We built Versanova after repeatedly hitting the same wall while deploying AI agents in production: agents don’t actually learn. Most systems rely on static prompts, brittle memory stores, or ad-hoc heuristics. They can retrieve context, but they can’t evolve from experience, share knowledge across tasks, or adapt the way a real employee does on the job. As systems grow more agentic, this becomes the bottleneck. Our goal was to build a learning layer that sits alongside LLMs and memory—one that continuously distills experiences into reusable knowledge, updates behavior over time, and works across real workflows, not just chat demos. During the build, our approach shifted heavily from theory to practice. Early prototypes were too abstract. Feedback from teams building real agentic products pushed us to focus on production constraints: observability, control, predictable updates, and easy integration into existing stacks. Today, that learning layer is live in production, with clear pricing and an API teams can use immediately. We’re excited to share it and learn from the community’s feedback.

Versanova

A learning layer for AI agents of tomorrow

A learning layer for AI agents of tomorrow