Agentic AI Software Engineers: Programming with Trust
This is an incremental opinion piece discussing trust issues in AI-driven software engineering for developers and researchers.
The paper argues that deploying AI software engineers using LLMs requires building trust comparable to human practices, and suggests LLM agents could shift programming focus from scale to trust.
Large Language Models (LLMs) have shown surprising proficiency in generating code snippets, promising to automate large parts of software engineering via artificial intelligence (AI). We argue that successfully deploying AI software engineers requires a level of trust equal to or even greater than the trust established by human-driven software engineering practices. The recent trend toward LLM agents offers a path toward integrating the power of LLMs to create new code with the power of analysis tools to increase trust in the code. This opinion piece comments on whether LLM agents could dominate software engineering workflows in the future and whether the focus of programming will shift from programming at scale to programming with trust.