Towards large language model-based personal agents in the enterprise: Current trends and open problems

Vinod Muthusamy, Yara Rizk, Kiran Kate, Praveen Venkateswaran, Vatche Isahagian, Ashu Gulati, Parijat Dube

Abstract
There is an emerging trend to use large language models (LLMs) to reason about complex goals and orchestrate a set of pluggable tools or APIs to accomplish a goal. This functionality could, among other use cases, be used to build personal assistants for knowledge workers. While there are impressive demos of LLMs being used as autonomous agents or for tool composition, these solutions are not ready mission-critical enterprise settings. For example, they are brittle to input changes, and can produce inconsistent results for the same inputs. These use cases have many open problems in an exciting area of NLP research, such as trust and explainability, consistency and reproducibility, adherence to guardrails and policies, best practices for composable tool design, and the need for new metrics and benchmarks. This vision paper illustrates some examples of LLM-based autonomous agents that reason and compose tools, highlights cases where they fail, surveys some of the recent efforts in this space, and lays out the research challenges to make these solutions viable for enterprises.
Anthology ID:
2023.findings-emnlp.461
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6909–6921
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.461
DOI:
10.18653/v1/2023.findings-emnlp.461
Bibkey:
Cite (ACL):
Vinod Muthusamy, Yara Rizk, Kiran Kate, Praveen Venkateswaran, Vatche Isahagian, Ashu Gulati, and Parijat Dube. 2023. Towards large language model-based personal agents in the enterprise: Current trends and open problems. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6909–6921, Singapore. Association for Computational Linguistics.
Cite (Informal):
Towards large language model-based personal agents in the enterprise: Current trends and open problems (Muthusamy et al., Findings 2023)
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