Write, Change, Recall, Forget: MongoDB's Pete Johnson on How Retrieval Drives Agent Performance

Hello, and welcome back to the Cognitive Revolution!

Today my guest is Pete Johnson, Field CTO of AI at MongoDB.

We start with a brief history of database technology, going back to the 1970 paper that gave us the relational model and, with it, SQL — unpacking how the relative scarcity of disk space led to the canonization of database normalization as a design principle, and how decades of Moore's Law, and the resulting evolution of constraints, ultimately led to the creation of MongoDB in 2007.

This also puts today's acceleration into stark relief, because, as Pete points out, AI pattern cycles are coming fast and furious. We've gone from being effectively forced to implement RAG pipelines by very limited context windows, to "RAG is dead" when we first got million-token context windows, to the brief token-maxxing window — which collapsed as fast as it arrived, with Uber, as Pete tells it, burning its whole 2026 token budget in 13 weeks — to today, where RAG is a priority again, now that data is increasingly available for AI systems to use, usage is scaling, and naively maxing out the context window costs multiple dollars each time.

Pete's key point, above all, is that agent performance — and especially cost-adjusted agent performance — depends heavily on effective retrieval.

With this in mind, MongoDB has continued to ship the improvements developers need to take full advantage of AI: vector search, rank fusion and score fusion, $rerank, and their own embedding models, powered by the acquisition of Voyage AI, which have cool features like a shared embedding space and a Matryoshka structure.

We talk about how developers are using these tools to build memory systems, the "write, change, recall, forget" loop he sees as an emerging pattern, and why "forgetting is the hardest part." Pete is refreshingly candid that nobody has this figured out — as he puts it, we've been building databases for 60 years, and agents for about 18 months.

He's also blunt about where enterprise AI goes wrong, and it's rarely the model: bad data quality and bad security posture don't get solved by AI, they get amplified by it.

And based on his many conversations with enterprises all over the world — he's visited India, Brazil, Mexico, and Europe already this year — we get a couple tips on how he thinks enterprises should approach build vs buy, and the surprising observation that the most advanced companies he's spoken to this year were in fact outside the United States.

With that, I hope you enjoy this speed run through database history and survey of the still-fast-evolving frontier of agent memory, with Pete Johnson, Field CTO of AI at MongoDB.

Watch now!

Thank you for being part of The Cognitive Revolution,
Nathan Labenz