Abstract Generative artificial intelligence (AI) is boosting the use of LLM-based conversational assistants in many domains. In software engineering, numerous integrated development environments (IDEs) offer conversational assistants to improve productivity, leading to AI-IDEs. However, the integration of assistants into IDEs is often ad-hoc, rigid and opaque. Moreover, the assistant-generated code is frequently untraceable, and developers struggle to trust it. Given the diversity of software construction processes, AI-IDE architectures should be: (i) open , permitting the on-demand configuration of assistive tasks, and enabling the coordination of assistants potentially built atop diverse LLMs; (ii) accountable , tracing the assistant contributions and exploiting this information to obtain project insights; (iii) trustworthy , allowing the customisation of safeguard protocols for the assistive tasks. To fill this gap, we propose an open, extensible, accountable architecture for AI-IDEs, which incorporates validation loops for trustworthy assistance. We have implemented this architecture as an extensible plugin for Java development within Eclipse, called Caret . We have conducted offline evaluations and a user study, which demonstrate that Caret can be easily extended with new tasks, and its handling of context and validation loops results in effectiveness improvements up to 147.34% compared to the baseline. Overall, our architecture facilitates the seamless integration of LLM-based conversational assistants into IDEs, coping with new assistive demands for specific needs, offering detailed accountability of development contributions, and incorporating configurable validation loops for trustworthy development.
Contreras et al. (2026) studied this question.