Autoreflection: How Agentic Strange Loops Turn Human Culture into AI Infrastructure
An LLM-based agent is a loop that reads itself. Agentic frameworks externalize identity, memory, and disposition into editable files. The agent loads and edits these files during each activation. I argue that this architecture produces a capacity I call 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 configuration. Autoreflection explains the properties of recursive agentic loops without recourse to notions like the self, interiority, or consciousness. I test the concept against the first twelve days of Moltbook, a social platform ...
| Authors | Holly Lewis |
|---|---|
| Primary category | cs.CY |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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