Towards Hybrid Automation by Bootstrapping Conversational Interfaces for IT Operation Tasks
Open accessAbstract
Process automation has evolved from end-to-end automation of repetitive process branches to hybrid automation where bots perform some activities and humans serve other activities. In the context of knowledge-intensive processes such as IT operations, implementing hybrid automation is a natural choice where robots can perform certain mundane functions, with humans taking over the decision of when and which IT systems need to act. Recently, ChatOps, which refers to conversation-driven collaboration for IT operations, has rapidly accelerated efficiency by providing a cross-organization and cross-domain platform to resolve and manage issues as soon as possible. Hence, providing a natural language interface to bots is a logical progression to enable collaboration between humans and bots. This work presents a no-code approach to provide a conversational interface that enables human workers to collaborate with bots executing automation scripts. The bots identify the intent of users' requests and automatically orchestrate one or more relevant automation tasks to serve the request. We further detail our process of mining the conversations between humans and bots to monitor performance and identify the scope for improvement in service quality.
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Contemporary orchestration mechanisms manage agent selection, context distribution, and interaction flow control in multi-agent systems, enabling effective cooperation through user input processing, contextual distribution, and optimal agent selection based on capability assessment.
“Contemporary orchestration mechanisms manage agent selection, context distribution, and interaction flow control in multi-agent systems, enabling effective cooperation through user input processing, contextual distribution, and optimal agent selection based on capability assessment [902, 53, 175].”
From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [53]
Orchestration mechanisms enable effective cooperation among human and non-human actors through user input processing, contextual distribution, and optimal agent selection based on capability assessment and response evaluation, while managing message flow, ensuring task progression, and addressing task deviations.
“Orchestration mechanisms constitute the critical coordination infrastructure for multi-agent systems, managing agent selection, context distribution, and interaction flow control [902], enabling effective cooperation among human and non-human actors through user input processing, contextual distribution, and optimal agent selection based on capability assessment and response evaluation [53], while”
From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [53]
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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 13 · see citing works
- Authors
- Bandlamudi, J., Mukherjee, K., Agarwal, P., Dechu, S., Huo, S., Isahagian, V., Muthusamy, V., Purushothaman, N., & Sindhgatta, R.
- Affiliations
- IBM (United States) (US)
- ORCID
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2023-06-26
- Volume
- 37
- Issue
- 13
- Pages
- 15654-15660
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W4382198212
- Cited by
- 3
- References
- 1
- Citations by year
- 2025: 1 · 2024: 2
- FWCI
- 1.30
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Robotic Process Automation Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- conversational interfaces, IT operations, human-robot collaboration, intent classification, no-code, process mining, service quality monitoring, automation scripts
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