Self-Healing Schema Agent

Agentic Pipeline Repair with Human Approval

Demo mode: This showcase simulates Amazon API schema changes that break data pipelines. The agent uses Claude to diagnose the issue and generate a YAML config patch — in production, this creates a PR for human approval before auto-deploying the fix.

Select a schema error scenario:

Self-Healing Schema Agent

Case Study

Industry

Amazon Agency / E-Commerce Data Infrastructure

Context

Built for an Amazon agency that manages advertising and sales data for 30+ brands across multiple European marketplaces. Their data pipeline pulls reports from Amazon's SP-API, Advertising API, and Data Kiosk into BigQuery for analytics and reporting.

The Challenge

Amazon changes their API schemas 2-3 times per year without warning. Fields get renamed, added, or removed — and the pipeline breaks silently. The team discovers the issue when clients report missing data in their dashboards, sometimes hours or days later.

Pain Points

✖Schema changes break the pipeline silently — no automatic detection or recovery

✖Debugging takes hours: compare expected vs. actual fields, search Amazon docs, manually patch config

✖Every broken report affects multiple clients simultaneously — cascading support tickets

✖Manual config updates are error-prone and require deep knowledge of the YAML schema