Toward architecting self-coding information systems
Abstract
Self-coding information systems represent a novel agentic AI approach where systems autonomously generate, test, and deploy code at runtime to dynamically adapt their functionality and reduce feature development time.
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.
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