  Large Language Models (LLM) and Generative Pre-trained Transformers (GPT),
are reshaping the field of Software Engineering (SE). They enable innovative
methods for executing many software engineering tasks, including automated code
generation, debugging, maintenance, etc. However, only a limited number of
existing works have thoroughly explored the potential of GPT agents in SE. This
vision paper inquires about the role of GPT-based agents in SE. Our vision is
to leverage the capabilities of multiple GPT agents to contribute to SE tasks
and to propose an initial road map for future work. We argue that multiple GPT
agents can perform creative and demanding tasks far beyond coding and
debugging. GPT agents can also do project planning, requirements engineering,
and software design. These can be done through high-level descriptions given by
the human developer. We have shown in our initial experimental analysis for
simple software (e.g., Snake Game, Tic-Tac-Toe, Notepad) that multiple GPT
agents can produce high-quality code and document it carefully. We argue that
it shows a promise of unforeseen efficiency and will dramatically reduce
lead-times. To this end, we intend to expand our efforts to understand how we
can scale these autonomous capabilities further.
