Warp scores 71% on SWE-bench Verified

SWE-bench serves as the primary benchmark for evaluating large language models (LLMs) and AI agents on coding tasks by assessing their ability to address real-world GitHub issues within complex open-source codebases. Warp's agent demonstrated significant success on the SWE-bench Verified evaluation, autonomously resolving 71% of instances and ranking in the top five on the leaderboard, highlighting the effectiveness of its single-agent, single-attempt architecture. The system utilizes an array of tools, such as editfiles and createfile, to enhance the agent's capability for efficient code modifications, and employs a model-choice infrastructure to manage provider outages and latency. Its evaluation harness, adapted for Docker and integrated with Warp's UI framework, allows for comprehensive testing across 500 instances, underscoring the value of context-dependent tool availability and recovery mechanisms in agentic systems. Warp's performance suggests that single-attempt architectures can be competitive for coding tasks, especially for user-facing applications where multi-attempt methods might introduce unacceptable latency.