Tools & Applications

The Silent Failures in Agentic Data Science: Why Autonomous Pipelines Break Without Alerts

The Mirage of HTTP 200 and Execution-Level Success In traditional software development, systems fail loudly: API calls throw exceptions, code exits with error codes, and monitoring alerts trigger immediate investigation. However, as data science teams deploy multi-agent systems—where foundation models act as reasoning engines to clean datasets, formulate hypotheses, execute SQL queries, and train

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