AI Agents for Data Engineering: Pipelines, Tests, and Context

AI agents help data engineers with the drafting work: pipeline logic, schema documentation, validation tests, and data-contract boilerplate. Engineers review and run every output before it touches production data. Tools like fenbs keep the context those sessions need, including data-contract decisions, schema rules, and lessons from runs that failed.

Data engineering involves a lot of repetitive structure. A transform looks similar to twenty transforms before it. An agent that understands the pattern can produce a working first draft faster than typing it from scratch. The skill is knowing which part of the output to trust and which part to check carefully.

Where AI agents fit in a data engineering workflow

Not every task benefits from an agent. Agents work well on tasks with clear structure: produce this transform from that schema, write tests for this contract, document this pipeline step. They need human review on anything that affects data in production, because they do not have direct knowledge of your data's actual shape or the downstream systems depending on it.

• Draft an ETL or ELT transform from a schema description and a transformation rule
• Write data quality tests and validation checks from a contract specification
• Document a pipeline step in a standard format
• Review a schema diff and flag breaking changes
• Summarize a failed run's error log into a structured incident description
• Generate a data dictionary entry from a table description

Pipeline changes and schema review

A common request is drafting a new transform or modifying an existing one. Give an agent the source schema, the target schema, and the transformation logic in plain language. It returns a first draft. Your job is to check the field mappings, the handling of nulls and edge cases, and the business rule each step encodes.

Schema review is a good fit for agents because breaking changes follow patterns: column removed, type narrowed, non-null added. An agent can compare two schema versions and return a structured list of changes with a note on each one. A data contract decision made last quarter explains which of those changes are acceptable and which need a migration plan.

That is where context becomes critical. If the contract decision lives only in a Confluence page you forgot about, the agent working on a schema change today will not know it. Recording the decision in fenbs means the next agent you ask to review a schema diff reads it at the start of the session.

Validation tests and data quality checks

Writing data quality tests is tedious but important. An agent can generate a test suite from a contract specification: null checks, range checks, referential integrity, and business-rule assertions. Give it the contract and the schema; review each test before adding it to your pipeline.

The most useful contribution is when you describe a failure mode in plain language and ask the agent to write a test that would catch it. Explain what went wrong in the last incident, and the agent translates that into a concrete assertion. The discipline of describing failures in words also makes them easier to record as lessons.

Learning from failed runs

Data pipelines fail in ways that are worth remembering. A run that failed because of an unexpected null in a third-party feed is a lesson about that feed's reliability. An agent can help you write a structured post-mortem from an error log, but the lesson needs to live somewhere the next engineer or next session can find it.

The same failure mode appearing twice in two months is a signal that the lesson did not travel. Recording it in fenbs with the context of what happened and what the fix was means the next agent working on a similar pipeline reads the lesson before it drafts anything. The context engineering for AI agents guide covers how to structure this kind of ongoing context.

Documentation and data dictionaries

Documentation falls behind because it takes time and does not feel urgent until someone new joins or a pipeline breaks at 2 AM. An agent can generate a first draft of a data dictionary entry, a pipeline overview, or a README from a description you provide. Give it the schema, the business context, and any relevant contract decisions.

Review it for accuracy before publishing. Agents produce plausible documentation that can be wrong in specific ways, especially about business rules they inferred rather than were told. Correct those errors and save the correct version back as a note so future drafts start from the right description.

How to do this with fenbs

Start with an active pipeline where the team has already made a few contract decisions. Record those before connecting an assistant.

1. Create a free account. Start with a personal kanban board. When a team needs shared access, use a team kanban board and set member roles.
2. Add the pipeline or data product as a project in fenbs. Write a short description of its purpose and the systems it connects.
3. Record each data-contract decision as a Decision in fenbs: what was agreed, who agreed it, and whether it is a standing rule. Add schema constraints that apply to any new work.
4. Connect your AI app from Integrations. At the start of each session, ask the assistant to read the project context, including decisions and any prior lessons.
5. After each significant failure, add a Lesson in fenbs. Before each session ends, ask the assistant to save progress and note any new assumptions or constraints.

When an AI agent is not the right choice

Do not let an agent push changes to a production pipeline without human review of the output and a test run in a safe environment. Agents do not have runtime visibility into your data or its dependencies. They work on structure and patterns; you supply the knowledge of what the data actually contains.

Agents also cannot replace the judgment involved in data modeling decisions: which tables to denormalize, how to handle late-arriving data, where the performance trade-offs are. Those decisions belong to an engineer. Record them in fenbs so the next session has them; do not expect an agent to derive them from first principles.