REVIEW 6 cited by
Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 6 Pith papers
-
Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages
Users who first used a Spanish AI writing assistant subsequently used the English AI writing assistant less, suggesting a spillover that violates choice independence.
-
AgentX: Towards Orchestrating Robust Agentic Workflow Patterns with FaaS-hosted MCP Services
AgentX, a stage-planner-executor agentic workflow, matches ReAct and Magentic-One on output quality in three applications while cutting token use on web search, and MCP servers deployed on AWS Lambda run at negligible...
-
Are LLM Agents the New RPA? A Comparative Study with RPA Across Enterprise Workflows
In head-to-head tests on three standard automation challenges, RPA bots beat LLM computer-use agents on speed and reliability, while the agents needed far less development time.
-
Shapley-Coop: Credit Assignment for Emergent Cooperation in Self-Interested LLM Agents
Shapley-Coop asks LLM agents to negotiate prices for contributions based on Shapley value reasoning, improving cooperation and reward fairness in three multi-agent tasks.
-
GGBond: Growing Graph-Based AI-Agent Society for Socially-Aware Recommender Simulation
GGBond is an agent-based simulator that couples a five-layer cognitive agent model with a dynamic multilayer social graph to evaluate recommender systems under long-term feedback.
-
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews
A CrewAI-based multi-agent system with human oversight built financial models and carried out model risk management checks on three public credit datasets, with results comparable to AutoML and Kaggle baselines.
Discussion (0). Continue with ORCID to comment.