Better evals for AI agents
Multi-step AI agents are hard to evaluate. We're building a way to measure whether agents make good decisions — not just whether each step is correct.
Research
Half of our work is for clients. The other half is research — long-horizon bets that make the client work better.
Active projects
Multi-step AI agents are hard to evaluate. We're building a way to measure whether agents make good decisions — not just whether each step is correct.
Generalising what we built for Project Lattice. The aim: systems where you can replay yesterday's state exactly, by design — not by accident.
Software you notice at a glance, not at a click. Small physical prototypes — a lamp, a wall display — that share one design language.
Most test suites cover the easy cases. We're generating adversarial test data from real production traces — open-sourcing the harness this quarter.
Future trajectories
Agents that review pull requests the way a senior engineer would — checking for correctness, not just style.
Instrument any codebase in under a minute. No YAML. No SDKs. Just understanding.
Models that stay coherent across very long documents and multi-day task sequences. The infrastructure problem no one has solved.