definity definitely defines agentic data engineering platform
Tricky to spell but easy to pronounce, definity has unveiled its agentic data engineering platform.
The technology is purpose-built to operate and optimise enterprise lakehouse and Spark data pipelines.
The team remind us that enterprise data platforms now underpin AI, analytics and core business operations.
Delivering another home truth, definity wants us to remember that data engineering teams are expected to deliver faster while maintaining production reliability and controlling costs across increasingly complex environments.
However, says definity, most teams still operate with fragmented tooling that monitors isolated signals – data quality, execution health, infrastructure performance, or spend – after the fact and without a unified operational context.
As a result, data engineering remains reactive and manual, and more importantly, AI agents lack the runtime understanding required to take effective action and operate safely in production environments.
Monitoring moves to agentic operation
This means that definity now introduces a new operating model for enterprise data platforms: agentic data engineering.
The platform provides runtime intelligence powering AI agents that enable teams to continuously optimise platform cost, prevent incidents before they impact the business, and increase developer velocity.
According to Roy Daniel, CEO and co-founder of definity, at the core of definity is an in-motion architecture that operates directly within production pipelines, without requiring code changes. By observing pipelines during execution, the platform captures full-stack signals across infrastructure behaviour, pipeline execution, and data characteristics.
definity’s Daniel: Agentic data engineering introduces a new operating model – agents that continuously understand, optimise and protect data pipelines in production.
This unified runtime context, combined with the ability to safely control pipeline execution in real time, enables the shift from monitoring to true agentic operation.
Without runtime intelligence and control, AI agents remain advisory and post hoc.
With it, they can autonomously analyse, optimise, and take action in production.
“As AI becomes embedded across the enterprise, data platforms can no longer be operated through fragmented, reactive tooling,” said Daniel. “Agentic data engineering introduces a new operating model – agents that continuously understand, optimise and protect data pipelines in production. definity was purpose-built to deliver on that promise for the enterprise.”
He promises that global enterprises are able to use definity to reduce platform costs by more than 30 percent through job-level optimisation.
Spark supercharge
They can also prevent pipeline and data incidents in motion before they impact the business and resolve complex Spark issues 10 times faster.
The software supports large lakehouse deployments across both cloud and on-premises Spark environments, including Databricks, AWS EMR, GCP Dataproc, and Spark on K8S. By embedding intelligence directly into pipeline execution, definity simplifies day-to-day platform operations while enabling continuous optimization at enterprise scale. Adoption has accelerated as enterprises seek operational leverage beyond traditional observability tooling in these platforms.