Quote · The MongoDB Podcast
Modern AIOps: What It Takes to Build Reliable AI Products
Where this was said
Advice for AI Engineers: Primitives First, Process Always
At 47:15 · chapter starts 46:50
Jesse invites Karthik to distill lessons from building AI agents at Langtrace into actionable advice for engineering teams. Karthik's first piece of advice is architectural: get close to the bare metal. Frameworks are tempting, but the AI industry is too nascent for its abstractions to have settled. [1] — Karthik Kalyanamaran "Every experienced AI builder Karthik knows has followed the same arc: start with a framework, hit its limits, and strip back to native mode…" 46:50 He uses a crisp analogy — it's the HTML era of AI, not the React era — to argue that building directly with native model primitives (OpenAI's API, Anthropic's API, raw tool definitions) gives you the deepest understanding of what's actually happening and the most freedom to discover what works. His own arc mirrors his advice: started with frameworks, hit their limits, returned to primitives. His second piece of advice is operational: commit to the boring work. Evaluations, tracing, iterative testing — none of it is glamorous, and most developers resist it. But this discipline is the difference between an AI product that degrades silently in production and one that improves continuously. Jesse punctuates the point by asking the audience if anyone actually enjoys testing, then laughing off the silence.
Every experienced AI builder Karthik knows has followed the same arc: start with a framework, hit its limits, and strip back to native model primitives. The lesson is to start there. Building directly on OpenAI's or another provider's raw API gives you the deepest understanding of what's actually happening — and that understanding is what lets you design a real architecture.
Karthik argues the AI industry is so early-stage that reaching for frameworks is premature — developers should build directly with model primitives, just as early web developers worked in raw HTML before React existed.
AI coding agents like Cursor or Claude Sonnet can write 50-100 lines for a bug that needs one. If you can't read the code, you'll ship a mess. Vibe coding doesn't eliminate the need for programming knowledge — it changes the job from writing code to critically reviewing what AI produces.
