arXiv:2603.13428v2 Announce Type: replace-cross Abstract: With AI agents increasingly deployed as long-running systems, it becomes essential to autonomously construct and continuously evolve customized software to enable interaction within dynamic environments. Yet, existing benchmarks evaluate agents on isolated, one-off coding tasks, neglecting the temporal dependencies and technical debt inherent in real-world software evolution. To bridge this gap, we introduce DeepCommit, an agentic pipeline that reconstructs verifiable Milestone DAGs from noisy commit logs, where milestones are defined a
The increasing deployment of AI agents into long-running systems necessitates robust evaluation methods that account for continuous evolution and real-world complexities.
This development addresses a critical gap in AI agent evaluation, moving beyond isolated tasks to assess their ability to manage and evolve software, which is crucial for reliability and scalability.
The introduction of benchmarks like DeepCommit shifts AI agent evaluation from static coding challenges to dynamic, continuous software evolution, mirroring real-world operational demands.
- · AI agent developers
- · Software engineering researchers
- · Companies deploying AI in dynamic environments
- · Traditional AI benchmarking methods
- · AI agents poorly designed for continuous adaptation
Improved, more robust AI agents capable of continuous software evolution will emerge.
This will accelerate the adoption of autonomous agents in complex, long-running systems across various industries.
The development lifecycle of software could be fundamentally altered, with AI agents taking a more active role in maintenance and evolution.
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Read at arXiv cs.AI