A multi-agent AI system that analyzes, validates, and debugs enterprise datasets to ensure production-ready AI pipelines on Azure.
Modern enterprises increasingly rely on data-driven and AI-powered systems, yet data quality issues such as schema inconsistencies, missing values, anomalies, and silent data drift remain one of the biggest risks to production AI reliability.
This project proposes an enterprise-grade, agentic AI platform that uses multiple collaborating AI agents to automatically analyze, debug, and assess the readiness of datasets used in AI and analytics pipelines.
Built on Microsoft’s AI platform, the system leverages the Microsoft Agent Framework to orchestrate specialized agents, including:
- A Schema Intelligence Agent for structure, type, and constraint analysis
- An Anomaly Detection Agent for identifying outliers and distribution irregularities
- A Data Drift Agent to detect changes between historical and current datasets
- A Recommendation Agent that synthesizes findings into actionable insights and remediation suggestions
The platform produces a comprehensive Data Health Report, highlighting risks, quality scores, and production-readiness indicators. The solution is designed to be deployable on Azure, integrating Azure AI services, cloud-native infrastructure, and secure, scalable deployment patterns.
This project demonstrates how agentic AI systems can move beyond single-agent applications to deliver real-world, enterprise-ready AI infrastructure, improving trust, reliability, and governance in AI-driven systems.