System Design

The Engineering Challenge Building a modern logistics platform (like food delivery, ride-hailing, or fleet management) requires computing distances and Estimated Times of Arrival (ETA) at an immense scale. The $N^2$ Problem: If you have 1,000 drivers and 1,000 orders, calculating the distance between every possible combination requires 1,000,000 individual route calculations. Speed: These calculations must happen in real-time (under 50ms) to ensure seamless user experiences and prevent dispatching algorithms from timing out. Accuracy: The system must account for real-world constraints such as one-way streets, “no left turn” rules, and dynamic traffic congestion. Standard point-to-point APIs (like basic Google Maps API calls) are too slow and too expensive for massive Distance Matrix generation. You need an internal, highly optimized Routing Engine. ...

← Series hub Next → For the past two years, enterprise AI adoption has been dominated by a singular architectural pattern: API integration with massive, closed-source models (Frontier LLMs). While this API-Centric model allows for rapid prototyping, it becomes a severe liability when scaled to production workloads handling sensitive company data. The Problem with API-Centric Architectures Relying exclusively on commercial APIs (such as GPT-4 or Claude 3.5 Sonnet) introduces three critical bottlenecks for scale-ups and enterprises: ...

In less than two years since its launch, the Model Context Protocol (MCP) has transformed from an internal Anthropic initiative into an open industry standard. Now managed by the Agentic AI Foundation under the Linux Foundation, MCP is redefining how we design software systems. If TCP/IP connects computers, and REST connects microservices, then the MCP protocol was born to directly connect LLMs (Large Language Models) and AI Agents with real-world data and tools. ...

While using an AI to write code or answer support tickets is becoming commonplace, the true transformation in enterprise software lies in Agentic Systems. We are moving away from monolithic, single-prompt architectures toward distributed networks of AI Agents that can plan, coordinate, and execute complex workflows autonomously. The Limitation of the “Single Agent” Paradigm Many organizations begin their AI journey by building a “monolithic agent”—stuffing an entire knowledge base and every possible tool into a single LLM’s context window. As the system scales, this approach inevitably collapses: ...

The software industry is witnessing a historic transfer of power. Power is gradually leaving the hands of those who “only know how to type code” to those who “know how to solve problems using systems and AI.” Context: When “Writing Code” is No Longer an Exclusive Skill For over two decades, the value of a programmer was largely measured by their understanding of language syntax, mastery of frameworks (React, Angular, Spring Boot, etc.), and ability to memorize APIs. ...

Prerequisite: To understand the context and why we need Multi-Agent systems instead of traditional Microservices, please refer to Comprehensive AI-Native System Architecture. When first approaching GenAI, most developers start by stuffing a massive prompt into a single LLM, hoping it completes the entire task. However, as the system scales, this “Single Monolithic Agent” approach reveals fatal flaws regarding performance, cost, and risk control. That is when we need a Multi-Agent System. ...

For years, the image of a talented programmer was often associated with blazing fast typing speeds, the ability to memorize dozens of API libraries, and writing code without a single syntax error. We called them pure “Coders”. But as AI enters the playing field, a harsh reality has emerged: Writing code is only the easiest part of building software. Who are “Code Typists”? “Code Typists” is not a derogatory term, but a way to describe a common working state. You are in this state if: ...

Upon realizing that typing speed has been defeated by AI (as discussed in Part 1), an invisible fear engulfs programmers: “So what will I do if AI does everything?” The answer lies in clearly defining the boundary: AI doesn’t do “everything”. AI only handles the technical muscle work, while humans retain the brains and responsibility. To optimize the software development process without losing control, we need to draw a red line between the “Machine’s Territory” and the “Human’s Territory”. ...

Social media and tech marketing campaigns constantly inject a concept into our heads: “10x Developer thanks to AI”. The image of a programmer sipping coffee, typing a few prompts, and finishing a week’s worth of work in one morning is incredibly appealing. But the truth in the trenches of real-world projects is much harsher. AI provides immense power, but it follows the law of conservation of energy: The time you save when “typing code” will be partially (or entirely) reclaimed during the reading and maintenance phases if you don’t know what you’re doing. ...

The traditional Software Development Life Cycle (SDLC) is often described as a factory assembly line. Business Analysts (BA) write requirements $\rightarrow$ Designers draw UI $\rightarrow$ Developers (Dev) write code $\rightarrow$ Quality Assurance (QA) finds bugs $\rightarrow$ DevOps pushes to the server. Everyone sits in their own “silo” and communicates via Jira tickets. But AI has swung a sledgehammer, smashing these walls. When a BA can ask AI to generate a runnable Proof of Concept, and a Developer can ask AI to write automated test scripts, the boundaries between roles become incredibly blurred. ...