Ziyou Li

Ziyou Li, Agnia Sergeyuk, Maliheh Izadi

Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflows. We present Prompt-with-Me, a practical solution for structured prompt management embedded directly in the development environment. The system automatically classifies prompts using a four-dimensional taxonomy encompassing intent, author role, software development lifecycle stage, and prompt type. To enhance prompt reuse and quality, Prompt-with-Me suggests language refinements, masks sensitive information, and extracts reusable templates from a developer's prompt library. Our taxonomy study of 1108 real-world prompts demonstrates that modern LLMs can accurately classify software engineering prompts. Furthermore, our user study with 11 participants shows strong developer acceptance, with high usability (Mean SUS=73), low cognitive load (Mean NASA-TLX=21), and reported gains in prompt quality and efficiency through reduced repetitive effort. Lastly, we offer actionable insights for building the next generation of prompt management and maintenance tools for software engineering workflows.

Roham Koohestani, Ziyou Li, Anton Podkopaev et al.

This paper establishes a formal equivalence between the architectural classes of modern agentic AI systems and the abstract machines of the Chomsky hierarchy. We posit that the memory architecture of an AI agent is the definitive feature determining its computational power and that it directly maps it to a corresponding class of automaton. Specifically, we demonstrate that simple reflex agents are equivalent to Finite Automata, hierarchical task-decomposition agents are equivalent to Pushdown Automata, and agents employing readable/writable memory for reflection are equivalent to TMs. This Automata-Agent Framework provides a principled methodology for right-sizing agent architectures to optimize computational efficiency and cost. More critically, it creates a direct pathway to formal verification, enables the application of mature techniques from automata theory to guarantee agent safety and predictability. By classifying agents, we can formally delineate the boundary between verifiable systems and those whose behavior is fundamentally undecidable. We address the inherent probabilistic nature of LLM-based agents by extending the framework to probabilistic automata that allow quantitative risk analysis. The paper concludes by outlining an agenda for developing static analysis tools and grammars for agentic frameworks.