Large Language Model Agents for Autonomous Task Planning and Workflow Automation
Keywords:
large language model agents; autonomous task planning; workflow automation; human-in-the-loop systems; AI governance; system architecture; infrastructure sustainability; robustness; fairness; policy implicationsAbstract
Large language model agents are emerging as a distinctive class of socio-technical systems that combine generative language capabilities with planning, memory, tool use, and orchestration mechanisms. Unlike conventional language models that respond to isolated prompts, agents can decompose goals, select actions, invoke external services, and coordinate multi-step workflows with varying degrees of autonomy. This paper examines the system-level implications of deploying such agents for autonomous task planning and workflow automation. It argues that the central challenge is not merely model capability but the design of architectures that balance autonomy with accountability, flexibility with reliability, and efficiency with sustainability. The analysis begins with the architectural foundations of agentic systems, including reasoning loops, memory substrates, tool interfaces, and multi-agent coordination. It then considers planning under uncertainty, human-in-the-loop workflow integration, governance and fairness, deployment infrastructure, robustness, security, and policy implications. Across these domains, the paper emphasizes structural trade-offs: greater autonomy can increase throughput but complicate auditability; tighter human oversight can improve legitimacy but reduce responsiveness; centralized infrastructure can simplify governance but create concentrated failure and environmental costs. The conclusion outlines a research agenda for dependable, contestable, and sustainable agentic automation, arguing that progress depends on interdisciplinary work spanning artificial intelligence, systems engineering, law, organizational behavior, and public policy.
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