2026· International Journal of Modern Innovations and Emerging Trends· 0 citations
Abstract
AI agents are becoming a fundamental part of modern software creation, helping developers in generating code, debugging, designing systems, etc. But there is a clear difference between how beginners and experienced software engineers get benefits from these tools. Newbies usually depend on agents for one-time prompts and quick answers, whereas mature users utilize them through well-defined, repeated workflows that raise productivity and consistency. In this article, we discuss this difference and emphasize that getting the full potential does not merely depend on better prompts but on workflows driven by instructions developers create clear and reusable instruction files to direct agent behavior across tasks. When developers stop seeing agents only as chat interfaces but as programmable collaborators, they can produce more reliable and high-quality outputs. We offer in our paper methods like designing modular instructions, narrowing down the context, and iterative refinement loops, as well as a case study illustrating how a team made a code review more efficient and minimized the rework by making agent instructions standard. The results stress that structured forms of interaction rather than sporadic use are the main ways to tap into advanced features. Our paper provides a conceptual model for agent usage at large scale, hands-on advice for the implementation of instruction files in actual settings, and validation that skillful developers can far exceed basic usage by adopting orderly, system-like approaches to agent collaboration.
IDE Agent Mode is changing how the developers work, from writing code to debugging & software management, by embedding powerful AI features right inside popular development setups. This article first conceptualizes IDE Agent Mode as a workflow where AIs are coding partners that not only understand the context of the project but are also able to write code, suggest better solutions, automate routine tasks, and even help with debugging at the same time. With the increase in the complexities of software projects and the trend towards shorter development cycles, AI-driven software development has become an effective way to enhance developers' productivity, minimize human errors, and foster innovation. On the other hand, deciding which AI model should be used for which programming task remains one thorny issue that developers and companies face. This is due to significant differences among models in their performance, speed, reasoning capabilities, cost, management of context, and compatibility with the development tools. This article not only lists the AI models that are most often used in IDE Agent environments but also assesses their performance in various coding assistance tasks such as code completion, bug fixing, documentation generation, and architectural reasoning. The method includes a comparative study, obtaining developer feedback, and assessment of the practical workflow in order to determine the pros and cons of different models in real-life software engineering situations. Results show that there is no 'silver bullet' model that fits all requirements; rather, the choice of model should be tailored to the project's needs, the professional level of the team, and scalability requirements, as well as financial constraints. The research also reveals that small models serve very well for fast coding assistance, whereas the more feature-heavy models are quite apt at doing complex tasks such as debugging and system-level designing.
Madhurima Kommuru· International Journal of Mod...· 0 citations
This paper is the first to study how SE processes are changing in the development of SE agents and what challenges developers face, and describes a seven-stage workflow and five process shifts, including a move toward evaluation-driven development.
Yunbo Lyu, David Williams, Jieke Shi et al.· 0 citations
The recent emergence of vibe-coding workflows is changing what coding agents are expected to do. Instead of merely completing code under fully specified instructions, agents are increasingly expected to transform incomplete product intent into working software by combining various abilities including planning, requirement clarification, tool use, debugging, and repository-level construction. Yet existing benchmarks have not fully caught up with this shift, evaluating agents on static, fully specified tasks. In this paper, we introduce ICAE-Bench, a benchmark for evaluating coding agents under interactive project-building settings. The basic idea is to start from a fuzzy product requirement, simulating the dynamic paradigm with an automated User Agent. To make this setting both realistic and evaluable, ICAE-Bench introduces three key designs. First, to avoid the ambiguity of unconstrained fuzzy requirements, each task derives ambiguity from a precise real open-source repository with executable behavior. Second, to ensure high-quality and reproducible user simulation, ICAE-Bench grounds interaction through User Agent Data, allowing the User Agent to reveal hidden constraints without inventing new requirements or leaking implementation artifacts. Third, to evaluate open-ended repositories fairly, ICAE-Bench uses standardized black-box tests together with multi-dimensional diagnostics, including functional correctness, semantic and API similarity, structural fidelity, design quality, and interaction quality.
Zhongyuan Peng, Dan Huang, Chuyu Zhang et al.· 1 citation
The proliferation of Generative Artificial Intelligence (Gen AI) powered by large language models (LLMs) has transformed the software development process, introducing new paradigms for code generation, debugging, testing, and maintenance. While early applications focused on leveraging single, independent LLMs to assist developers with isolated tasks, recent advances have shifted toward multi-agent systems (MAS) that orchestrate multiple LLM-based agents working collaboratively toward common objectives. Despite their promising potential, using MAS encompasses a set of challenges for developers who have to carefully select the right technology, devise proper coordination rules, and design specific roles for the involved agents. In this paper, we provide a comprehensive overview of the existing tools and frameworks for implementing MAS in software engineering. First, we conducted a quantitative analysis of the most relevant open source MAS frameworks by evaluating their documentation, features, and capabilities from the developers'perspective. Second, we performed a qualitative evaluation of a subset of the selected frameworks by implementing a common use case: the summarization of README.MD files. The findings show that the selected frameworks provide a good coverage of fundamental components of MAS, though advanced features such as telemetry of agents are still missing. In addition, the empirical evaluation shows that there is no significant difference in terms of ROUGE scores considering the summarization task. Finally, we provide a set of lessons learned and challenges that can help researchers and practitioners to select a suitable MAS framework according to their needs.
Mariama Celi Serafim De Oliveira, M. Ibiyo, Marco Gianrusso et al.· 0 citations
Agent Skills are an emerging way to extend large language model agents with reusable procedural knowledge that the agent loads on demand. Anthropic introduced Agent Skills and published the format as an open specification supported across several agent tools. This note argues that a skill is a software artefact and that its construction should follow software-engineering principles, with qualifications: single responsibility, separation of interface from implementation, low coupling, and economy in a shared token budget, together with behavioural evaluation in place of deterministic testing. Using Claude Code as the reference implementation, it describes how a skill is structured, how its contents are loaded in stages, and how to write the description on which selection depends. It places skills against the other mechanisms a developer can use to shape agent behaviour, like project memory files, slash commands, subagents, external tool connections, and hooks, and gives a rule for choosing between them based on who decides that a mechanism runs and what guarantee it provides. It then sets out an evaluation-driven authoring process, a set of patterns and faults commonly encountered in authoring, and the trust question raised by using skills from third parties. We illustrate the comparison drawn in UML class style, the loading model, the anatomy of a skill, the relative position of each mechanism, and the points at which skills and hooks act during a session.
Large language model (LLM)-powered agents have rapidly evolved from code-completion tools into solvers of complex software engineering tasks. As developers collaborate with coding agents over time, their preferences emerge through repeated interactions and can be used to adapt agent behavior to better meet individual developers'needs. Capturing and reusing these preferences may reduce repeated corrections and improve developer-agent collaboration. Agent skills provide a lightweight mechanism for transferring experience without modifying model parameters. However, existing work primarily focuses on task-specific skills, and it remains unclear whether developer-specific skills distilled from interaction histories can generalize to future tasks. We propose a framework for extracting reusable developer preferences from interaction traces. It first generates personalized skills through rule-based bootstrapping and evidence-grounded refinement, and then evaluates them using a reproducible replay framework with an interactive, trajectory-conditioned LLM-based human developer simulator. We conduct an experiment on 206 real-world developer-agent sessions from 13 developers and compare personalized skills against no-skill, generic-skill, and other-user-skill baselines. Personalized skills provide small and inconsistent improvements over the no-skill baseline, whereas generic skills pooled across developers achieve the largest and most consistent gains. Further analysis suggests that personalized skills become more effective when developer preferences appear frequently, particularly when their histories contain multiple examples relevant to future tasks. These findings provide empirical insights into when developer-specific personalization is effective and demonstrate that broadly transferable procedural knowledge can be more robust than developer-specific preference signals.