A reference framework for building and evaluating agentic data pipeline systems.
The Agentic Data Pipeline Framework defines the architectural requirements of an agentic data pipeline system. Like the Twelve-Factor App for web services, APF provides a shared vocabulary for building, evaluating, and comparing agentic data pipeline platforms.
Pipelines must know why they exist.
They must encode the business outcome they support, the consumers they serve, the freshness expectations they must satisfy, and the impact of failure on downstream systems.
Pipelines must continuously observe their own behavior.
This includes pipeline health, data quality, schema evolution, execution performance, and other signals required for autonomous operation.
Pipelines must detect and correct failures automatically.
Schema drift correction, retry strategies, dynamic workflow adjustment, and remediation planning should be first-class capabilities within the framework.
Pipelines must retain operational knowledge.
This includes prior incidents, successful remediation patterns, historical system behavior, and persistent memory that improves future decisions over time.
Pipelines must coordinate specialist AI agents.
Rather than depending solely on static orchestration, agentic data pipelines should rely on cooperating agents with specialized roles coordinated by an orchestrating agent.
APF turns the category definition of agentic data pipelines into a practical evaluation model. It ensures systems implement the three tenets of the category: intent awareness, self-healing, and AI orchestration.
Dagen.ai is built around these principles; an AI-native workspace purpose-built for teams designing, operating, and monitoring agentic data pipeline systems.