The MongoDB Podcast

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Modern AIOps: What It Takes to Build Reliable AI Products

Explore episode Jun 5, 2026
Technology
The Future of AIOps: From Surfacing Data to Intelligent Recommendations

Modern AIOps: What It Takes to Build Reliable AI Products · Jun 5, 2026 Technology

Right now, Langtrace surfaces all the data and leaves interpretation to the developer. The next frontier is the platform providing intelligent suggestions — telling you whether to fix your prompt, improve your context, or tune your retrieval — without the developer having to figure it out themselves. Langtrace is also building AI agents internally to stay close to the real problems.

1m 40s

Where this was said

The Future of AIOps and Langtrace's Roadmap

At 45:40 · chapter starts 42:40

The forward-looking section reveals both the intellectual humility and the ambition behind Langtrace's roadmap. Karthik acknowledges that even the core question of how to architect AI agents — single reasoning model vs. planner-executor split — has no settled answer. Open problems like tool calls that return massive JSON payloads (too large for any context window to handle reliably, even at 2 million tokens) remain unsolved. The direction Langtrace is pursuing is a shift from 'here is all the data' to 'here is what you should do about it' — intelligent, actionable suggestions that tell a developer whether to fix their prompt, improve context, or tune retrieval, without requiring them to diagnose the problem themselves. To stay grounded in real developer pain, the Langtrace team builds AI agents internally. One experiment became a product: Hey Zest, a Slack bot platform in closed beta that lets any team deploy AI agents — including one backed by MongoDB Atlas Vector Search for natural-language database queries — directly in their workspace. It emerged from the same dogfooding philosophy that created Langtrace.

Technology
Build with Model Primitives First, Then Pick a Framework

Modern AIOps: What It Takes to Build Reliable AI Products · Jun 5, 2026 Technology

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.

2 min

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