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 what Definity, a Chicago-based data pipeline operations startup, is building into: embedding agents directly inside the Spark or DBT driver to act during a pipeline run, not after it. One enterprise customer identified 33% of its optimization opportunities in the first week of deployment and cut troubleshooting and optimization effort by 70%, according to Definity. The company also claims customers are resolving complex Spark issues up to 10x faster.”You need three big things for agentic data operations: full stack context that is real time and production aware. Control of the pipeline. And the ability to validate in a feedback loop. Without that, you can be outside looking in and read only,” Roy Daniel, CEO and co-founder of Definity told VentureBeat in an exclusive interview.The company on Wednesday announced that it has raised $12 million in Series A financing led by GreatPoint Ventures, with participation from Dynatrace and existing investors StageOne Ventures and Hyde Park Venture Partners.Why existing pipeline monitoring breaks down at scaleExisting tools approach the problem from outside the execution layer — Datadog, which acquired data quality monitor Metaplane last year, Databricks system tables, and platforms like Unravel Data and Acceldata all re …
