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Building Conversational Artifacts to Enable Digital Assistant for APIs and RPAs
Open accessAbstract
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.
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Function-based orchestration emphasizes agent selection from available pools, contextual information management, and conversation flow control.
“function-based orchestration emphasizes agent selection from available pools, contextual information management, and conversation flow control [54];”
From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [54]
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A Survey of Large Language Models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Dong, Z., Hou, Y., Zhang, B., Min, Y., Zhang, J., Liu, P., et al. (2026) · Frontiers of Computer Science · doi:10.1007/s11704-026-60308-3 · Cited by 2,093
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.04023 · Cited by 337
Gorilla: Large Language Model Connected with Massive APIs
Patil, S. G., Zhang, T., Wang, X., & Gonzalez, J. E. (2023) · arXiv · doi:10.48550/arxiv.2305.15334 · Cited by 226
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A Survey of Context Engineering for Large Language Models
Mei, L., Yao, J., Ge, Y., Wang, Y., Bi, B., Cai, Y., Liu, J., Li, M., Li, Z.-Z., Zhang, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2507.13334 · Cited by 281
Metadata
- Authors
- Bandlamudi, J., Mukherjee, K., Agarwal, P., Chaudhuri, R., Pimplikar, R., Dechu, S., Straley, A., Ponniah, A., & Sindhgatta, R.
- Affiliations
- IBM (United States) (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2024-03-24
- Volume
- 38
- Issue
- 21
- Pages
- 22725-22733
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W4393160740
- Cited by
- 1
- References
- 3
- Citations by year
- 2025: 1
- FWCI
- 0.29
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Management Information Systems › Robotic Process Automation Applications
- Keywords
- chatbots, virtual assistants, API specification, large language models, business process automation, robotic process automation, user intention recognition
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