Research on systems that adapt.

We study how agents learn through use, retain what matters, and become more useful without losing human direction.

The questions guiding our work.

The research moves across six connected areas. Each one asks what an agent needs in order to work well over time, in a particular environment, for particular people.

  • [01/06]Adaptation

    How systems change through use.

    We study how an AI system can learn from experience without drifting away from the person it serves.

    Learning without drift
  • [02/06]Memory

    What should persist.

    We study what should survive across tasks and how past experience should shape future work.

    Across tasks
  • [03/06]Agency

    Work that continues over time.

    We study how agents pursue longer-running work across tools, interruptions, and changing conditions.

    Long-running work
  • [04/06]Evaluation

    Whether a system improved.

    We study how to distinguish useful improvement from a system that has merely changed.

    Evidence of progress
  • [05/06]Direction

    How people shape the system.

    We study how intent, taste, and judgment can guide a system without being reduced to a settings page.

    Human direction
  • [06/06]Software

    What comes after fixed workflows.

    We study what becomes possible when software is formed around a particular person or organization.

    Particular systems
How we work

The questions come from real work.

Rifty maintains a private agent system that works across tools, companies, and tasks that continue over time. It exposes problems that are easy to miss in a demonstration: incomplete information, changing standards, delayed consequences, and decisions without one correct answer.

Publications

The work will be published here.

Research articles, technical reports, and evaluations will appear here once the underlying work is ready to be examined.

Coming soon