Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.
Abstract
The rise of AI agents is transforming how software can be built. The promise of agents is that developers might write code quicker, delegate multiple tasks to different agents, and even write a full piece of software purely out of natural language. In reality, what roles agents play in professional software development remains in question. This paper investigates how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments. Through field observations (N=13) and qualitative surveys (N=99), we find that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes, employing strategies for controlling agent behavior leveraging their expertise. In addition, experienced developers enjoy working with agents as source of collaboration rather than complete delegation given their judgment for task suitability. Our results shed light on the value of software development best practices in effective use of agents, suggest the kinds of tasks for which agents may be suitable, and point towards future opportunities for better agentic interfaces and agentic use guidelines.
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
Artificial intelligence is rapidly changing the landscape of software development. With the unique ability to quickly generate code and the potential to disrupt traditional workflows, AI tools have found growing adoption within the software development process. Subsequently, this topic has been the focus of academic work, including research examining qualitative impacts to productivity and the analysis of sentiments from the developers who utilize AI tools. While this material is extensive, our research team identified a gap within existing literature: what do software managers have to say? The overarching goal of this study is to examine the views of software managers on how AI tools have affected software development. We seek to understand how managers, who leverage a top-down view of the development process, perceive the influence of AI on developers, their own roles, and the broader labor market. To answer these questions, we conducted an empirical study by releasing an online questionnaire containing both qualitative and quantitative questions, sampling software managers employed across both tech-focused and non-tech-focused companies. Through a survey of 42 managers, we found that managers hold nuanced views on the introduction of AI into software development. They encourage developers to use AI, perceive it as valuable for testing, and apply it themselves for knowledge work. At the same time, they raise concerns about privacy, responsibility, transparency, and over-reliance. Many also predict a loss of jobs within the software development market due to consolidation driven by AI. Overall, AI is seen by managers as both a powerful productivity tool and a source of new ethical challenges. Our investigation paves the way for a comprehensive understanding of how AI is perceived by those who directly manage the introduction of these tools into traditional software development workflows, revealing a road map for future endeavors for the software development community.
Xin Zhao, Brian Vu, Sitesh Pattanaik· AIware· 0 citations
Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows. While developers increasingly benefit from these coding agents, their impact on software quality remains insufficiently understood. In particular, how agentic contributions evolve across the software development lifecycle has not been thoroughly investigated. This study aims to characterize agentic pull requests (PR) in comparison to human generated PRs and to examine how their properties change across different stages of the development lifecycle. Using the AIDev dataset, we first analyze how differences in merge rates between agentic and human generated PRs vary over time. We then identify the types of development tasks where AI coding agents are predominantly applied and investigate how these task distributions evolve across development quarters. Finally, we compare a set of key characteristics of agentic and human generated PRs, focusing on their implications for software quality and their temporal dynamics. Overall, our findings provide an empirical and longitudinal perspective on the role of AI coding agents in software development, offering a more nuanced understanding of their benefits and limitations in real-world practices.
Iren Mazloomzadeh, Mohammad Mehdi Morovati, F. Khomh· 0 citations
The rise of Software Engineering (SE) agents, i.e., LLM-based agents that can understand large codebases and carry out engineering tasks with limited human intervention, has been marked by rapid advances and adoption, but little is known about how developers build these systems in practice: existing studies mine repositories or examine deployment, but few investigate how SE agents are constructed. Through semi-structured interviews with 20 practitioners from 12 organizations and an online survey of 80 practitioners, this paper is the first to study how SE processes are changing in the development of SE agents and what challenges developers face. We find that as implementation becomes cheaper, bottlenecks shift rather than disappear: long-standing work in requirements, coordination, and deployment becomes more visible, while reviewing generated code and evaluating agent behavior become new and increasingly central forms of work. We characterize a seven-stage workflow and five process shifts, including a move toward evaluation-driven development, in which evaluation is increasingly defined early and steers iteration, and the emergence of specifications as first-class artifacts that teams test and version alongside code. We further identify six challenges that teams face, together with 12 corresponding practices they use or propose to address them, including unreliable evaluation signals, comprehension debt as code outpaces understanding, and behavioral changes introduced by provider-side model updates.
Yunbo Lyu, David Williams, Jieke Shi et al.· 0 citations
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.
Madhurima Kommuru, Srujana Pulipaka· International Journal of Mod...· 0 citations
Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agent harness: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops. While these agent harnesses evolve at extreme velocities, no study has examined how this evolution affects agent quality (i.e., effectiveness and efficiency) over time. Practitioners regularly report quality regressions after agent harness updates, yet consistently attribute them to the underlying model rather than the harness itself. In this paper, we address this gap by conducting the first controlled longitudinal study that isolates the agent harness contribution. Unlike prior work that fixes the agent harness and varies the model, we fix the model and vary only the agent harness, evaluating 35 sequential releases to measure their impact on agent effectiveness and efficiency. We first empirically study the development and release evolution of five major open-source agent harnesses (i.e., Codex, Qwen Code, Gemini, OpenCode, and OpenHands), revealing extreme release velocities exceeding two releases per day and thousands of issues within months. We then perform a controlled deep dive into 35 sequential releases of the Qwen Code CLI, evaluating each against 50 stratified SWE-bench Verified tasks while holding the underlying LLM constant. We trace the resulting quality fluctuations to specific development patterns and architectural components, and illustrate our findings with concrete qualitative evidence linking individual pull requests to measured quality shifts.
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
MIT News · Artificial Intelligence· news.mit.eduJul 7, 2026
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.
