##Dataguard: A Multiagentic Pipeline for ML

This post is my submission for DEV Education Track: Build Multi-Agent Systems with ADK . Dataguard: A Multi-Agent System for Reliable ML Pipelines What I Built I built Dataguard , a multi-agent pipeline designed to ensure data reliability and trustworthiness in ML workflows. Dataguard solves the problem of unreliable or inconsistent inputs by embedding specialized agents into a modular FastAPI system. The pipeline validates, reviews, and orchestrates data flow, making it production‑ready, scalable, and resilient to errors. Cloud Run Embed 👉 Dataguard Validator Service 👉 Dataguard Frontend App json {"message":"Validator running successfully"} - **Dataguard Extractor** → Pulls raw data from source archives and prepares it for validation. - **Dataguard Validator** → Enforces schema rules, checks for missing fields, and ensures type safety. - **Dataguard Reviewer** → Applies business rules, flags anomalies, and confirms readiness for downstream tasks. - **Dataguard Orchestrator** → Coordin