mlsanityData to 14 Oct 2026

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

arXiv 2308.05391 ↗First posted 10 Aug 2023cs.AIcited by 19

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

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.

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.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 8; 2025: 8; 2026: 1.

19 papers.

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  1. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  2. 2024ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agentscited by 133
  3. 2024Empowering Biomedical Discovery with AI Agentscited by 118
  4. 2024On-Device Language Models: A Comprehensive Reviewcited by 104
  5. 2024Large Language Models for UAVs: Current State and Pathways to the Futurecited by 33
  6. 2024Exploring Autonomous Agents through the Lens of Large Language Models: A Reviewcited by 31
  7. 2025Human Decision-making is Susceptible to AI-driven Manipulationcited by 19
  8. 2023Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agentscited by 14
  9. 2025Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crewscited by 12
  10. 2025Shapley-Coop: Credit Assignment for Emergent Cooperation in Self-Interested LLM Agentscited by 12
  11. 2024Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directionscited by 10
  12. 2024GreedLlama: Performance of Financial Value-Aligned Large Language Models in Moral Reasoningcited by 9
7 more
  1. 2025BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systemscited by 8
  2. 2026Quantifying Trust: Financial Risk Management for Trustworthy AI Agentscited by 8
  3. 2025E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processingcited by 5
  4. 2025GGBond: Growing Graph-Based AI-Agent Society for Socially-Aware Recommender Simulationcited by 3
  5. 2024Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipelinecited by 1
  6. 2025FactFlow: Automatic Fact Sheet Generation and Customization from Tabular Dataset via AI Chain Design & Implementationcited by 1
  7. 2025Are LLM Agents the New RPA? A Comparative Study with RPA Across Enterprise Workflowscited by 1

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  1. 2024Can We Trust AI Agents? A Case Study of an LLM-Based Multi-Agent System for Ethical AI
  2. 2025Agentic AI Software Engineers: Programming with Trust
  3. 2025Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents
  4. 2024Creating an LLM-based AI-agent: A high-level methodology towards enhancing LLMs with APIs
  5. 2026A Large-Scale Empirical Study of Quality Assurance Practices and Gaps in AI Agents
  6. 2025A Survey of AI Agent Protocols
  7. 2025TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems
  8. 2025A Survey on Trustworthy LLM Agents: Threats and Countermeasures