Human agency relocates from interface affordances to conversational processes of goal articulation, output evaluation, and outcome negotiation in AI interactions.
AI Agents and Agentic Systems: A Multi-Expert Analysis
2 Pith papers cite this work, alongside 79 external citations. Polarity classification is still indexing.
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A large-scale empirical study categorizes bugs in LLM agents and demonstrates that a specialized LLM agent can annotate them accurately at very low cost.
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After the Interface: Relocating Human Agency in the Age of Conversational AI
Human agency relocates from interface affordances to conversational processes of goal articulation, output evaluation, and outcome negotiation in AI interactions.
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When Agents Fail: A Comprehensive Study of Bugs in LLM Agents with Automated Labeling
A large-scale empirical study categorizes bugs in LLM agents and demonstrates that a specialized LLM agent can annotate them accurately at very low cost.