Modern human labor is characterized by specialization; we train for years and develop particular tools that allow us to perform well across a variety of tasks. Similarly, specialized AI agents with task-specific tools or architectures often fail to generalize beyond their intended scope. In this work, we ask: can agents achieve generalizability across diverse domains with a small, but well-chosen set of general tools? We propose OpenHands-Versa, a single-agent system with a modest number of general tools like code execution, search engine, web browser and multimodal file viewer, for three practical domains: software engineering, deep research, and web browsing. Notably, OpenHands-Versa demonstrates superior or competitive performance over task-specific specialized agents on three challenging benchmarks: SWE-Bench Multimodal, GAIA, and The Agent Company, with absolute improvements in success rate of 9.1, 1.3, and 9.1 points, respectively. Thus, our single-agent system can achieve strong generalization indicating that specialist agents for these domains provide no practical benefit. Furthermore, we find that specialist multi-agent systems do not generalize beyond their intended scope. These findings establish OpenHands-Versa as a strong baseline for future research.

Software testing is essential for maintaining software reliability, yet issue reproduction tests are often missing when bugs are reported. Automatically generating such tests can accelerate debugging, support test-driven development, and improve automated software engineering systems that rely on execution-based verification. Existing approaches largely depend on proprietary language models, limiting accessibility and reproducibility. We present SWE-Tester a framework for training open-source large language models to generate issue reproduction tests from natural language issue reports. To support this task, we curate a dataset of 41K training instances from 2.6K open-source GitHub repositories. Fine-tuning on this dataset improves issue reproduction performance across multiple model families, yielding gains of up to 10% points in success rate and 21% in change coverage on SWT-Bench Verified. We further study the effects of model size, training data scale, and inference-time compute, observing consistent improvements along all three axes. Our results demonstrate that open-source models can be effectively specialized for issue reproduction test generation and provide a scalable foundation for future software testing and automated issue-resolution systems.