Building Conversational Artifacts to Enable Digital Assistant for APIs and RPAs


  • Jayachandu Bandlamudi IBM Research
  • Kushal Mukherjee IBM Research
  • Prerna Agarwal IBM Research
  • Ritwik Chaudhuri IBM Research
  • Rakesh Pimplikar IBM Research
  • Sampath Dechu IBM Research
  • Alex Straley IBM Software
  • Anbumunee Ponniah IBM Software
  • Renuka Sindhgatta IBM Research



Virtual Assistants , Agents, Automation, Natural Language , Track: Deployed Applications


In the realm of business automation, digital assistants/chatbots are emerging as the primary method for making automation software accessible to users in various business sectors. Access to automation primarily occurs through APIs and RPAs. To effectively convert APIs and RPAs into chatbots on a larger scale, it is crucial to establish an automated process for generating data and training models that can recognize user intentions, identify questions for conversational slot filling, and provide recommendations for subsequent actions. In this paper, we present a technique for enhancing and generating natural language conversational artifacts from API specifications using large language models (LLMs). The goal is to utilize LLMs in the "build" phase to assist humans in creating skills for digital assistants. As a result, the system doesn't need to rely on LLMs during conversations with business users, leading to efficient deployment. Experimental results highlight the effectiveness of our proposed approach. Our system is deployed in the IBM Watson Orchestrate product for general availability.




How to Cite

Bandlamudi, J., Mukherjee, K., Agarwal, P., Chaudhuri, R., Pimplikar, R., Dechu, S., Straley, A., Ponniah, A., & Sindhgatta, R. (2024). Building Conversational Artifacts to Enable Digital Assistant for APIs and RPAs. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 22725-22733.



IAAI Technical Track on Deployed Highly Innovative Applications of AI