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
This 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
This study 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, offering a more nuanced understanding of their benefits and limitations in real-world practices.
Iren Mazloomzadeh, Mohammad Mehdi Morovati, F. Khomh· 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
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
This paper empirically study the development and release evolution of five major open-source agent harnesses, revealing extreme release velocities exceeding two releases per day and thousands of issues within months, and performs the first controlled longitudinal study that isolates the agent harness contribution.
O. Sghaier, Hao Li, Bram Adams et al.· 2 citations
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.
