The Auton Agentic AI Framework
Sheng Cao, Zhao Chang, Chang Li, Hannan Li, Liyao Fu, Ji Tang

TL;DR
The paper introduces the Auton Agentic AI Framework, a structured architecture for developing autonomous AI agents that separates specifications from execution, enabling portability, safety, and efficiency in agent systems.
Contribution
It formalizes an architecture with a clear separation between agent specifications and runtime, introduces safety and self-evolution mechanisms, and proposes runtime optimizations for autonomous agents.
Findings
Formalized agent execution as an augmented POMDP with latent reasoning.
Developed a hierarchical memory architecture inspired by biological systems.
Implemented runtime optimizations reducing latency in agent workflows.
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 (LLMs) 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…
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Taxonomy
TopicsMulti-Agent Systems and Negotiation · Reinforcement Learning in Robotics · Constraint Satisfaction and Optimization
