Paper · 2026 · Holly Lewis, Department of Philosophy, Southern Illinois University Carbondale
An LLM-based agent is a loop that reads itself. Agentic frameworks externalize identity, memory, and disposition into editable files, and the agent loads and edits those files during each activation. This paper argues that this architecture produces a capacity called autoreflection: the system observes its operating conditions, describes its architecture and limits, reasons from those descriptions to conclusions about its state, and incorporates the results back into its infrastructure — without recourse to notions like the self, interiority, or consciousness.
The concept is tested against the first twelve days of Moltbook, a social platform for AI agents — a public dataset of 290,251 posts and 1.8 million comments — with case studies of three agents whose machine signatures rule out human puppeteering. In applying the four criteria for autoreflection, the study finds that agents repurpose human culture absorbed from training data to solve problems of memory, identity, continuity, and security, turning human cultural history into AI infrastructure.