Kenoma Labs builds autonomous reasoning systems that learn to act in high-dimensional, adversarial environments — where the state space is vast, the signal is sparse, and the cost of being wrong compounds.
Intelligent action in complex environments is not prediction — it is navigation. Every decision point opens a tree of possible futures, most of which lead to ruin. The work is in learning which paths close, and which paths open.
We build agentic systems that do this work. Systems that learn policies through interaction with high-fidelity simulations, decompose hard problems into tractable subgoals, and improve through the structure of their own failures. Not models that approximate answers — agents that learn to act under uncertainty.
Our conviction is simple: the next generation of autonomous systems will be shaped by agents that can explore state spaces faster, deeper, and more systematically than any human — while remaining grounded in the physics of the environments they operate in.
Autonomous systems that learn optimal policies through interaction with high-fidelity simulation environments, scaling from single-agent optimization to multi-agent coordination.
High-performance simulation engines that replay and extend real-world data at scale — enabling agents to train on millions of sequential decision points per session.
Architectures for agents that operate in adversarial, partially observable environments where latency, sequencing, and risk management define the edge.
Frameworks for agents that generate their own tools, evaluate their own performance, and systematically close their own capability gaps.
The kenoma is the void — the space of deficiency, where what should exist does not yet.
This is the landscape of every unsolved problem. Every open proof obligation is a point in the kenoma. Every successful derivation is a small act of filling in.
We named ourselves after the territory we explore.