Agents That Build Agents: The ALITA-G Revolution
From Static Models to Self-Evolving Systems Large Language Models (LLMs) began as static entities — vast but inert collections of parameters. Over the last year, they’ve learned to act: wrapped in agentic shells with tools, memory, and feedback loops. But ALITA-G (Qiu et al., 2025) pushes further, imagining agents that don’t just act — they evolve. The paper proposes a framework for turning a general-purpose agent into a domain expert by automatically generating, abstracting, and reusing tools called Model Context Protocols (MCPs). This marks a shift from “agents that reason” to “agents that grow.” ...