arXiv:2603.26233v2 Announce Type: replace Abstract: As Large Language Model (LLM) agents are increasingly deployed in open-ended domains like software engineering, they frequently encounter underspecified instructions that lack crucial context. While human developers naturally resolve underspecification by asking clarifying questions, current agents are largely optimized for autonomous execution. In this work, we systematically evaluate the clarification-seeking abilities of LLM agents on an underspecified variant of SWE-bench Verified. We propose an uncertainty-aware multi-agent scaffold that
As LLM agents are increasingly deployed in complex, open-ended domains like software engineering, the limitations of their current autonomous execution approach are becoming more apparent.
Improving the clarification-seeking abilities of AI agents is crucial for their effective and reliable deployment in critical white-collar workflows, potentially reducing errors and increasing user trust.
The focus shifts from purely autonomous execution in AI agents to integrating uncertainty-aware, human-like clarification processes, enhancing their ability to handle ambiguous instructions.
- · AI Agent Developers
- · Software Engineering Teams
- · AI-powered SaaS Platforms
- · Manual Code Clarification Roles
- · Inefficient AI Agent Deployments
LLM agents will be more capable of handling ambiguous or underspecified tasks, leading to fewer errors and more robust outputs.
This capability can accelerate the adoption of AI agents in highly complex and regulated industries where clarity and correctness are paramount.
The development of sophisticated clarification mechanisms might lead to novel interaction paradigms between humans and AI, fostering more collaborative 'copilot' relationships.
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