Abstract
Systems for escalating interactions from automated agents to human agents can create inefficiencies, for example, by transferring unstructured transcripts. An intermediary system can employ a generative artificial intelligence synthesis engine to process the context of an automated interaction upon an escalation trigger. The engine may analyze the dialogue transcript, user metadata, and the automated agent's internal state to perform semantic abstraction, diagnose potential failure points, and infer a possible resolution. The system can then generate a structured briefing for the human agent, which could include a concise summary, a failure diagnosis, or a recommended next action presented as an interactive element. This process may facilitate a more efficient handoff and contribute to an improved escalation workflow by providing the human agent with synthesized, contextual information.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Raithatha, Deep and Kayande, Tanmay, "Generative AI Synthesis of Automated Interactions into a Structured Agent Briefing", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10338