Augento

Augento offers a platform for reinforcement learning (RL) fine-tuning of large language models (LLMs), enabling users to adapt foundational AI models to their specific use cases. Unlike traditional supervised fine-tuning, which requires curated prompt-response pairs, Augento’s RL approach learns directly from feedback using reward functions. This means you don’t need to collect perfect training data—just define what success looks like, and the model will optimize its behavior accordingly.

The platform is designed for seamless integration: simply connect your existing LLM, specify a reward function, and start RL training jobs. Augento automatically captures relevant training cases from your production data flow, identifies error cases, and manages the entire fine-tuning process. Once training is complete, your custom fine-tuned models are hosted on Augento’s infrastructure and can be accessed via a standard API, making deployment as easy as switching an API key.

Augento’s RL fine-tuning is particularly effective for complex tasks where prompt engineering falls short, such as coding agents, tool-using agents, browser automation, and vision-language models for robotics. For example, a coding agent fine-tuned with Augento saw a 40% reduction in critical bugs with just 20 training samples, thanks to a reward function that evaluated code against a compiler.

The solution is fully automated and scalable, catering to both startups and enterprises. With flexible pricing and no need for extensive data collection, Augento empowers developers to create more reliable, specialized, and efficient AI agents tailored to their unique requirements.