Technical Limitations of LLM-Based AI Agents and Their Links to Bias and Governance Challenges: A Narrative Review

Large language model (LLM)-based AI agents combine reasoning and planning, memory, external knowledge use, tool calling, and multi-agent collaboration to perform complex tasks. However, they face planning failures, context-maintenance limitations, collaboration instability, vulnerabilities arising from external interactions, bias, unclear attribution of responsibility, and difficulties in oversight and incident tracing. This narrative literature review examined the technological evolution and technical limitations of AI agents and developed a conceptual mapping relating these limitations to selected issues of bias and governance. Selected early theoretical studies were included; the main focus was post-2022 scholarly literature and official institutional and corporate materials through July 2026, with publication-status and evidence updates during revision in September 2026. Sources were categorized into concepts and trends, single- and multi-agent architectures, technical limitations, and socio-ethical issues; limitations were compared using a problem addressed–mitigation approach–residual limitation framework. The analysis suggests that autonomous actions performed on behalf of users, ambiguity in the scope of authority, multi-agent interactions, external knowledge use, observation of external environments, and tool calling may be linked to unclear attribution of responsibility, bias propagation and amplification, and difficulties in oversight and incident tracing. Potential mediators include multiple actors, miscoordination and conformity, inherited external-source biases, and new attack surfaces. This conceptual mapping provides a basis for future research on trustworthy AI-agent design, operation, and governance.