The training ground behind Manifest
An agent is only as good as the environments it trains in. We design reinforcement-learning environments with dense reward signals grounded in real business logic, and we've delivered them to frontier labs. That work ships through two surfaces: the Platform, where tasks are authored, evaluated, and quality-checked, and Build, a no-code way to compose environments from blocks.
Platform
The evaluation pipeline behind Manifest's agents: where we author benchmark tasks, run them against frontier models, and validate every trace before a capability is trusted with real operational work.
Track every task from draft to done.
Watch work move through the pipeline with per-model scores, run status, and spend in one view.
Build
Where Manifest's agents train: composing reinforcement-learning environments from blocks described in plain language, then building, running, and testing them against operational scenarios.
Compose an environment from blocks.
Lay out an environment as a chain of blocks described in plain language and wired to the next. No code required.
Run, test, and tune in place.
Send an agent through the environment you composed and watch it resolve, retry, and recover live. Then adjust the reward and run it again.