MongoDB, Inc. Class A The Six Five Summit: AI Unleashed 2026

Great to be here, and I'm excited to talk about how enterprise data architecture is needing to change with these rapid advancements we see in AI. How the conversation has shifted from which models we have to use to the trusted context these models need in production.

Great question, Jason. If you look back, MongoDB started as a database developers loved because it made building apps fast and intuitive. What really shifted in the market is that software itself is changing. We're moving from this static, deterministic code to these autonomous AI agents that are perceiving, reasoning, and acting on the fly. When generative AI first exploded, a lot of teams reacted by pulling together the standalone vector databases onto their legacy stacks. Pretty quickly, that became an operational nightmare of data sync issues and latency. Organizations realized that in order to run AI in production, you cannot have your vector sitting in one place, your operational data in another place, and your security rules somewhere else. You need a unified platform. It's not just about running AI, it's about how software is being built.

Emergent Labs is a great example of why flexibility matters here. They are one of our customers. They tested Postgres first but decided on MongoDB Atlas because when agents are building applications, the data model is constantly evolving. See, with Atlas, the schema evolves right alongside your application rather than forcing you to run migrations every time something changes. That is why they have been able to power around 2 million agentic applications on MongoDB.

Yeah. I think of foundation models as these very brilliant reasoning engines, like the Einsteins of the engineering world. But without context, they know zero facts about your actual business. The way I define good context is pretty simple. Bring together real-time operational signals with historical records and explicit business rules into something that can be together consumed by the AI that it can trust. If you feed these models raw text without that operational context, it is just making educated guesses. But when you give it real context, it can actually make smart decisions. Another customer of ours, AT&T, is a great example of this. They are bringing together real-time network signals with historical outage data, so combine all of that together so their AI can make better decisions about where repair crews actually need to go. That has physical implications. That is real business impact.

In their case, 3.1 million unnecessary dispatches avoided, $12 million- Wow of downtime saved.

When an agent is dispatching physical crews or touching billing systems, bad context isn't just an inconvenience or minor hallucination. It's a massive financial impact.

Yeah, look, I like to say agentic applications, everybody's building them. But an agent is only as smart as the context it has access to.

Okay. An agent without memory is really just an expensive chatbot.

You are talking about general knowledge and whatnot. It cannot hold onto a thread, it cannot learn, and agents now need to handle complex multi-step processes. Without memory, it just cannot do that. When we talk about state and memory, we are talking about an agent's ability to maintain this continuity. Remembering what happened 3 steps ago, keeping track of what the user was trying to accomplish, and then remembering their user preferences over time. If you want an agent to handle a process that takes 3 days and requires 5 different approvals, it has to hold onto that state without dropping the ball. Doing that at scale, though, immense data problem. That is a core data problem.