Introducing Resolve AI Labs

At Resolve AI Labs, we're developing AI systems that can reason about production infrastructure the way the best engineers do, and over time, across more context than any team of engineers can hold in their heads. As a first step, this means domain-specific models for causal reasoning, our own verifier models for evaluating the quality of open-ended investigations, and orchestration frameworks that coordinate multiple agents across fragmented evidence in real time. In production, there's rarely a clean "right answer" to train against. Engineers disagree on the root cause. Postmortems get revised months later. The reward signal itself is an open research problem.

The data problem is just as hard. Production incidents are rare by definition. The ones most worth learning from happen the least often. Their telemetry is ephemeral: logs rotate, metrics retention windows close. The exact conditions of an incident from six weeks ago are, in most cases, gone. Furthermore, the data is sensitive, and it often can't leave a customer's environment. You can't brute-force this with scale. So we're building simulation and replay infrastructure that lets us train and evaluate durably, independent of which data still exists.

These aren't engineering problems you can solve with a better wrapper. They're open research problems. Our founding team comes from Meta Superintelligence Labs and Google DeepMind, alongside engineers with deep expertise in observability and infrastructure. We need both.

What we're building toward

The goal is a fundamentally different operating model for production software. An AI system that can investigate, reason, and act across the full production environment, continuously, without waiting for a human to step in.

That means agents are increasingly taking point to handle the operational load currently falling on engineers by default: the 3 am pages, the multi-hour investigations, the context switching that makes it hard to build anything new. The best engineers should be setting policy, handling true exceptions, and building forward, not keeping up.

We are moving from Human-in-the-Loop to Human-on-the-Loop, and Resolve AI Labs is how we get there.

Resolve AI Labs is hiring researchers and engineers working on complex reasoning, long-horizon multi-agent interactions, RL over massive environments, and ambiguous evaluations. If these problems interest you, we'd like to talk.