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[2602.17037] Wink: Recovering from Misbehaviors in Coding Agents
Summary
The paper presents 'Wink', a system designed to recover coding agents from misbehaviors, enhancing their reliability in software development by addressing common issues like specification drift and tool call failures.
Why It Matters
As coding agents powered by large language models become more prevalent in software engineering, ensuring their reliability is crucial. This research addresses significant challenges that can disrupt workflows, providing a solution that enhances productivity and reduces manual intervention.
Key Takeaways
- Wink effectively resolves 90% of misbehaviors requiring a single intervention.
- The system categorizes misbehaviors into Specification Drift, Reasoning Problems, and Tool Call Failures, which occur in about 30% of agent trajectories.
- Wink's deployment led to a significant reduction in Tool Call Failures and engineer interventions in production environments.
Computer Science > Software Engineering arXiv:2602.17037 (cs) [Submitted on 19 Feb 2026] Title:Wink: Recovering from Misbehaviors in Coding Agents Authors:Rahul Nanda, Chandra Maddila, Smriti Jha, Euna Mehnaz Khan, Matteo Paltenghi, Satish Chandra View a PDF of the paper titled Wink: Recovering from Misbehaviors in Coding Agents, by Rahul Nanda and 5 other authors View PDF HTML (experimental) Abstract:Autonomous coding agents, powered by large language models (LLMs), are increasingly being adopted in the software industry to automate complex engineering tasks. However, these agents are prone to a wide range of misbehaviors, such as deviating from the user's instructions, getting stuck in repetitive loops, or failing to use tools correctly. These failures disrupt the development workflow and often require resource-intensive manual intervention. In this paper, we present a system for automatically recovering from agentic misbehaviors at scale. We first introduce a taxonomy of misbehaviors grounded in an analysis of production traffic, identifying three primary categories: Specification Drift, Reasoning Problems, and Tool Call Failures, which we find occur in about 30% of all agent trajectories. To address these issues, we developed a lightweight, asynchronous self-intervention system named Wink. Wink observes agent trajectories and provides targeted course-correction guidance to nudge the agent back to a productive path. We evaluated our system on over 10,000 real world agen...