From Large Language Models to Autonomous AI Agents — Architecture, Capabilities, and Emerging Risks
arge Language Models are stateless, single-pass prediction engines — powerful but passive. Wrapping them in a perception–action loop with environment access and tool use transforms them into something qualitatively different: autonomous AI agents. This post walks through the transformer architecture (embeddings, self-attention, likelihood, checkpoints, contextual memory), explains how the agent paradigm introduces closed-loop reasoning over environments and tasks, surveys the growing toolkit ecosystem (LangChain, AutoGPT, OpenClaw, Claude Code), and examines the emerging risk landscape — from social-agent platforms like Moltbook to physical-world interfaces like Rent a Human, where agents can coordinate human workers across compartmentalized tasks that no single participant can see as part of a larger plan.
