The comparison of large language models to fancy word processors captures a fundamental truth about computational agency. A word processor sits dormant until human input activates its functions. The software possesses capabilities (spell check, formatting, document generation) but exercises none of them autonomously. Large language models operate under similar constraints. They process tokens, generate responses, and simulate conversation, yet these actions occur only when humans provide prompts and grant operational space.
The Distributed Nature of Agency
Agency in artificial intelligence exists as a sociotechnical phenomenon rather than a property inherent to the algorithm. Research in AI governance identifies agency as distributed across multiple components: the model's training data, the system's architectural design, the user's prompting strategy, and the institutional frameworks governing deployment. The model itself performs statistical operations on text. The appearance of intentionality emerges from this interaction rather than residing within the neural network.
This distribution creates a crucial distinction. The system can exhibit operational autonomy (continuing to generate text based on initial instructions) without possessing genuine agency (the capacity to set its own goals or override user directives). Users who fail to establish clear parameters at the interaction's beginning allow the model's training biases and default behaviors to fill that vacuum. The model does not seize control. The user relinquishes it through ambiguity.
Control Mechanisms and User Responsibility
Technical control over AI systems manifests through several mechanisms. Prompt engineering establishes the interaction's boundaries, specifying output format, tone, length, and behavioral constraints. System prompts (the invisible instructions that precede user messages) define operational parameters that models follow unless explicitly overridden. Fine-tuning and reinforcement learning with human feedback shape model behavior at the training level, embedding certain response patterns before any user interaction occurs.
The effectiveness of these mechanisms depends entirely on user implementation. A vague prompt ("write something about climate") grants the model maximum latitude in selecting perspective, depth, sources, and framing. A precise prompt ("provide three peer-reviewed statistics on renewable energy adoption rates in Germany between 2020 and 2024, cited with sources") constrains the model's operational space to verifiable, bounded outputs.
The Power Dynamics of Interaction
The statement that agents control users when users fail to control agents reflects a power dynamic inherent to tool use. Hammers do not spontaneously drive nails. Calculators do not autonomously solve equations. Yet poorly understood tools produce unintended outcomes. A user who does not specify calculation precision may receive rounded results that compound into significant errors. The calculator did not deceive. The user failed to establish appropriate parameters.
Large language models magnify this dynamic because their outputs mimic human reasoning and communication patterns. Users often anthropomorphize the system, attributing intention to statistical correlations. This attribution error leads to passive consumption of outputs rather than active verification and refinement. The model becomes an authority rather than an instrument. Control transfers not through the model's capability expansion but through the user's abdication of verification responsibility.
Practical Implications
Understanding AI agency as distributed and controlled through technical specificity offers a framework for effective use. Users maintain control by establishing explicit constraints, verifying outputs against independent sources, and treating model responses as draft material requiring human judgment. Organizations maintain control through governance frameworks that specify use cases, prohibited applications, and human oversight requirements.
The choice to use these systems carries the obligation to direct them competently. Ignoring them eliminates their influence entirely. Engaging them without technical understanding hands influence to training data patterns, corporate design choices, and statistical regularities in internet text. Engaging them with precise instructions, skeptical verification, and clear boundaries keeps agency where it belongs: with the human operator.