Multimodal Large Language Agents for Autonomous GUI Understanding and Computer Interaction
Keywords:
multimodal large language agents, graphical user interfaces, autonomous computer interaction, human-computer interaction, AI governance, system architecture, robustness, fairness, sustainabilityAbstract
Autonomous graphical user interface understanding and computer interaction represent a frontier in artificial intelligence systems research, where multimodal large language agents are expected to perceive screens, interpret interface semantics, plan multi-step tasks, and execute actions across heterogeneous software environments. This paper examines the system-level architecture, infrastructure requirements, governance challenges, and long-term sustainability of such agents. It argues that the central difficulty is not merely visual recognition or language generation, but the integration of perception, memory, planning, tool invocation, action grounding, and accountable execution within dynamic socio-technical infrastructures. The discussion distinguishes desktop, web, and mobile settings, emphasizing trade-offs among latency, privacy, robustness, accessibility, and auditability. It also considers how evaluation benchmarks shape engineering priorities, often privileging short-horizon task success while underrepresenting failure recovery, fairness, and human oversight. Governance concerns include permission boundaries, prompt injection, data leakage, unsafe automation, and the distribution of responsibility among users, vendors, and deployers. The paper concludes that trustworthy multimodal computer-use agents require layered architectures, sandboxed execution, transparent telemetry, continuous evaluation, and policy frameworks that treat automation as an infrastructural commitment rather than a single model capability. By connecting technical design with institutional accountability, the analysis offers a forward-looking agenda for deploying agents that can operate graphical environments without eroding human control, equity, or system resilience.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
