**Editorial Brief**
AA has introduced the Coding Agent Index, which includes three benchmarks covering a wide range of coding tasks. These benchmarks are designed to test different aspects of AI agents’ capabilities and help in understanding their performance better. The index is composed of:
– **SWE-Bench-Pro-Hard-AA**: 150 realistic coding tasks from Scale AI’s SWE-Bench Pro.
– **Terminal-Bench v2**: 84 agentic terminal tasks ranging from system administration to machine learning, with some tasks filtered due to environment incompatibility.
– **SWE-Atlas-QnA**: 124 technical questions about code behavior and issues, requiring agents to explore codebases.
This index provides a comprehensive view of how various coding agent models perform across different scenarios. It’s a valuable tool for researchers and developers looking to evaluate AI systems more accurately.
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