DevOps Meets AI: Evaluating the Performance of Leading LLMs | Harness Blog

Harness evaluates how large language models can automate DevOps pipeline configuration by generating YAML steps from natural-language commands, aiming to reduce manual effort, errors, and deployment delays. The study compares direct calls to GPT-4o, O3-mini-medium, Claude 3.7, DeepSeek R1, and DeepSeek V3 with an AutoGen-based agentic approach that separates tasks such as context generation, schema verification, and step creation. Using 115 benchmark pipeline-step YAML files derived from QA test data, the team measured outputs against known configurations with DeepDiff-based similarity metrics, including modified versions that account for schema validity, optional fields, and extra keys. Results indicate that agentic workflows improved quality across all metrics and eliminated schema-validation failures among supported models, while the strongest models achieved pipeline-generation pass rates above 95 percent.

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