The Auton Agentic AI Framework

--> Computer Science > Artificial Intelligence arXiv:2602.23720 [Submitted on 27 Feb 2026] Title: The Auton Agentic AI Framework Authors: Sheng Cao , Zhao Chang , Chang Li , Hannan Li , Liyao Fu , Ji Tang View a PDF of the paper titled The Auton Agentic AI Framework, by Sheng Cao and 5 other authors View PDF HTML Abstract: The field of Artificial Intelligence is undergoing a transition from Generative AI -- probabilistic generation of text and images -- to Agentic AI, in which autonomous systems execute actions within external environments on behalf of users. This transition exposes a fundamental architectural mismatch: Large Language Models produce stochastic, unstructured outputs, whereas the backend infrastructure they must control -- databases, APIs, cloud services -- requires deterministic, schema-conformant inputs. The present paper describes the Auton Agentic AI Framework, a principled architecture for standardizing the creation, execution, and governance of autonomous agent systems. The framework is organized around a strict separation between the Cognitive Blueprint, a declarative, language-agnostic specification of agent identity and capabilities, and the Runtime Engine, the platform-specific execution substrate that instantiates and runs the agent. This separation enables cross-language portability, formal auditability, and modular tool integration via the Model Context Protocol . The paper formalizes the agent execution model as an augmented Partially Observable Markov Decision Process with a latent reasoning space, introduces a hierarchical memory consolidation architecture inspired by biological episodic memory systems, defines a constraint manifold formalism for safety enforcement via policy projection rather than post-hoc filtering, presents a three-level self-evolution framework spanning in-context adaptation through reinforcement learning, and describes runtime optimizations -- including parallel graph execution, speculative inference, and dynamic ...