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** → 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.
- **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 me confidence in end‑to‑end deployment.
##Repo link:
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
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