi-agent collaboration. Continuous iterations over time will improve your agentic system building skills.

#agenticsystems

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

Check out this 8-Layer Architecture for LLM Systems

Large Language Models (LLMs) are more than just massive neural networks, they’re complex multi-layered systems built for performance, reliability, and scalability.

Each layer plays a unique role; from managing raw data and embeddings to deployment and safety. Together, they form the backbone of how modern AI operates in real-world environments.

1. Infrastructure Layer
The foundation of LLMs, handling compute power, networking, and storage across CPUs, GPUs, or TPUs.

2. Data Processing Layer
Focuses on data ingestion, cleaning, tokenization, and sampling, which turns raw data into training-ready datasets.

3. Embedding & Representation Layer
Transforms words into numerical embeddings for semantic understanding using techniques like positional encoding and PCA.

4. Model Architecture Layer
Defines the core neural network structure which includes attention heads, normalization, and architecture design for token prediction.

5. Training & Optimization Layer
Handles pretraining, fine-tuning, and distributed optimization for model performance and scalability across datasets.

6. Alignment & Safety Layer
Ensures models align with human values and ethics through reinforcement learning, feedback loops, and safety policies.

7. Evaluation & Serving Layer
Manages testing, inference, and model evaluation pipelines, ensuring reliability and real-world performance consistency.

8. Deployment & Integration Layer
Covers API deployment, SDKs, monitoring, and analytics, bringing the model into production environments.

To summarize, each layer in the LLM architecture contributes to a balanced system that enables real-world integration. However, this doesn’t come without challenges.


#LLM

If you look closely at this stack across providers, you’ll notice that AI is just part of the puzzle. I’m not exaggerating when I say, when launching production-grade systems, 80% of the AI challenges continue to be engineering challenges.
 
Selecting which model to work with isn’t even close to being the whole story. To successfully deploy and scale intelligent systems, one needs to understand how to make tradeoffs while evaluating hundreds of services offered by cloud providers like AWS, Google Cloud, and Microsoft Azure 

Each cloud has its edge; AWS leads in scalability, Google in data innovation, and Microsoft in enterprise integration. Let’s see how they compare across every key layer of the stack :

1.🔸Security & Governance
- AWS ensures secure access and monitoring with IAM and GuardDuty.
- Google focuses on unified security through Command Center and KMS.
- Microsoft leads enterprise defense with Azure Defender and Sentinel.

2.🔸Integration & Automation
- AWS automates workflows with Step Functions and Glue.
- Google connects systems using Dataflow and Workflows.
- Microsoft streamlines operations through Logic Apps and Data Factory.

3.🔸Compute & Infrastructure
- AWS delivers scalable compute with EC2, Lambda, and Inferentia chips.
- Google uses TPUs and GKE for AI scalability.
- Microsoft powers hybrid workloads with Azure VMs and Functions.

4.🔸Data & Analytics
- AWS supports data analysis through Redshift and Athena.
- Google dominates big data with BigQuery and Looker.
- Microsoft combines analytics and visualization via Synapse and Power BI.

5.🔸Edge & Hybrid
- AWS offers low-latency AI with Outposts and Wavelength.
- Google secures edge processing with GDC and Confidential Computing.
- Microsoft extends cloud capabilities using Azure Arc and Stack Edge.

6.🔸Cloud AI Services
- AWS offers SageMaker, Comprehend, and Rekognition APIs.
- Google provides Vertex AI and Gemini for advanced AI solutions.
- Microsoft integrates OpenAI, Cognitive Services, and ML Studio.

7.🔸Agent & Developer Tools
- AWS includes Bedrock Agents and CodeWhisperer.
- Google enables Gemini and LangChain integrations.
- Microsoft supports Copilot Studio and Semantic Kernel.

8.🔸Prototyping & Design Tools
- AWS empowers testing with SageMaker Studio Lab.
- Google simplifies development using AI Studio and Opal.
- Microsoft focuses on no-code creation via Designer and Recognizer Studio.

9.🔸Core Models
- AWS relies on Titan and Bedrock models.
- Google leads with Gemini.
- Microsoft uses Phi, Orca, and Azure OpenAI.

Understand how to set up your architecture for scalability, performance, cost, and reliability is a huge advantage, whether via single-cloud, multi-cloud, hybrid, or on-prem. 

Curious to know how you evaluate tradeoffs from services across these providers to set up your AI systems.

Ever tried to connect the dots between AI, ML, DL, and LLMs and felt like you were swimming in a sea of acronyms?

AI Engineers who consistently build AI products have learned that understanding the full AI ecosystem is just as critical as knowing the latest model release. One cannot successfully build intelligent systems without a fundamental understanding of the layers that make them work, from data and models to infrastructure and agents.

This All in One AI Cheat Sheet (2026 Edition) is a compact technical map that shows how everything fits together across the modern AI stack.

Here is what it covers:

1️⃣AI vs ML vs DL
Learn how Artificial Intelligence, Machine Learning, and Deep Learning differ and where they converge.

2️⃣Core Concepts
Grasp the fundamentals of neural networks, supervised and unsupervised learning, and reinforcement learning.

3️⃣Model Types
Explore regression, classification, clustering, generative models, and transformers that enable context aware reasoning.

4️⃣AI Tools and Frameworks
Work with TensorFlow, PyTorch, Scikit learn, LangChain, AutoGen, and CrewAI to build and deploy intelligent systems.

5️⃣Popular Algorithms
Understand the mathematical foundations behind Linear Regression, Random Forests, SVM, and Gradient Boosting.

6️⃣AI Infrastructure
Discover how FAISS and Pinecone enable vector storage, while MLflow and Weights and Biases support experiment tracking and monitoring.

7️⃣Agentic AI
My favorite evolution in the AI landscape. These are systems that plan, reason, and act autonomously using protocols such as MCP (Model Context Protocol) and A2A (Agent to Agent Communication). This is where LLMs evolve into agents that can collaborate and learn continuously.

8️⃣LLM Techniques and Prompting
Master methods like Chain of Thought, ReAct, RAG, fine tuning, and tool use to enhance reasoning and adaptability.

9️⃣Evaluation Metrics
Track performance through accuracy, RMSE, BLEU, Perplexity, and autonomy efficiency to measure true model and agent impact.

🔟Real World Use Cases
See AI in action through predictive analytics, voice generation, robotics, and decision making in dynamic environments.

Would love feedback on anything to be added or removed from this list. Dont forget to share your experience with the community at large. 

#AI

Who’s leading the charge among the Top 15 AI Companies?

We’ve witnessed how AI has gradually become the backbone of value in tech. Check out how these top 15 major players structure their moats to deliver value in this space:

🔸NVIDIA ($4.33T) anchors the stack since its GPU and compute engines power nearly everything.

🔸Microsoft ($3.80T) and Apple ($3.79T) are turning AI into product abstractions at scale.

🔸Google / Alphabet ($2.98T) remains a core innovator via models, search, and foundation services.

🔸Meta ($1.86T) and Tesla ($1.46T) push AI into social systems, robotics, perception, and autonomy.

🔸Players like CoreWeave, Palantir, Adobe, and Tempus AI illustrate how infrastructure, domain specialization, and platform depth are increasingly prized.

Some Insights Worth Noting

🔹Infrastructure is shifting from commodity to strategic. The recent $14B Meta-CoreWeave deal underlines how long term compute supply contracts can alter competitive balance.  

🔹Among AI firms, multiples remain aggressive: the median revenue multiple is approx 25-30×.  

🔹Efficiency metrics matter: some “AI supernovas” deliver over $1.1M ARR per engineer, far above conventional SaaS norms.  

🔹The frontier is vertical AI: models fine-tuned for health, law, finance, biotech will win share over general models when domain constraints and compliance weigh heavily.

What does real moat look like? It’s a combination of compute + model IP + data + application + feedback loops.

Which company would you add to the list and which one would you remove? And why?

graphical user interface, application, Teams

We’re often fascinated when observing AI Agents act autonomously, being capable of planning, reasoning, and collaborating like humans. But how many of us are aware of what’s under the hood to make all of this possible?

Behind every powerful AI agent lies a multi-layered protocol stack, just like the internet has TCP/IP or cloud has architecture layers.

The below stack defines how agents think, act, and interact, enabling structured intelligence across reasoning, communication, execution, and learning.

Think of it as the “Operating System of Agentic AI”, where each layer plays a role in making decisions, retrieving knowledge, executing tasks, and adapting from feedback.

Here’s the breakdown:

🔸Foundation Layer (L1):
The core data and compute layer, powered by LLMs (GPT, Claude), embeddings, datasets, and GPUs/TPUs.

🔸Execution Layer (L2):
Runs the agent’s actions in real or simulated environments via runtimes like LangGraph and automations on Make.com or n8n.

🔸Infrastructure Layer (L3):
Ensures secure communication through context tokens, message passing, and protocols like A2A and A2E.

🔸Agent Layer (L4):
Focuses on autonomy and intelligence. Includes memory systems (LangChain Memory), tool usage (MCP, APIs), and feedback loops (RLHF).

🔸App Layer (L5–L7):
Handles interaction and coordination, which includes reasoning, planning, and knowledge access using tools like ReAct, ReWOO, and RAG.

In this complex space, building modular intelligence is a good strategy. As an example, you may not need a persistent memory for your agents at first launch, based on well thoughtout tradeoffs. Later on, you should be able to evolve your system by adding long-term memory. Just be careful about how this addition will affect user experience as well as agent performance. 


#AIAgents

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The Battle

Christina Stathopoulos, MSc

Greg Coquillo

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