##Dataguard: A Multiagentic Pipeline for ML

Source: Dev.to

This post is my submission for DEV Education Track: Build Multi‑Agent Systems with ADK. 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.

Architecture

Services

• Dataguard Validator Service
• Dataguard Frontend App

{
  "message": "Validator running successfully"
}

Agents

• 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 → Coordinates the workflow, routes data between agents, and manages error handling.

Together, these agents form Dataguard, a modular, production‑ready pipeline that can be extended with additional agents for new tasks.

Observations

Surprises

How quickly Cloud Run revisions can be deployed and verified — under 30 seconds for a full build‑push‑deploy cycle.

Challenges

IAM role configuration and Artifact Registry permissions required careful troubleshooting. Explicit verification scripts and directory structure were critical for reproducibility.

Takeaway

Schema alignment and modular agent design are essential for reliability. Automated health checks (✅ Service healthy) gave confidence in end‑to‑end deployment.

Repository

https://github.com/NikhilRaman12/Dataguard-ML-Multiagentic-Pipeline.git

Call to Action

Explore the repo, try the live demo, and share your feedback — I’d love to hear how you’d extend Dataguard with new agents or workflows.