arXiv:2606.30573v1 Announce Type: new Abstract: We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks. Existing frontier SWE benchmarks typically provide complete requirements upfront and evaluate agents on autonomous implementation. In contrast, SWE-Interact places agents in a realistic developer workflow: a carefully designed user simulator starts with vague or incomplete instructions, progressively reveals requirements, inspects the agent's workspace, and provides targeted feedback, revisions, and new constra
The rapid advancement in large language models necessitates better evaluation methods for agentic capabilities, moving beyond static benchmarks to interactive, real-world scenarios.
This development addresses a critical gap in evaluating sophisticated AI agents, shifting focus from autonomous task completion to collaborative, iterative problem-solving, which is crucial for practical software development.
The standard for benchmarking coding agents now includes user-driven interaction, progressive requirement revelation, and feedback loops, leading to more robust and adaptable AI developers.
- · AI agent developers
- · Software engineering teams
- · AI evaluation platforms
- · Static AI benchmarking methods
- · Companies relying on autonomous, non-interactive agents
Coding agents will evolve to be more interactive and adaptive, capable of handling ambiguity and iterative feedback.
The efficiency and reliability of AI-assisted software development will significantly improve as agents integrate better into human workflows.
This could lead to a fundamental restructuring of software development teams, with AI agents acting as highly capable, iterative partners rather than simple code generators.
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