Klavis AI

Klavis AI offers live environments designed for the training of AI agents, focusing on coding and agentic tool-use tasks. The platform delivers both coding-oriented and tool-use datasets that emphasize long-horizon tasks, programmatic verification, and granular reward structures. It caters to scenarios where agents must perform complex activities such as code editing, test writing, debugging, and interacting with real-world tools and SaaS applications.

For coding agent data, Klavis AI provides a range of task types including short coding prompts and extended, long-horizon coding challenges. These tasks are supported by features such as manual review, programmatic verification, binary pass/fail rewards, and more nuanced, granular reward systems. The environments are offered in Dockerized formats to facilitate local setup and reproducibility, and they support iterative code, test, and debug workflows. This infrastructure allows for deterministic tests and is suitable for reinforcement learning (RL) and supervised fine-tuning (SFT) procedures.

On the agentic tool-use side, Klavis AI supplies data involving demo-only tool calls and long-horizon workflows across a selection of over 600 real tools and SaaS applications. These tasks are structured as static task worlds or state-mutating workflows, with rewards that may be subjective, rubric-based, or determined by language models. The platform’s tool-use data is designed to simulate realistic workflows with logically consistent state, noisy inputs, and verifiable outcomes, making it relevant for developing frontier agents intended to interact with real production environments.

Klavis AI is aimed at users such as AI labs and researchers who are building and evaluating advanced agentic systems. The platform highlights its ability to deliver high-quality, realistic data and environments for the development and assessment of AI agents on complex, real-world tasks.