A new academic paper proposes a framework for understanding and building AI agents, distinguishing between "agentic" systems that rely on external scaffolding and "agentive" systems with internalized capabilities. The paper introduces the Goal-Identity-Configurator (GIC) architecture, which aims to enable true autonomy and endogenous capabilities for AI agents. The authors also discuss the implications for auditability, controllability, and safety of increasingly autonomous AI systems. AI
IMPACT Clarifies the distinction between engineered AI workflows and true AI autonomy, impacting future agent development and safety research.
RANK_REASON The cluster contains an academic paper discussing AI agent architectures and proposing a new model.
Read on arXiv cs.MA (Multiagent) →
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