dltHub: dlt made agents good at building pipelines. Now they're safe enough to run for your whole team.

dltHub presents itself as an AI-native data engineering platform that combines agent-assisted pipeline development with managed infrastructure, governance, and collaboration features for data teams in scale-ups, enterprises, and regulated industries. Its AI Harness works with coding agents such as Claude, Codex, and Cursor to build, deploy, monitor, diagnose, and propose fixes for pipelines using production context while keeping credentials protected and human approvals in the loop. The platform now supports organizations and workspaces, Git-based CI/CD promotion between staging and production, workspace-specific keys and secrets, ownership and review workflows, and usage-based pricing rather than per-seat fees. dltHub also manages scheduling, orchestration, monitoring, scaling, and infrastructure provisioning without requiring teams to operate Airflow or Kubernetes, while processing data into existing warehouses rather than storing it itself. Its six main components are the AI Harness, Context Catalog, ingestion, transformation, orchestration, and managed infrastructure; many core capabilities are generally available, while transformations, email alerts, secrets management, and several catalog features remain in public preview or development.

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