Chief Roadblock Remover and Learning Enabler | Software Development Expert | Leadership & Career Coach | Speaker | Author | Building great products, building great teams!

🎯 I'm a result-driven 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗟𝗲𝗮𝗱𝗲𝗿, 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗘𝘅𝗽𝗲𝗿𝘁, 𝗖𝗼𝗮𝗰𝗵, 𝗧𝗿𝗮𝗶𝗻𝗲𝗿 𝗮𝗻𝗱 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁 with 20 years of professional experience in different domains, including the 𝗳𝗶𝗻𝗮𝗻𝗰𝗲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆, 𝗯𝗮𝗻𝗸𝗶𝗻𝗴, 𝗲𝗻𝗲𝗿𝗴𝘆 𝘀𝗲𝗰𝘁𝗼𝗿, 𝘁𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁𝗮𝘁𝗶𝗼𝗻, 𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝘁𝗲𝗹𝗰𝗼. 

🚀 My mission is to provide 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲 focused on architecture, the best agile practices, passionately 𝗯𝘂𝗶𝗹𝗱 𝗵𝗶𝗴𝗵-𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝘁𝗲𝗮𝗺𝘀 𝗮𝗻𝗱 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 to deliver innovative, 𝗵𝗶𝗴𝗵-𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀, working closely with customers and developing software products on different platforms.

🎓 As a 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿, I published 20+ papers and book chapters in international conferences and journals (having more than 440+ citations and h-index 11 on Google Scholar), obtaining 𝗣𝗵.𝗗. 𝗶𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲𝘀. Also, I have been involved as a 𝗿𝗲𝘃𝗶𝗲𝘄𝗲𝗿 𝗮𝗻𝗱 𝗽𝗿𝗼𝗴𝗿𝗮𝗺 𝗰𝗼𝗺𝗺𝗶𝘁𝘁𝗲𝗲 𝗺𝗲𝗺𝗯𝗲𝗿—member of ACM, the IEEE Computer Society, and GOAI Research group. 

🧑‍💼 I work as a professional 𝗛𝗶𝗴𝗵-𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗖𝗮𝗿𝗲𝗲𝗿 𝗰𝗼𝗮𝗰𝗵 for people in engineering leadership positions.

✍🏻 Additionally, I am a 𝗽𝗮𝘀𝘀𝗶𝗼𝗻𝗮𝘁𝗲 𝗮𝘂𝘁𝗵𝗼𝗿, who helps more than 400,000 engineers, managers, and architects to build great careers, products, and teams through various platforms and channels.

𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲:

📌 Leadership
📌 Team building
📌 Making things efficient
📌 Creating self-contained systems
📌 Be highly organized and make others too
📌 Focus on strategy/goals
📌 Make things happen
📌 Career Coaching
📌 Mentoring
📌 Line management
📌 Effective client communication
📌 Software Architecture
📌 DevOps mindset
📌 Cloud (Azure)
📌 .NET/C# (ASP.NET/WPF)
📌 SQL Server/NoSQL
📌 Agile methods (Scrum, Kanban, SAFe)
📌 Product/project management

📲 Feel free to contact me here via LinkedIn (DM)!

Dr Milan Milanović

Developers don't like legacy projects

But, in software, legacy means it makes money

'Greenfield' means 'no revenue' (yet)

Legacy code is messy because it has survived user contact

Greenfield is clean because nobody's tried to break it

The code you hate is funding your salary

The code you want to write isn't

Chief Roadblock Remover and Learning Enabler | Helping 400K+ engineers and leaders grow through better software, teams & careers | Author | Speaker | Leadership & Career Coach

The best engineers I know delete more code than they write

Junior engineers add features. Senior engineers remove complexity

Every line of code you write is a liability. It needs to be maintained. It can break. It adds cognitive load to anyone who reads it later

The best pull requests I've seen in the last year? Half of them deleted more than they added. Someone refactored three classes into one. Someone replaced 200 lines of custom logic with a library function. Someone removed an entire abstraction layer that wasn't pulling its weight

Deletion is a skill. You have to know what's safe to remove. You have to understand the system well enough to see what's redundant, over-engineered, or just wrong

Next time you open a file, ask: What can I remove?

The best code is the code you don't write

𝗗𝗼 𝗻𝗼𝘁 𝗹𝗲𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝘁𝗵𝗶𝗻𝗸 𝘆𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗯𝗲𝗹𝗼𝗻𝗴

As former President Obama said in the podcast, when you sit with people at fancy places (Harvard, US Senate, etc.) with people with fancy titles and talk to them, you will learn one thing...

They are nothing special; they are just a normal person like you.

They just have confidence that they belong to something.

Give that confidence to yourself and sit at that table.

And when you get a seat at the table, make room for someone else who has never had a seat.

I wish you a great week ahead 👋!

Video: Pivot Podcast.

#management #personaldevelopment #success #career #leadership

If you're a developer who ships code daily, remember these:

- First, make it work; nobody cares how elegant broken code is
- Then make it fast, slow code that works still frustrates users
- After that, make it pretty, so that your teammates read code more than they write it
- Add tests that actually catch bugs, not just boost coverage metrics
- Refactor when you finally understand what you're building (usually the second time through)

Perfect code on the first try is a myth. 

Fast iteration beats slow perfection every time

If you're a senior software engineer, here is an incomplete list of engineering practices you must know:

- Source control / GIT
- Tests (check Test Pyramid)
- Clean code
- Pair / Mob programming
- Design Patterns
- Refactoring
- TDD / BDD
- YAGNI, DRY, KISS, SOLID
- Automatic code analysis
- Code reviews
- Documenting

Anything else to add?

𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 𝘃𝘀 𝗟𝗼𝗮𝗱 𝗕𝗮𝗹𝗮𝗻𝗰𝗲𝗿 𝘃𝘀 𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗣𝗿𝗼𝘅𝘆

Regarding a system design, we often need clarification about the roles of a Load Balancer and an API Gateway. Most of our resources are about their implementation rather than real-life use cases.

𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 

It sits between a client and a group of backend services. It serves as a reverse proxy, accepting all application programming interface (API) calls, aggregating the services needed to fulfill them, and returning the appropriate results. 

User authentication, rate limits, and statistics are typical duties that API gateways handle on behalf of an API service system. Also, the API gateway can handle faults (circuit breakers), logging, and monitoring.

𝗟𝗼𝗮𝗱 𝗕𝗮𝗹𝗮𝗻𝗰𝗲𝗿 

It is a service that distributes incoming traffic across many servers or resources. Usually, we have two or more web servers on the backend, and it 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝘀 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘁𝗿𝗮𝗳𝗳𝗶𝗰 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘁𝗵𝗲𝗺. 

Its primary purpose is to use resources optimally. A more equal task allocation and increased capacity can enhance the system's responsiveness and reliability. 

There are three load balancers at a high level: hardware-based, cloud-based, and software-based.

𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗣𝗿𝗼𝘅𝘆 

A server sits in front of backend servers and forwards client requests to them. Reverse proxies are typically used to enhance security, performance, and reliability. 

A reverse proxy receives a request from a client, forwards it to another server, and returns the response to the client, giving the impression that the first proxy server handled the request. These proxies ensure that users don't access the origin server directly, giving the web server anonymity. 

They are usually used for Load balancing, where we need to handle incoming traffic and distribute it across multiple backend servers, or for caching.

So, the main thing that differs these two is that an 𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 𝗶𝘀 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗼𝗻 𝗿𝗼𝘂𝘁𝗶𝗻𝗴 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 to the appropriate service and it handles requests for APIs, while a 𝗟𝗼𝗮𝗱 𝗯𝗮𝗹𝗮𝗻𝗰𝗲𝗿 𝗶𝘀 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗼𝗻 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗻𝗴 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 𝗲𝘃𝗲𝗻𝗹𝘆 between a group of servers and handles requests that are sent to a single IP address, which works at protocol or socket level (TCP, HTTP).

Some 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 of API Gateways are Amazon API Gateway, Ocelot (.NET-based), Tyk, or Apache APISIX, while Load Balancers are Azure Load Balancer, HAProxy, or Seesaw.

An example of reverse proxy services is 𝗔𝗽𝗮𝗰𝗵𝗲 𝗣𝗿𝗼𝘅𝘆, 𝗡𝗴𝗶𝗻𝘅 𝗼𝗿 𝗜𝗜𝗦 with additional modules (Url Rewrite).

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

Check out this simple breakdown of the four core LLM architectures shaping most of today’s advanced systems.

Not all LLMs reason the same way. Building reliable AI systems, or choosing the right model for a workflow requires a clear mental model of the architectures that drive them.

Let’s review:

1.🔸Decoder Only (GPT, LLaMA, Grok)
Generates text by predicting one token at a time using previous context.

Best for generative workloads, conversational systems, and multi step reasoning.

2.🔸Encoder Only (BERT, RoBERTa)
Processes text with full bidirectional context to learn dense semantic representations.

Best for classification tasks, retrieval, and embedding generation.

3.🔸Encoder Decoder (T5, FLAN T5, BART)
Separates understanding from generation. The encoder builds meaning, the decoder transforms it.

Best for translation, summarization, and any structured text to text transformation.

4.🔸Mixture of Experts (Mixtral, Gemini 1.5)
Routes each input to a subset of specialized expert networks.

Best for scaling model capacity while controlling compute cost.

These architectures go beyond academic details to determine latency profiles, context handling, fine tuning strategies, and how models behave under real workloads. A clear grasp of them helps you design better pipelines, choose the right foundation models, and understand why two LLMs can produce very different outcomes on the same task.

Let me know your thoughts!

#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

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