Om Sharma

The more I build agentic systems at Razorpay, the more I understand that - at its core, it is an agentic loop with tool calls, integrations, and retrieval. The hard part is... actually making it run reliably, at scale, under real production load. And this is what makes system design even more important. Your AI system is still expected to scale. It will still need microservices, message queues, consistency guarantees, load balancing, work distribution, state management, rate limiting, throttling, fallbacks, service-to-service communication, QoS, etc. It is great that you are looking into AI and are interested. You should be. Everyone should be. But it is important not to skip system design and cs fundamentals. I know it seems overwhelming, but it is what it is. First principles are not going anywhere, and that is super essential for actually building applied AI systems and running them reliably at scale. If you are a backend engineer and are kind of skipping these things, pause and reflect once. It is always good to be great at system design, not because it will help you crack interviews (it will), but because it will make you meaningfully better at your job. Seeing it firsthand. Remember, you will not be shipping prototypes to production. The difference between prototype and production code is 15 components and 1000 commits.