arXiv:2606.02875v1 Announce Type: new Abstract: Coding-agent benchmarks evaluate whether a single uninterrupted agent can resolve a repository issue. Real software work is messier: tasks are interrupted, reassigned, reviewed, and resumed from partial states left by another agent or engineer. We study this missing dimension through \emph{handoff debt}: the rediscovery cost imposed when a predecessor's work is opaque or incomplete. Our takeover protocol interrupts a coding agent at deterministic handoff points, freezes the repository, and evaluates successor agents under four handoff views: repo
The proliferation of sophisticated coding agents makes their real-world integration challenges, particularly task handoffs, an increasingly critical research area.
This research addresses a fundamental hurdle for autonomous coding agents, impacting their reliability and the efficiency gains they can deliver in complex engineering environments.
The focus expands from agents completing isolated tasks to understanding and mitigating the 'handoff debt' incurred when tasks are interrupted and resumed by different agents or humans.
- · AI agent developers
- · Software engineering teams
- · Productivity software providers
- · Companies with chaotic development workflows
- · Software engineers unprepared for agent collaboration
Improved coordination mechanisms and protocols will be integrated into AI coding agents to manage task transitions more effectively.
Software development team structures and roles may evolve to explicitly incorporate and optimize for agent-to-agent and human-to-agent handoffs.
The concept of 'handoff debt' could be generalized across various AI-driven collaborative workflows, leading to new metrics for evaluating AI system integration.
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Read at arXiv cs.AI