Definity embeds agents inside Spark pipelines to catch failures before they reach agentic AI systems

For most data engineering teams, managing pipeline reliability often means waiting for an alert, manually tracing failures across distributed jobs and clusters, and fixing problems after they've already hit the business. Agentic AI needs the data to be there, clean and on time. A pipeline that fails silently or delivers stale data doesn't just break a dashboard — it breaks the AI system depending on it.That gap is w…