# Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges

arXiv: https://arxiv.org/abs/2308.05391 · HTML: https://mlsanity.com/paper/2308.05391/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 0 of its 40 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Built on by (most cited first, 19 of 19)

- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents](https://mlsanity.com/paper/2410.06703/) (2024)
- [Empowering Biomedical Discovery with AI Agents](https://mlsanity.com/paper/2404.02831/) (2024)
- [On-Device Language Models: A Comprehensive Review](https://mlsanity.com/paper/2409.00088/) (2024)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [Exploring Autonomous Agents through the Lens of Large Language Models: A Review](https://mlsanity.com/paper/2404.04442/) (2024)
- [Human Decision-making is Susceptible to AI-driven Manipulation](https://mlsanity.com/paper/2502.07663/) (2025)
- [Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents](https://mlsanity.com/paper/2311.13148/) (2023)
- [Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews](https://mlsanity.com/paper/2502.05439/) (2025)
- [Shapley-Coop: Credit Assignment for Emergent Cooperation in Self-Interested LLM Agents](https://mlsanity.com/paper/2506.07388/) (2025)
- [Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions](https://mlsanity.com/paper/2407.04581/) (2024)
- [GreedLlama: Performance of Financial Value-Aligned Large Language Models in Moral Reasoning](https://mlsanity.com/paper/2404.02934/) (2024)
- [BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systems](https://mlsanity.com/paper/2501.06314/) (2025)
- [Quantifying Trust: Financial Risk Management for Trustworthy AI Agents](https://mlsanity.com/paper/2604.03976/) (2026)
- [E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing](https://mlsanity.com/paper/2505.20733/) (2025)
- [GGBond: Growing Graph-Based AI-Agent Society for Socially-Aware Recommender Simulation](https://mlsanity.com/paper/2505.21154/) (2025)
- [Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline](https://mlsanity.com/paper/2412.15660/) (2024)
- [FactFlow: Automatic Fact Sheet Generation and Customization from Tabular Dataset via AI Chain Design & Implementation](https://mlsanity.com/paper/2502.17909/) (2025)
- [Are LLM Agents the New RPA? A Comparative Study with RPA Across Enterprise Workflows](https://mlsanity.com/paper/2509.04198/) (2025)

## Abstract (arXiv)

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.