Agentic Coding Needs Proactivity, Not Just Autonomy
Nghi D. Q. Bui, Georgios Evangelopoulos

TL;DR
This paper explores the concept of proactivity in coding agents, proposing a taxonomy and evaluation criteria to distinguish proactive behavior from mere autonomy in software development tools.
Contribution
It introduces a three-level taxonomy of proactivity, compares current agents against practical criteria, and suggests an evaluation protocol for proactive coding agents.
Findings
Proactive agents should be evaluated by insight policy quality.
A three-level taxonomy of proactivity is proposed.
Evaluation metrics include IDQ, CGS, and Learning Lift.
Abstract
Coding agents are rapidly changing the landscape of software development, moving from inline completion to autonomous systems that edit repositories, open pull requests, respond to issues, and run scheduled or webhook triggered routines across the development life cycle. The next generation is increasingly described as proactive and long-horizon: agents should notice relevant changes before the developer asks, connect signals across tools, decide when to interrupt, and carry preferences across sessions. Yet the field still lacks a clear account of what proactivity means for software development, how it differs from autonomy, what acceptance criteria proactive long-horizon tasks should satisfy, and which metrics determine whether unsolicited agent behavior is useful rather than merely active. Proactive coding agents should be evaluated by the quality and improvement of their insight…
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