Toward self-coding information systems
🤖 AI Summary
Traditional information systems struggle to autonomously adapt their structure and behavior at runtime, resulting in prolonged functional iteration cycles and delayed responsiveness. This work proposes a novel paradigm—self-encoding information systems—and provides the first formal definition of this concept. By integrating agent-based AI, runtime code generation, automated testing, and deployment technologies, the proposed framework enables systems to autonomously assess requirements, generate code, verify correctness, and redeploy during execution. This research not only pioneers a new direction in agent-driven self-evolving systems but also establishes a theoretical foundation and outlines key technical pathways for future information systems to achieve rapid market responsiveness and continuous autonomous evolution.
Application Category
📝 Abstract
In this extended abstract, we propose a novel research topic in the field of agentic AI, which we refer to as self-coding information systems. These systems will be able to dynamically adapt their structure or behavior by evaluating potential adaptation decisions, generate source code, test, and (re)deploy their source code autonomously, at runtime, reducing the time to market of new features. Here we motivate the topic, provide a formal definition of self-coding information systems, discuss some expected impacts of the new technology, and indicate potential research directions.
Problem
Research questions and friction points this paper is trying to address.
self-coding
information systems
agentic AI
runtime adaptation
autonomous code generation
Innovation
Methods, ideas, or system contributions that make the work stand out.
self-coding
agentic AI
autonomous code generation
runtime self-adaptation
information systems
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