Complete AI Training
Sign inGet my AI kit

Your job's AI kit

Get your AI kit

Tell us who you are and what you do. We show you your kit right away and email you the link: skills, prompts, AI agents, MCP servers and courses for your job.

500+ jobs ready, and we make a kit for any other job. No payment needed to look.

Share

AI agent for data engineers

Data Pipeline Schema Adaptation Agent

Pipelines adapt to upstream changes without corrupting data.

CodingIT and development
See how it worksGet access
Data Pipeline Schema Adaptation Agent: what goes in, what the agent does and what you get

What it does

Upstream data changes, such as a renamed field or a switch in units, can break downstream jobs or quietly corrupt reports. When a schema change is detected upstream, this agent diffs the old and new schema and identifies what changed. It checks whether each changed field keeps the same meaning and unit, using the documented field definitions. If it does, it applies a mapping on a branch and runs downstream fixture checks. If outputs differ from expectations, it revises the mapping. If a renamed field also changed units, it stops the simple rename and requires a supported conversion. A data engineer approves. Edge case: 'weight_lb' becomes 'weight' in kilograms, so a rename alone would corrupt every report.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueYes, continueApprovedNoNo 1 STARTS WHEN Upstream schema change 2 USES A TOOL Diff the schema 3 CHECKS THE RESULT Is the meaning and unit unchanged? If not: require a supported conversion. Back to step 2. 4 USES A TOOL Apply the mapping on a branch 5 USES A TOOL Run downstream fixture checks 6 CHECKS THE RESULT Do downstream outputs match expectations? If not: revise the mapping. Back to step 4. 7 YOU APPROVE Data engineer approves 8 RESULT Tested schema adaptation patch
Read the steps as a list
  1. Upstream schema change
  2. Diff the schema
  3. Is the meaning and unit unchanged?If not: require a supported conversion. Back to step 2.
  4. Apply the mapping on a branch
  5. Run downstream fixture checks
  6. Do downstream outputs match expectations?If not: revise the mapping. Back to step 4.
  7. Data engineer approvesThe agent waits here for your OK.
  8. Tested schema adaptation patch

How it decides

It maps by semantics, not just names, and checks units.

  • Check meaning and units before any mapping.
  • A unit change requires a supported conversion, never a rename.
  • Outputs must match expectations within tolerance.

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Field definitions source
  • Downstream fixtures and expected outputs
  • Tolerance for output differences (default 0.1%)
  • Who approves the patch

What keeps you in control

It always asks you first

  • Production schema
  • Data publication

Hard limits

  • Branch only.

It stops when

  • Done: checks pass.

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensOn June 9 the supplier feed renamed 'price_usd' to 'price' and added a currency column. The agent's unit check failed because 40% of rows were in euros. It required a conversion using the approved daily rates, applied it on a branch and ran fixture checks. Revenue totals matched expected outputs within $1. The data engineer approved the patch on June 10.

More agents for data engineers

Quality control · Risk management

Government Data Publication Preflight Agent

Datasets that are valid and safe to publish.

7 steps2 checks2 jobs
Quality control · Data cleanup

Insurance Bordereaux Validation Agent

Bordereaux that pass schema and reconcile to control totals.

7 steps1 check2 jobs
Quality control · Coding

Database Migration Rehearsal Agent

Migrations rehearsed safely with schema and data intact.

7 steps1 check4 jobs
Quality control · Science and research

Research Data Intake Validation Agent

A validated, reproducible working dataset with raw data untouched.

7 steps2 checks14 jobs