Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges
It addresses trust problems for users and developers of AI automation agents, but is incremental as it builds on prior work without introducing new methods or results.
The paper examines trust issues in LLM-based AI automation agents, analyzing existing literature and identifying new challenges and considerations specific to this emerging technology, while evaluating how current products address these aspects.
Trust in AI agents has been extensively studied in the literature, resulting in significant advancements in our understanding of this field. However, the rapid advancements in Large Language Models (LLMs) and the emergence of LLM-based AI agent frameworks pose new challenges and opportunities for further research. In the field of process automation, a new generation of AI-based agents has emerged, enabling the execution of complex tasks. At the same time, the process of building automation has become more accessible to business users via user-friendly no-code tools and training mechanisms. This paper explores these new challenges and opportunities, analyzes the main aspects of trust in AI agents discussed in existing literature, and identifies specific considerations and challenges relevant to this new generation of automation agents. We also evaluate how nascent products in this category address these considerations. Finally, we highlight several challenges that the research community should address in this evolving landscape.