Frequently Asked Questions

An agentic pipeline is a data pipeline that knows why it exists, can autonomously detect and repair failures, and is coordinated by AI agents that reason, act, and learn over time.

A pipeline becomes agentic when it implements the three core tenets of the category: intent awareness, self-healing, and AI orchestration.

ETL pipelines execute predefined workflows. Agentic pipelines continuously adapt to changing data conditions, support both human and machine consumers, and can repair failures automatically.

Because the data stack is no longer serving only human analysts. AI systems, RAG applications, and autonomous agents are becoming first-class consumers of data infrastructure.

Not exactly. Self-healing is one of the three tenets of agentic pipelines, but an agentic pipeline must also be intent-aware and AI-orchestrated.

The Agentic Pipeline Framework (APF) defines five architectural requirements an agentic pipeline system must satisfy: pipeline intent is explicitly defined; pipelines are self-healing; execution is decoupled from orchestration; systems serve both human and machine consumers; and an AI orchestration layer coordinates all agents.

The Autonomous Data Loop is the four-step cycle through which a self-healing pipeline operates: Observe pipeline health, Diagnose the root cause of any failure, Repair the problem at the code level, and Learn from the outcome so the same class of failure is resolved faster next time.

Specialist agents are AI agents with defined roles within the pipeline — ingestion, transformation, data quality, or repair. They are deployed by an orchestrating agent and reason through problems rather than follow static instructions. A repair specialist, for example, can write or rewrite dbt and Spark code to fix a failure, then deploy that fix through the customer's existing CI/CD.

Bolting an AI layer onto a static pipeline changes how tasks are executed but not how the pipeline thinks. An agentic data pipeline is architected from the ground up around intent, agents, and memory; the system knows what it is for, can act on that knowledge, and retains what it learns across every run.

Dagen.ai is the platform built to implement the APF. It uses specialist agents to write, rewrite, and debug dbt and Spark code; deploys repairs through the customer's existing CI/CD; and makes every action auditable and reversible.