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Researchers have released AgentLens, an open-source benchmark that moves beyond binary pass/fail scoring to evaluate how coding agents follow instructions, use tools, verify work, and recover from errors throughout their entire problem-solving process. The benchmark combines formal verification with LLM-generated trajectory reviews and side-by-side comparisons to provide detailed explanations for each evaluation score, enabling developers to diagnose agent behavior and catch regressions in production pipelines.
Why it matters: As AI coding agents become production tools, evaluating the quality of their reasoning and interaction patterns—not just final outputs—is critical for identifying when agents are trustworthy versus when they might mislead users.
