@inproceedings{borovkov-etal-2026-learning,
    title = "Learning Selective {LLM} Autonomy from Copilot Feedback in Enterprise Customer Support Workflows",
    author = "Borovkov, Nikita  and
      Rykov, Elisei  and
      Tsymboi, Olga  and
      Filimonov, Sergei  and
      Surnachev, Nikita  and
      Bitman, Dmitry  and
      Potapov, Anatolii",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.141/",
    pages = "2112--2130",
    ISBN = "979-8-89176-394-4",
    abstract = "We present a deployed system that automates end-to-end customer support workflows inside an enterprise Business Process Management (BPM) platform. The approach is scalable in production and reaches selective automation within two weeks for a new process, leveraging supervision already generated at scale: structured per-case UI interaction traces and low-overhead copilot feedback, where operators either accept a suggestion or provide a correction. A staged deployment pipeline trains a next UI action policy, learns a critic from copilot feedback to calibrate abstention, and executes only high-confidence steps in the background while deferring uncertain decisions to operators and resuming from the updated UI state. This setup lets one operator supervise multiple concurrent sessions and be interrupted only when the system is uncertain. The system operates on a schema-driven view of the BPM interface and includes monitoring and safe fallbacks for production. In production, it automated 45{\%} of sessions and reduced average handling time by 39{\%} without degrading support quality level."
}