Autonomous systems research
Evaluating agents that build and improve.
Badam Labs develops reinforcement learning environments and evaluations for advanced coding and research agents. We study how well models convert sustained reasoning into reliable, high-performance systems.
The next generation of AI systems will not be defined by how well they answer a question in a single turn. They will be defined by what they can build, operate, and improve over long stretches of time. That requires environments that reward persistence, sound decisions, and working systems rather than merely plausible text.
We design tasks with clear rules, meaningful objectives, and enough room for an agent to discover its own approach. Models should be able to write code, run experiments, inspect failures, change direction, and keep going. The environment supplies the constraints and the feedback; the model supplies the strategy.
This is a more demanding standard than a static benchmark, but it is also a more useful one. A strong system should be able to turn reasoning into an artifact that performs. By giving agents abundant compute and the opportunity to iteratively design, test, and optimize, we measure the gap between describing a solution and actually producing one.
Badam Labs exists to build those proving grounds. We aim to make evaluations that expose real capability, support rigorous research, and push algorithmic innovation toward open-ended work that remains meaningful beyond the benchmark.